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\n  \n 2024\n \n \n (11)\n \n \n
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\n \n\n \n \n \n \n \n \n A Large-Scale Analysis to Optimize the Control and V2V Communication Protocols for CDA Agreement-Seeking Cooperation.\n \n \n \n \n\n\n \n Hyeon, E.; Misra, P.; and Karbowski, D.\n\n\n \n\n\n\n . 2024.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{hyeon_large-scale_2024,\n\ttitle = {A {Large}-{Scale} {Analysis} to {Optimize} the {Control} and {V2V} {Communication} {Protocols} for {CDA} {Agreement}-{Seeking} {Cooperation}},\n\turl = {https://anl.box.com/s/yhov6oot1gjf0gbfu58q97liptphgeqr},\n\tdoi = {10.1109/TCST.2024.3400570},\n\tauthor = {Hyeon, Eunjeong and Misra, Priyash and Karbowski, Dominik},\n\tyear = {2024},\n}\n\n
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\n \n\n \n \n \n \n \n \n Testing Cellular Vehicle-to-Everything Communication Performance and Feasibility in Automated Vehicles.\n \n \n \n \n\n\n \n Liang, Z.; Han, J.; Li, X.; Karbowski, D.; Ma, C.; and Rousseau, A.\n\n\n \n\n\n\n In Presented at the 35th IEEE Intelligent Vehicles Symposium, June 2024. \n \n\n\n\n
\n\n\n\n \n \n \"TestingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liang_testing_2024,\n\ttitle = {Testing {Cellular} {Vehicle}-to-{Everything} {Communication} {Performance} and {Feasibility} in {Automated} {Vehicles}},\n\turl = {https://anl.box.com/s/bqp75fd5oee7g23q2arb3ycunmfwj0re},\n\tbooktitle = {Presented at the 35th {IEEE} {Intelligent} {Vehicles} {Symposium}},\n\tauthor = {Liang, Zhaohui and Han, Jihun and Li, Xiaopeng and Karbowski, Dominik and Ma, Chengyuan and Rousseau, Aymeric},\n\tmonth = jun,\n\tyear = {2024},\n}\n\n
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\n \n\n \n \n \n \n \n \n Validation and Electrification of Medium-sized (69kW) Agricultural Tractor Models.\n \n \n \n \n\n\n \n Kim, N.; Burke, G.; Kim, Y.; Lajunen, A.; and Vijayagopal, R.\n\n\n \n\n\n\n In 37th International Electric Vehicle Symposium and Exhibition (EVS37), April 2024. World Electric Vehicle Association (WEVA)\n \n\n\n\n
\n\n\n\n \n \n \"ValidationPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{kim_validation_2024,\n\ttitle = {Validation and {Electrification} of {Medium}-sized ({69kW}) {Agricultural} {Tractor} {Models}},\n\turl = {https://anl.box.com/s/qmpgqu4dk34r0150gto3ceyppju3x4ql},\n\tabstract = {This study presents a comprehensive exploration of medium-sized agricultural tractor electrification using the Autonomie vehicle simulation software. The research centers on assessing energy consumption and performance parameters during the electrification of powertrains. Initially, a detailed medium-sized agricultural tractor model is developed and validated. Extensive comparisons of energy consumption and component operating areas, based on fieldwork conducted by Chungnam National University, establish a robust foundation for the vehicle model.\nAdditionally, alternative powertrain models, including series hybrid, fuel cell hybrid, and battery electric configurations, are systematically developed. These electrified powertrains aim to attain performance levels comparable to their conventional counterparts. Simulation outcomes reveal the promising potential of fuel cell hybrid and battery electric powertrains in reducing energy consumption and emissions. However, challenges related to operational efficiency and duration persist, indicating avenues for future research and development in the realm of agricultural electrification.},\n\tlanguage = {English},\n\tbooktitle = {37th {International} {Electric} {Vehicle} {Symposium} and {Exhibition} ({EVS37})},\n\tpublisher = {World Electric Vehicle Association (WEVA)},\n\tauthor = {Kim, Namdoo and Burke, George and Kim, Yong-Joo and Lajunen, Antti and Vijayagopal, Ram},\n\tmonth = apr,\n\tyear = {2024},\n\tkeywords = {Autonomie, DOE Anlysis, MD vehicle},\n}\n\n
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\n This study presents a comprehensive exploration of medium-sized agricultural tractor electrification using the Autonomie vehicle simulation software. The research centers on assessing energy consumption and performance parameters during the electrification of powertrains. Initially, a detailed medium-sized agricultural tractor model is developed and validated. Extensive comparisons of energy consumption and component operating areas, based on fieldwork conducted by Chungnam National University, establish a robust foundation for the vehicle model. Additionally, alternative powertrain models, including series hybrid, fuel cell hybrid, and battery electric configurations, are systematically developed. These electrified powertrains aim to attain performance levels comparable to their conventional counterparts. Simulation outcomes reveal the promising potential of fuel cell hybrid and battery electric powertrains in reducing energy consumption and emissions. However, challenges related to operational efficiency and duration persist, indicating avenues for future research and development in the realm of agricultural electrification.\n
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\n \n\n \n \n \n \n \n \n Energy Savings Impact of Eco-Driving Control Based on Powertrain Characteristics in Connected and Automated Vehicles On-Track Demonstrations.\n \n \n \n \n\n\n \n Jeong, J.; Kandaswamy, E.; Dudekula, A. B.; Han, J.; Karbowski, D.; and Naber, J.\n\n\n \n\n\n\n In SAE Technical Paper, Detroit, MI, USA, April 2024. \n ISSN: 0148-7191, 2688-3627\n\n\n\n
\n\n\n\n \n \n \"EnergyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{jeong_energy_2024,\n\taddress = {Detroit, MI, USA},\n\ttitle = {Energy {Savings} {Impact} of {Eco}-{Driving} {Control} {Based} on {Powertrain} {Characteristics} in {Connected} and {Automated} {Vehicles} {On}-{Track} {Demonstrations}},\n\tshorttitle = {Energy {Savings} {Impact} of {Eco}-{Driving} {Control} {Based} on {Powertrain} {Characteristics} in {Connected} and {Automated} {Vehicles}},\n\turl = {https://anl.box.com/s/cshs9q2hzlo56i6c0g81gohf6mkk7qm8},\n\tdoi = {10.4271/2024-01-2606},\n\tabstract = {This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery. The implemented control is a universal speed planner that solves the eco-driving optimal-control problem within a receding-horizon framework, utilizing V2I communications for signal phase and timing information. The controller calculates accelerator and brake pedal positions using the vehicle’s state and real-time environmental information. Both the Pacifica, target vehicle, and the Bolt, EV, are equipped with a drive-by-wire system. The experiments encompass five road scenarios repeated three times, covering a 3.7-km track with various stop signs, traffic signals, and speed limits. Three control calibrations are employed to represent human-driver-like, non-connected automated, and V2I-connected driving. First and foremost, the results demonstrate functional eco-driving controls with no extreme acceleration or traffic law violations in the Pacifica (ICE vehicle). Energy savings of up to 6\\% without connectivity and up to 22\\% with V2I connectivity are achieved in the ICE vehicle as well. Additionally, a comparison is made between an ICE vehicle and an EV to analyze the energy-saving impacts of eco-driving controls across different powertrain characteristics. In conclusion, this study emphasizes the significance of correlating powertrain design with controls and eco-driving strategies during the development of CAVs.},\n\tlanguage = {English},\n\turldate = {2024-04-23},\n\tbooktitle = {{SAE} {Technical} {Paper}},\n\tauthor = {Jeong, Jongryeol and Kandaswamy, Elangovan and Dudekula, Ahammad Basha and Han, Jihun and Karbowski, Dominik and Naber, Jeffrey},\n\tmonth = apr,\n\tyear = {2024},\n\tdoi = {10.4271/2024-01-2606},\n\tnote = {ISSN: 0148-7191, 2688-3627},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control, XIL Testing},\n}\n\n
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\n This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery. The implemented control is a universal speed planner that solves the eco-driving optimal-control problem within a receding-horizon framework, utilizing V2I communications for signal phase and timing information. The controller calculates accelerator and brake pedal positions using the vehicle’s state and real-time environmental information. Both the Pacifica, target vehicle, and the Bolt, EV, are equipped with a drive-by-wire system. The experiments encompass five road scenarios repeated three times, covering a 3.7-km track with various stop signs, traffic signals, and speed limits. Three control calibrations are employed to represent human-driver-like, non-connected automated, and V2I-connected driving. First and foremost, the results demonstrate functional eco-driving controls with no extreme acceleration or traffic law violations in the Pacifica (ICE vehicle). Energy savings of up to 6% without connectivity and up to 22% with V2I connectivity are achieved in the ICE vehicle as well. Additionally, a comparison is made between an ICE vehicle and an EV to analyze the energy-saving impacts of eco-driving controls across different powertrain characteristics. In conclusion, this study emphasizes the significance of correlating powertrain design with controls and eco-driving strategies during the development of CAVs.\n
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\n \n\n \n \n \n \n \n Human Driver Interaction with Eco-Speed Advisory System in Connected Vehicles: Simulation and Experimental Results.\n \n \n \n\n\n \n Han, J.; Ard, T.; Wang, R.; Gupta, P.; Vahidi, A.; Jia, Y.; and Karbowski, D.\n\n\n \n\n\n\n In Presented at the 103rd Transportation Research Board (TRB) Annual Meeting, January 2024. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{han_human_2024,\n\ttitle = {Human {Driver} {Interaction} with {Eco}-{Speed} {Advisory} {System} in {Connected} {Vehicles}: {Simulation} and {Experimental} {Results}},\n\tbooktitle = {Presented at the 103rd {Transportation} {Research} {Board} ({TRB}) {Annual} {Meeting}},\n\tauthor = {Han, Jihun and Ard, Tyler and Wang, Rongyao and Gupta, Prakhar and Vahidi, Ardalan and Jia, Yunyi and Karbowski, Dominik},\n\tmonth = jan,\n\tyear = {2024},\n\tkeywords = {DOE SMART, Ecodriving, RoadRunner},\n}\n\n
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\n \n\n \n \n \n \n \n \n Impact of Advanced Technologies on Energy Consumption of Advanced Electrified Medium-duty Vehicles.\n \n \n \n \n\n\n \n Kim, N.; Islam, E.; and Vijayagopal, R.\n\n\n \n\n\n\n In SAE Technical Paper 2024-01-2453, April 2024. SAE International\n \n\n\n\n
\n\n\n\n \n \n \"ImpactPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{kim_impact_2024,\n\ttitle = {Impact of {Advanced} {Technologies} on {Energy} {Consumption} of {Advanced} {Electrified} {Medium}-duty {Vehicles}},\n\turl = {https://anl.box.com/s/dr1ye4jzxeppi3rin2mt6wcqsp8d4rwh},\n\tdoi = {10.4271/2023-01-0196},\n\tabstract = {The National Highway Traffic Safety Administration (NHTSA) has been leading the responsibilities related to the rulemaking process of Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory (ANL), a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool named Autonomie. Autonomie has become one of the industry standard tools for analyzing vehicle performance, energy consumption and technology effectiveness. Through Inter Agency Agreement (IAA), the DOE Argonne Site Office (ASO), Argonne National Laboratory (ANL), have been tasked in conducting full vehicle simulation to support NHTSA CAFE rulemaking. For CAFE analysis, more than 10 thousand vehicle combinations are simulated across different heavy-duty pickup and vans (HDPUV) classes. This wide range of vehicle combinations consists of different vehicle powertrains combined with various component performances. The purpose of this paper was to study the performance and energy consumption of electrified powertrains simulated for the Notice of Proposed Rulemaking (NPRM) runs that has been published in 2023.},\n\tlanguage = {English},\n\tbooktitle = {{SAE} {Technical} {Paper} 2024-01-2453},\n\tpublisher = {SAE International},\n\tauthor = {Kim, Namdoo and Islam, Ehsan and Vijayagopal, Ram},\n\tmonth = apr,\n\tyear = {2024},\n\tkeywords = {Autonomie, DOE Anlysis, MD vehicle},\n}\n\n
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\n The National Highway Traffic Safety Administration (NHTSA) has been leading the responsibilities related to the rulemaking process of Corporate Average Fuel Economy (CAFE) standards. Argonne National Laboratory (ANL), a U.S. Department of Energy (DOE) national laboratory, has developed a full-vehicle simulation tool named Autonomie. Autonomie has become one of the industry standard tools for analyzing vehicle performance, energy consumption and technology effectiveness. Through Inter Agency Agreement (IAA), the DOE Argonne Site Office (ASO), Argonne National Laboratory (ANL), have been tasked in conducting full vehicle simulation to support NHTSA CAFE rulemaking. For CAFE analysis, more than 10 thousand vehicle combinations are simulated across different heavy-duty pickup and vans (HDPUV) classes. This wide range of vehicle combinations consists of different vehicle powertrains combined with various component performances. The purpose of this paper was to study the performance and energy consumption of electrified powertrains simulated for the Notice of Proposed Rulemaking (NPRM) runs that has been published in 2023.\n
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\n \n\n \n \n \n \n \n \n Component Sizing Optimization Based on Technological Assumptions for Medium-duty Electric Vehicles.\n \n \n \n \n\n\n \n Park, D.; Jung, J.; Kim, N.; Islam, E.; Kim, N.; and Vijayagopal, R.\n\n\n \n\n\n\n In SAE Technical Paper 2024-01-2450, April 2024. SAE International\n \n\n\n\n
\n\n\n\n \n \n \"ComponentPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{park_component_2024,\n\ttitle = {Component {Sizing} {Optimization} {Based} on {Technological} {Assumptions} for {Medium}-duty {Electric} {Vehicles}},\n\turl = {https://anl.box.com/s/po64nu82w3dxaj3fgo3aq0ctjg4wpmu6},\n\tdoi = {10.4271/2023-01-0196},\n\tabstract = {In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements. We used Autonomie, a high-fidelity vehicle modeling and simulation tool developed by Argonne National Laboratory, integrating Pattern Search solvers to optimize component sizing based on our assumptions. Pattern Search, a direct-method numerical optimization algorithm, is widely used in a variety of applications. The method requires extensive evaluation and iteration but provides good optimization performance for the computational cost. This paper presents the potential energy savings for a medium-duty electric vehicle determined using both rule-based and optimized component sizing.},\n\tlanguage = {English},\n\tbooktitle = {{SAE} {Technical} {Paper} 2024-01-2450},\n\tpublisher = {SAE International},\n\tauthor = {Park, Dohyun and Jung, Jaekwang and Kim, Namwook and Islam, Ehsan and Kim, Namdoo and Vijayagopal, Ram},\n\tmonth = apr,\n\tyear = {2024},\n\tkeywords = {Autonomie, DOE Anlysis, MD vehicle},\n}\n\n
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\n In response to the stipulations of the Energy Policy and Conservation Act and the global momentum toward carbon mitigation, there has been a pronounced tightening of fuel economy standards for manufacturers. This stricter regulation is coupled with an accelerated transition to electric vehicles, catalyzed by advances in electrification technology and a decline in battery cost. Improvements in the fuel economy of medium- and heavy-duty vehicles through electrification are particularly noteworthy. Estimating the magnitude of fuel economy improvements that result from technological advances in these vehicles is key to effective policymaking. In this research, we generated vehicle models based on assumptions regarding advanced transportation component technologies and powertrains to estimate potential vehicle-level fuel savings. We also developed a systematic approach to evaluating a vehicle’s fuel economy by calibrating the size of the components to satisfy performance requirements. We used Autonomie, a high-fidelity vehicle modeling and simulation tool developed by Argonne National Laboratory, integrating Pattern Search solvers to optimize component sizing based on our assumptions. Pattern Search, a direct-method numerical optimization algorithm, is widely used in a variety of applications. The method requires extensive evaluation and iteration but provides good optimization performance for the computational cost. This paper presents the potential energy savings for a medium-duty electric vehicle determined using both rule-based and optimized component sizing.\n
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\n \n\n \n \n \n \n \n \n Simulation-Based Assessment of Energy Consumption of Alternative Powertrains in Agricultural Tractors.\n \n \n \n \n\n\n \n Lajunen, A.; Kivekas, K.; Freyermuth, V.; Vijayagopal, R.; and Kim, N.\n\n\n \n\n\n\n World Electric Vehicle Journal. 2024.\n \n\n\n\n
\n\n\n\n \n \n \"Simulation-BasedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@article{lajunen_simulation-based_2024,\n\ttitle = {Simulation-{Based} {Assessment} of {Energy} {Consumption} of {Alternative} {Powertrains} in {Agricultural} {Tractors}},\n\turl = {https://anl.box.com/s/2mxxia0o4zz42314m9zt0hj6mn80ixdn},\n\tdoi = {https://doi.org/10.3390/wevj15030086},\n\tabstract = {The objectives of this research were to develop simulation models for agricultural tractors with different powertrain technologies and evaluate the energy consumption in typical agricultural operations. Simulation models were developed for conventional, parallel hybrid electric, series hybrid electric, fuel cell hybrid, and battery electric powertrains. Autonomie vehicle simulation software (version 2022) was used for the simulations and the tractor models were simulated in two tilling cycles and in a road transport cycle with a trailer. The alternative powertrains were configured to have at least the same tractive performance as the conventional, diesel engine-powered tractor model. The simulation results showed that the potential of the parallel and series hybrid powertrains to improve energy efficiency depends heavily on the tractor size and the operating cycle conditions. The fuel cell hybrid and battery electric powertrains have a higher potential to reduce energy consumption and emissions but still have inherent technical challenges for practical operation. The battery-powered electric tractor would require improvements in the storage energy density to have a comparable operational performance in comparison to other powertrains. The fuel cell hybrid tractor already provided an adequate operating performance but the availability of hydrogen and refueling infrastructure could be challenging to resolve in the farming context.},\n\tjournal = {World Electric Vehicle Journal},\n\tauthor = {Lajunen, Antti and Kivekas, Klaus and Freyermuth, Vincent and Vijayagopal, Ram and Kim, Namdoo},\n\tyear = {2024},\n\tkeywords = {Autonomie, Vehicle control, Vehicle systems},\n}\n\n
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\n The objectives of this research were to develop simulation models for agricultural tractors with different powertrain technologies and evaluate the energy consumption in typical agricultural operations. Simulation models were developed for conventional, parallel hybrid electric, series hybrid electric, fuel cell hybrid, and battery electric powertrains. Autonomie vehicle simulation software (version 2022) was used for the simulations and the tractor models were simulated in two tilling cycles and in a road transport cycle with a trailer. The alternative powertrains were configured to have at least the same tractive performance as the conventional, diesel engine-powered tractor model. The simulation results showed that the potential of the parallel and series hybrid powertrains to improve energy efficiency depends heavily on the tractor size and the operating cycle conditions. The fuel cell hybrid and battery electric powertrains have a higher potential to reduce energy consumption and emissions but still have inherent technical challenges for practical operation. The battery-powered electric tractor would require improvements in the storage energy density to have a comparable operational performance in comparison to other powertrains. The fuel cell hybrid tractor already provided an adequate operating performance but the availability of hydrogen and refueling infrastructure could be challenging to resolve in the farming context.\n
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\n \n\n \n \n \n \n \n \n Modeling of hybrid-electric powertrains for small commuter aircraft to assess performance and emissions.\n \n \n \n \n\n\n \n Salucci, F.; Prabhakar, N.; and Karbowski, D. A.\n\n\n \n\n\n\n In AIAA SCITECH 2024 Forum, of AIAA SciTech Forum. American Institute of Aeronautics and Astronautics, January 2024.\n \n\n\n\n
\n\n\n\n \n \n \"ModelingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@incollection{salucci_modeling_2024,\n\tseries = {{AIAA} {SciTech} {Forum}},\n\ttitle = {Modeling of hybrid-electric powertrains for small commuter aircraft to assess performance and emissions},\n\turl = {https://anl.box.com/s/o1sng8k450is6dp03g4n4n171qws2l6z},\n\turldate = {2024-02-23},\n\tbooktitle = {{AIAA} {SCITECH} 2024 {Forum}},\n\tpublisher = {American Institute of Aeronautics and Astronautics},\n\tauthor = {Salucci, Francesco and Prabhakar, Nirmit and Karbowski, Dominik A.},\n\tmonth = jan,\n\tyear = {2024},\n\tdoi = {10.2514/6.2024-0280},\n}\n\n
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\n \n\n \n \n \n \n \n \n Equiticity.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n 2024.\n \n\n\n\n
\n\n\n\n \n \n \"EquiticityPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{noauthor_equiticity_2024,\n\ttitle = {Equiticity},\n\turl = {https://www.equiticity.org},\n\tabstract = {Equiticity is a 501(c)(3) non-profit organization programming and advocating for racial equity and increased mobility in Chicago and beyond.},\n\tlanguage = {en-US},\n\turldate = {2024-02-23},\n\tjournal = {Equiticity},\n\tyear = {2024},\n}\n\n
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\n Equiticity is a 501(c)(3) non-profit organization programming and advocating for racial equity and increased mobility in Chicago and beyond.\n
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\n \n\n \n \n \n \n \n Integrating Human Factors in Dynamic Rideshare Assignment: Willingness-To-Pay for Delay.\n \n \n \n\n\n \n Paul, J.; Gurumurthy, K. M.; Cokyasar, T.; Su, H.; Auld, J.; and Jia, Y.\n\n\n \n\n\n\n In Submitted for presentation at the 103rd Transportation Research Board (TRB) Annual Meeting, January 2024. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{paul_integrating_2024,\n\ttitle = {Integrating {Human} {Factors} in {Dynamic} {Rideshare} {Assignment}: {Willingness}-{To}-{Pay} for {Delay}},\n\tbooktitle = {Submitted for presentation at the 103rd {Transportation} {Research} {Board} ({TRB}) {Annual} {Meeting}},\n\tauthor = {Paul, Joseph and Gurumurthy, Krishna Murthy and Cokyasar, Taner and Su, Haotian and Auld, Joshua and Jia, Yunyi},\n\tmonth = jan,\n\tyear = {2024},\n}\n\n
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\n  \n 2023\n \n \n (39)\n \n \n
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\n \n\n \n \n \n \n \n \n Why it's so hard to build new electrical transmission lines in the U.S.\n \n \n \n \n\n\n \n Clifford, C.\n\n\n \n\n\n\n February 2023.\n Section: Climate Policy\n\n\n\n
\n\n\n\n \n \n \"WhyPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{clifford_why_2023,\n\ttitle = {Why it's so hard to build new electrical transmission lines in the {U}.{S}.},\n\turl = {https://www.cnbc.com/2023/02/21/why-its-so-hard-to-build-new-electrical-transmission-lines-in-the-us.html},\n\tabstract = {Building transmission lines in the U.S. is a slow process with many stakeholders, and the delays are holding back the country's clean energy transition.},\n\tlanguage = {en},\n\turldate = {2024-07-15},\n\tjournal = {CNBC},\n\tauthor = {Clifford, Catherine},\n\tmonth = feb,\n\tyear = {2023},\n\tnote = {Section: Climate Policy},\n}\n\n
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\n Building transmission lines in the U.S. is a slow process with many stakeholders, and the delays are holding back the country's clean energy transition.\n
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\n \n\n \n \n \n \n \n National Transmission Needs Study.\n \n \n \n\n\n \n \n\n\n \n\n\n\n . 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{noauthor_national_2023,\n\ttitle = {National {Transmission} {Needs} {Study}},\n\tlanguage = {en},\n\tyear = {2023},\n}\n\n
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\n \n\n \n \n \n \n \n \n A freight asset choice model for agent-based simulation models.\n \n \n \n \n\n\n \n Zuniga-Garcia, N.; Ismael, A.; and Stinson, M.\n\n\n \n\n\n\n The 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) and The 6th International Conference on Emerging Data and Industry 4.0 (EDI40), 220: 704–709. January 2023.\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{zuniga-garcia_freight_2023,\n\ttitle = {A freight asset choice model for agent-based simulation models},\n\tvolume = {220},\n\tissn = {1877-0509},\n\turl = {https://www.sciencedirect.com/science/article/pii/S1877050923006270},\n\tdoi = {10.1016/j.procs.2023.03.092},\n\tabstract = {Agent-based models (ABM) for transportation operations have been largely focused on passenger trips, while recent developments in the field began the incorporation of freight-related operations. However, these models rarely incorporate freight-related strategic asset decisions: fleet and distribution center (DC) ownership. These attributes are important for modeling freight transportation behavior in ABMs. This research develops behavioral models that jointly predict fleet ownership and distribution center control for freight-related firms in an ABM framework. A seemingly unrelated Tobit regression is estimated using large-scale data from more than 11 million establishments. The model is estimated using a Bayesian approach that allows for the quantification of the coefficients’ variability. Model results indicate that firms with higher revenue have an increased propensity to own fleet and (or) DC and prefer larger fleets and more DC space. Furthermore, Transportation firms generally have more heavy-duty trucks and much fewer medium-duty trucks, while firms in all other sectors strongly prefer medium-duty fleets. Food Services and other Retail firms have the greatest preference for owning or leasing their own DCs, followed by Manufacturing, Wholesale, and Transportation firms. A case study is developed for the city of Chicago using a high-performance ABM framework designed for simulating large-scale transportation systems. Transportation modelers and policymakers can use findings and methods from this research to study freight operations in large cities.},\n\tjournal = {The 14th International Conference on Ambient Systems, Networks and Technologies Networks (ANT) and The 6th International Conference on Emerging Data and Industry 4.0 (EDI40)},\n\tauthor = {Zuniga-Garcia, Natalia and Ismael, Abdelrahman and Stinson, Monique},\n\tmonth = jan,\n\tyear = {2023},\n\tkeywords = {agent-based simulation, depots, distribution centers, freight, truck fleet},\n\tpages = {704--709},\n}\n\n
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\n Agent-based models (ABM) for transportation operations have been largely focused on passenger trips, while recent developments in the field began the incorporation of freight-related operations. However, these models rarely incorporate freight-related strategic asset decisions: fleet and distribution center (DC) ownership. These attributes are important for modeling freight transportation behavior in ABMs. This research develops behavioral models that jointly predict fleet ownership and distribution center control for freight-related firms in an ABM framework. A seemingly unrelated Tobit regression is estimated using large-scale data from more than 11 million establishments. The model is estimated using a Bayesian approach that allows for the quantification of the coefficients’ variability. Model results indicate that firms with higher revenue have an increased propensity to own fleet and (or) DC and prefer larger fleets and more DC space. Furthermore, Transportation firms generally have more heavy-duty trucks and much fewer medium-duty trucks, while firms in all other sectors strongly prefer medium-duty fleets. Food Services and other Retail firms have the greatest preference for owning or leasing their own DCs, followed by Manufacturing, Wholesale, and Transportation firms. A case study is developed for the city of Chicago using a high-performance ABM framework designed for simulating large-scale transportation systems. Transportation modelers and policymakers can use findings and methods from this research to study freight operations in large cities.\n
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\n \n\n \n \n \n \n \n \n Estimating Mission-Based Energy and System Dynamics of e-VTOL Aircraft.\n \n \n \n \n\n\n \n Prabhakar, N.; Salucci, F.; and Karbowski, D.\n\n\n \n\n\n\n In San Diego, June 2023. AIAA\n \n\n\n\n
\n\n\n\n \n \n \"EstimatingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n\n\n\n
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@inproceedings{prabhakar_estimating_2023,\n\taddress = {San Diego},\n\ttitle = {Estimating {Mission}-{Based} {Energy} and {System} {Dynamics} of e-{VTOL} {Aircraft}},\n\turl = {https://anl.app.box.com/s/f0yoegseuxt0yxmgxoy1w3mtdxbozogn},\n\tpublisher = {AIAA},\n\tauthor = {Prabhakar, Nirmit and Salucci, Francesco and Karbowski, Dominik},\n\tmonth = jun,\n\tyear = {2023},\n\tkeywords = {Aeronomie, Aircraft Systems},\n}\n\n
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\n \n\n \n \n \n \n \n \n Potential Energy Saving by Different Cooperative Driving Automation Classes in Car-Following Scenarios $^{\\textrm{*}}$.\n \n \n \n \n\n\n \n Hyeon, E.; Karbowski, D.; and Rousseau, A.\n\n\n \n\n\n\n In 2023 American Control Conference (ACC), pages 1313–1318, San Diego, CA, USA, May 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"PotentialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{hyeon_potential_2023,\n\taddress = {San Diego, CA, USA},\n\ttitle = {Potential {Energy} {Saving} by {Different} {Cooperative} {Driving} {Automation} {Classes} in {Car}-{Following} {Scenarios} $^{\\textrm{*}}$},\n\tisbn = {9798350328066},\n\turl = {https://anl.box.com/s/og67f5zlawlbg7q4at6zgk12h4xmucom},\n\tdoi = {10.23919/ACC55779.2023.10156430},\n\turldate = {2023-11-01},\n\tbooktitle = {2023 {American} {Control} {Conference} ({ACC})},\n\tpublisher = {IEEE},\n\tauthor = {Hyeon, Eunjeong and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = may,\n\tyear = {2023},\n\tpages = {1313--1318},\n}\n\n
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\n \n\n \n \n \n \n \n \n Decision-Making Strategy Using Multi-Agent Reinforcement Learning for Platoon Formation in Agreement-Seeking Cooperation.\n \n \n \n \n\n\n \n Hyeon, E.; Karbowski, D.; and Rousseau, A.\n\n\n \n\n\n\n In 2023 IEEE Intelligent Vehicles Symposium (IV), pages 1–6, Anchorage, AK, USA, June 2023. IEEE\n \n\n\n\n
\n\n\n\n \n \n \"Decision-MakingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{hyeon_decision-making_2023,\n\taddress = {Anchorage, AK, USA},\n\ttitle = {Decision-{Making} {Strategy} {Using} {Multi}-{Agent} {Reinforcement} {Learning} for {Platoon} {Formation} in {Agreement}-{Seeking} {Cooperation}},\n\tisbn = {9798350346916},\n\turl = {https://anl.box.com/s/39xmwpvw2qe4x8q80n7rt3b32dyv78c6},\n\tdoi = {10.1109/IV55152.2023.10186813},\n\turldate = {2023-11-01},\n\tbooktitle = {2023 {IEEE} {Intelligent} {Vehicles} {Symposium} ({IV})},\n\tpublisher = {IEEE},\n\tauthor = {Hyeon, Eunjeong and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = jun,\n\tyear = {2023},\n\tpages = {1--6},\n}\n\n
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\n \n\n \n \n \n \n \n \n Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery.\n \n \n \n \n\n\n \n Cokyasar, T.; Subramanyam, A.; Larson, J.; Stinson, M.; and Sahin, O.\n\n\n \n\n\n\n Transportation Research Record, 2677(2): 190–203. February 2023.\n Publisher: SAGE Publications Inc\n\n\n\n
\n\n\n\n \n \n \"Time-ConstrainedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{cokyasar_time-constrained_2023,\n\ttitle = {Time-{Constrained} {Capacitated} {Vehicle} {Routing} {Problem} in {Urban} {E}-{Commerce} {Delivery}},\n\tvolume = {2677},\n\tissn = {0361-1981},\n\turl = {https://doi.org/10.1177/03611981221124592},\n\tdoi = {10.1177/03611981221124592},\n\tabstract = {Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications, such as e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and the maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest for e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities—Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI—and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics, including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mi BEV range as a baseline without mid-route recharging. Our results show that the BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 mi. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8\\% in Austin if service providers were to operate their distribution centers jointly.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2024-02-01},\n\tjournal = {Transportation Research Record},\n\tauthor = {Cokyasar, Taner and Subramanyam, Anirudh and Larson, Jeffrey and Stinson, Monique and Sahin, Olcay},\n\tmonth = feb,\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Inc},\n\tpages = {190--203},\n}\n\n
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\n Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications, such as e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and the maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest for e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities—Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI—and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics, including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mi BEV range as a baseline without mid-route recharging. Our results show that the BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 mi. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8% in Austin if service providers were to operate their distribution centers jointly.\n
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\n \n\n \n \n \n \n \n \n E-Commerce Retail Sales as a Percent of Total Sales.\n \n \n \n \n\n\n \n U.S. Census Bureau\n\n\n \n\n\n\n October 2023.\n Publisher: FRED, Federal Reserve Bank of St. Louis\n\n\n\n
\n\n\n\n \n \n \"E-CommercePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{us_census_bureau_e-commerce_2023,\n\ttitle = {E-{Commerce} {Retail} {Sales} as a {Percent} of {Total} {Sales}},\n\tshorttitle = {{ECOMPCTSA}},\n\turl = {https://fred.stlouisfed.org/series/ECOMPCTSA},\n\tabstract = {E-commerce sales are sales of goods and services where  the buyer places an order, or the price and terms  of the sale  are negotiated over an Internet, mobile device  (M-commerce),  extranet,  Electronic  Data  Interchange  (EDI)  network,  electronic  mail,  or  other  comparable  online  system.  Payment  may  or  may  not  be  made online.},\n\turldate = {2024-01-31},\n\tjournal = {FRED, Federal Reserve Bank of St. Louis},\n\tauthor = {{U.S. Census Bureau}},\n\tmonth = oct,\n\tyear = {2023},\n\tnote = {Publisher: FRED, Federal Reserve Bank of St. Louis},\n}\n\n
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\n E-commerce sales are sales of goods and services where the buyer places an order, or the price and terms of the sale are negotiated over an Internet, mobile device (M-commerce), extranet, Electronic Data Interchange (EDI) network, electronic mail, or other comparable online system. Payment may or may not be made online.\n
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\n \n\n \n \n \n \n \n SMART Mobility 2.0 : POLARIS-Specific Scenario Results for Three Regions.\n \n \n \n\n\n \n Auld, J.; Verbas, Ö.; Gurumurthy, K. M.; de Souza, F.; Zuniga-Garcia, N.; Cokyasar, T.; Sahin, O.; Khan, N. A.; Mansour, C.; Cook, J.; Huang, Y.; Ismael, A.; Hui, S.; Magassy, T.; White, G.; and Rousseau, A.\n\n\n \n\n\n\n Technical Report Argonne National Laboratory (ANL), June 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@techreport{auld_smart_2023,\n\ttitle = {{SMART} {Mobility} 2.0 : {POLARIS}-{Specific} {Scenario} {Results} for {Three} {Regions}},\n\tinstitution = {Argonne National Laboratory (ANL)},\n\tauthor = {Auld, Joshua and Verbas, Ömer and Gurumurthy, Krishna M. and de Souza, Felipe and Zuniga-Garcia, Natalia and Cokyasar, Taner and Sahin, Olcay and Khan, Nazmul Arefin and Mansour, Charbel and Cook, James and Huang, Yantao and Ismael, Abdelrahman and Hui, Shen and Magassy, Tassio and White, Griffin and Rousseau, Aymeric},\n\tmonth = jun,\n\tyear = {2023},\n}\n\n
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\n \n\n \n \n \n \n \n \n An optimization model for solving the route clustering problem.\n \n \n \n \n\n\n \n Cokyasar, T.; Davatgari, A.; and Mohammadian, A. K.\n\n\n \n\n\n\n In The 14th international conference on ambient systems, networks and technologies (ANT), volume 220, pages 180–186, 2023. Procedia Computer Science\n \n\n\n\n
\n\n\n\n \n \n \"AnPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{cokyasar_optimization_2023,\n\ttitle = {An optimization model for solving the route clustering problem},\n\tvolume = {220},\n\turl = {https://doi.org/10.1016/j.procs.2023.03.025},\n\tdoi = {https://doi.org/10.1016/j.procs.2023.03.025},\n\tbooktitle = {The 14th international conference on ambient systems, networks and technologies ({ANT})},\n\tpublisher = {Procedia Computer Science},\n\tauthor = {Cokyasar, Taner and Davatgari, Amir and Mohammadian, Abolfazl Kouros},\n\tyear = {2023},\n\tpages = {180--186},\n}\n\n
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\n \n\n \n \n \n \n \n \n Time-constrained capacitated vehicle routing problem in urban e-commerce delivery.\n \n \n \n \n\n\n \n Cokyasar, T.; Subramanyam, A.; Larson, J.; Stinson, M.; and Sahin, O.\n\n\n \n\n\n\n Transportation Research Record, 2677(2): 190–203. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"Time-constrainedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 10 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{cokyasar_time-constrained_2023-1,\n\ttitle = {Time-constrained capacitated vehicle routing problem in urban e-commerce delivery},\n\tvolume = {2677},\n\turl = {https://journals.sagepub.com/doi/full/10.1177/03611981221124592},\n\tdoi = {https://doi.org/10.1177/03611981221124592},\n\tnumber = {2},\n\tjournal = {Transportation Research Record},\n\tauthor = {Cokyasar, Taner and Subramanyam, Anirudh and Larson, Jeffrey and Stinson, Monique and Sahin, Olcay},\n\tyear = {2023},\n\tpages = {190--203},\n}\n\n
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\n \n\n \n \n \n \n \n \n Comparing regional energy consumption for direct drone and truck deliveries.\n \n \n \n \n\n\n \n Cokyasar, T.; Stinson, M.; Sahin, O.; Prabhakar, N.; and Karbowski, D.\n\n\n \n\n\n\n Transportation Research Record, 2677(2): 310–327. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"ComparingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 5 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{cokyasar_comparing_2023,\n\ttitle = {Comparing regional energy consumption for direct drone and truck deliveries},\n\tvolume = {2677},\n\turl = {https://journals.sagepub.com/doi/full/10.1177/03611981221145137},\n\tdoi = {https://doi.org/10.1177/03611981221145137},\n\tnumber = {2},\n\tjournal = {Transportation Research Record},\n\tauthor = {Cokyasar, Taner and Stinson, Monique and Sahin, Olcay and Prabhakar, Nirmit and Karbowski, Dominik},\n\tyear = {2023},\n\tpages = {310--327},\n}\n\n
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\n \n\n \n \n \n \n \n \n An X-in-the-Loop (XIL) Testing Framework for Validation of Connected and Autonomous Vehicles.\n \n \n \n \n\n\n \n Gupta, P.; Wang, R.; Ard, T.; Han, J.; Karbowski, D.; Vahidi, A.; and Jia, Y.\n\n\n \n\n\n\n In IEEE International Automated Vehicle Validation Conference 2023, Austin, TX, USA, October 2023. \n \n\n\n\n
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@inproceedings{gupta_x---loop_2023,\n\taddress = {Austin, TX, USA},\n\ttitle = {An {X}-in-the-{Loop} ({XIL}) {Testing} {Framework} for {Validation} of {Connected} and {Autonomous} {Vehicles}},\n\turl = {https://anl.box.com/s/f97c1x90ycgxj66xddgzyjyjt0pix34m},\n\tbooktitle = {{IEEE} {International} {Automated} {Vehicle} {Validation} {Conference} 2023},\n\tauthor = {Gupta, Prakhar and Wang, Rongyao and Ard, Tyler and Han, Jihun and Karbowski, Dominik and Vahidi, Ardalan and Jia, Yunyi},\n\tmonth = oct,\n\tyear = {2023},\n\tkeywords = {CAV, Connected and Automated Vehicles, DOE SMART, Vehicle control, XIL Testing},\n}\n\n
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\n \n\n \n \n \n \n \n Deploying Fast Charging Infrastructure for Electric Vehicles in Urban Networks: An Activity-Based Approach.\n \n \n \n\n\n \n Kavianipour, M.; Verbas, O.; Rostami, A.; Soltanpour, A.; Gurumurthy, K. M.; Ghamami, M.; and Zockaie, A.\n\n\n \n\n\n\n Transportation Research Record. 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{kavianipour_deploying_2023,\n\ttitle = {Deploying {Fast} {Charging} {Infrastructure} for {Electric} {Vehicles} in {Urban} {Networks}: {An} {Activity}-{Based} {Approach}},\n\tabstract = {This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users’ daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charges (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. Then, the problem of finding the optimum charging configuration is formulated as a Mixed-Integer Nonlinear Programming that considers travel time and travel distance dynamics, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and the detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.},\n\tjournal = {Transportation Research Record},\n\tauthor = {Kavianipour, Mohammadreza and Verbas, Omer and Rostami, Alireza and Soltanpour, Amirali and Gurumurthy, Krishna Murthy and Ghamami, Mehrnaz and Zockaie, Ali},\n\tyear = {2023},\n}\n\n
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\n This paper explores an important problem under the domain of network modeling, the optimal configuration of charging infrastructure for electric vehicles (EVs) in urban networks considering EV users’ daily activities and charging behavior. This study proposes a charging behavior simulation model considering different initial state of charges (SOC), travel distance, availability of home chargers, and the daily schedule of trips for each traveler. The proposed charging behavior simulation model examines the complete chain of trips for EV users as well as the interdependency of trips traveled by each driver. Then, the problem of finding the optimum charging configuration is formulated as a Mixed-Integer Nonlinear Programming that considers travel time and travel distance dynamics, the interdependency of trips made by each driver, limited range of EVs, remaining battery capacity for recharging, waiting time in queue, and the detour to access a charging station. This problem is solved using a metaheuristic approach for a large-scale case network. A series of examples are presented to demonstrate the model efficacy and explore the impact of energy consumption on the final SOC and the optimum charging infrastructure.\n
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\n \n\n \n \n \n \n \n Exploring Impacts of Electricity Tariff And Power Grid Constraints On Charging Behavior And Fast Charging Infrastructure Deployment In Urban Networks: An Activity-Based Approach.\n \n \n \n\n\n \n Rostami, A.; Verbas, O.; Soltanpour, A.; Ghafarnezhad, B.; Ghamami, M.; and Zockaie, A.\n\n\n \n\n\n\n In Washington, D.C., August 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{rostami_exploring_2023,\n\taddress = {Washington, D.C.},\n\ttitle = {Exploring {Impacts} of {Electricity} {Tariff} {And} {Power} {Grid} {Constraints} {On} {Charging} {Behavior} {And} {Fast} {Charging} {Infrastructure} {Deployment} {In} {Urban} {Networks}: {An} {Activity}-{Based} {Approach}},\n\tabstract = {In the past decade, electric vehicles (EVs) have experienced a significant surge in popularity as a highly efficient and environmentally friendly mode of transportation. However, EV users struggle with range anxiety and inadequate charging infrastructure. Recent advancements in battery technology and charging equipment have resulted in long-range EVs and fast-charging technology. Despite these advancements, many major cities continue to face a lack of sufficient charging infrastructure to cater to the daily urban trips of EV users. The interaction between the electricity grid demand and supply availability plays an important role to identify the appropriate locations for deployment of such infrastructure. However, most of developed models in the literature neglect this important factor. Thus, this study incorporates the integration of activity based modeling, charging behavior simulation, and charging infrastructure optimization model. The study employs POLARIS, a cutting-edge agent-based transportation model, to realistically capture user activities, trip chains, and resulting traffic patterns within a network. Additionally, a charging behavior simulation is incorporated to accurately estimate the charging demand of EVs based on their trajectories, battery performance, daily activities, and access to home charging. Next, a mathematical optimization model is reformulated to capture impacts of spatial and temporal distribution of electricity rates on the optimal deployment of charging infrastructure. Finally, the proposed framework incorporates the optimal charging station locations and number of chargers into the POLARIS platform to monitor users’ response. The framework is applied to the Chicago regional area network and rigorously tested and analyzed under various EV ownership and charging pricing scenarios.},\n\tauthor = {Rostami, Alireza and Verbas, Omer and Soltanpour, Amirali and Ghafarnezhad, Behdad and Ghamami, Mehrnaz and Zockaie, Ali},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n
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\n In the past decade, electric vehicles (EVs) have experienced a significant surge in popularity as a highly efficient and environmentally friendly mode of transportation. However, EV users struggle with range anxiety and inadequate charging infrastructure. Recent advancements in battery technology and charging equipment have resulted in long-range EVs and fast-charging technology. Despite these advancements, many major cities continue to face a lack of sufficient charging infrastructure to cater to the daily urban trips of EV users. The interaction between the electricity grid demand and supply availability plays an important role to identify the appropriate locations for deployment of such infrastructure. However, most of developed models in the literature neglect this important factor. Thus, this study incorporates the integration of activity based modeling, charging behavior simulation, and charging infrastructure optimization model. The study employs POLARIS, a cutting-edge agent-based transportation model, to realistically capture user activities, trip chains, and resulting traffic patterns within a network. Additionally, a charging behavior simulation is incorporated to accurately estimate the charging demand of EVs based on their trajectories, battery performance, daily activities, and access to home charging. Next, a mathematical optimization model is reformulated to capture impacts of spatial and temporal distribution of electricity rates on the optimal deployment of charging infrastructure. Finally, the proposed framework incorporates the optimal charging station locations and number of chargers into the POLARIS platform to monitor users’ response. The framework is applied to the Chicago regional area network and rigorously tested and analyzed under various EV ownership and charging pricing scenarios.\n
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\n \n\n \n \n \n \n \n Solving the Electric Vehicle Scheduling Problem at Large-Scale.\n \n \n \n\n\n \n Cokyasar, T.; Verbas, O.; Davatgari, A.; and Mohammadian, A. (.\n\n\n \n\n\n\n In Bilbao, Spain, October 2023. \n \n\n\n\n
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@inproceedings{cokyasar_solving_2023,\n\taddress = {Bilbao, Spain},\n\ttitle = {Solving the {Electric} {Vehicle} {Scheduling} {Problem} at {Large}-{Scale}},\n\tabstract = {Transit is the backbone of the transportation sector that not only reduces traffic congestion and environmental impact but also provides equity as an affordable mobility service. Bus electrification has an utmost importance to achieve global net-zero economy goals since buses are the main source of emissions in the transit sector. In this study, we revisit the single depot vehicle scheduling problem (SDVSP) to address the operational constraints electrification brings in. To this end, we follow a two-stage modeling approach: We solve an SDVSP model to form vehicle blocks in the first stage, and chain these blocks considering spatio-temporal and state-of-charge conditions in the second stage. A greedy algorithm heuristic is developed to address the complexity of the block chaining problem. Since transit agencies readily use SDVSP models to form blocks, incorporating a straightforward block chaining heuristic makes the solution approach easy to implement. An analysis conducted using this solution framework found that the electric vehicle range plays a key role in determining the fleet size. A 150-mile vehicle range allows for 98\\% electrification where each diesel vehicle is replaced by 1.6 electric vehicles. This also means a 25\\% increase in non-revenue time.},\n\tauthor = {Cokyasar, Taner and Verbas, Omer and Davatgari, Amir and Mohammadian, A. (Kouros)},\n\tmonth = oct,\n\tyear = {2023},\n}\n\n
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\n Transit is the backbone of the transportation sector that not only reduces traffic congestion and environmental impact but also provides equity as an affordable mobility service. Bus electrification has an utmost importance to achieve global net-zero economy goals since buses are the main source of emissions in the transit sector. In this study, we revisit the single depot vehicle scheduling problem (SDVSP) to address the operational constraints electrification brings in. To this end, we follow a two-stage modeling approach: We solve an SDVSP model to form vehicle blocks in the first stage, and chain these blocks considering spatio-temporal and state-of-charge conditions in the second stage. A greedy algorithm heuristic is developed to address the complexity of the block chaining problem. Since transit agencies readily use SDVSP models to form blocks, incorporating a straightforward block chaining heuristic makes the solution approach easy to implement. An analysis conducted using this solution framework found that the electric vehicle range plays a key role in determining the fleet size. A 150-mile vehicle range allows for 98% electrification where each diesel vehicle is replaced by 1.6 electric vehicles. This also means a 25% increase in non-revenue time.\n
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\n \n\n \n \n \n \n \n \n Impact of Vehicle Automation on Energy Consumption.\n \n \n \n \n\n\n \n Han, J.; Karbowski, D.; Jeong, J.; Kim, N.; Grave, J.; Shen, D.; Zhang, Y.; and Rousseau, A.\n\n\n \n\n\n\n In Road Vehicle Automation 9, pages 53–70. Springer, Cham, 2023.\n \n\n\n\n
\n\n\n\n \n \n \"ImpactPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@incollection{han_impact_2023,\n\ttitle = {Impact of {Vehicle} {Automation} on {Energy} {Consumption}},\n\turl = {https://anl.box.com/s/xphlw17wcn67tskl0sg7eh7iz7i3ef9q},\n\tbooktitle = {Road {Vehicle} {Automation} 9},\n\tpublisher = {Springer, Cham},\n\tauthor = {Han, Jihun and Karbowski, Dominik and Jeong, Jongryeol and Kim, Namdoo and Grave, Julien and Shen, Daliang and Zhang, Yaozhong and Rousseau, Aymeric},\n\tyear = {2023},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n\tpages = {53--70},\n}\n\n
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\n \n\n \n \n \n \n \n \n Energy-Efficient Driving in Connected Corridors Via Minimum Principle Control: Vehicle-in-The-Loop Experimental Verification in Mixed Fleets.\n \n \n \n \n\n\n \n Ard, T.; Guo, L.; Han, J.; Jia, Y.; Vahidi, A.; and Karbowski, D.\n\n\n \n\n\n\n IEEE Transactions on Intelligent Vehicles, 8(2): 1279 – 1291. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"Energy-EfficientPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{ard_energy-efficient_2023,\n\ttitle = {Energy-{Efficient} {Driving} in {Connected} {Corridors} {Via} {Minimum} {Principle} {Control}: {Vehicle}-in-{The}-{Loop} {Experimental} {Verification} in {Mixed} {Fleets}},\n\tvolume = {8},\n\turl = {https://anl.box.com/s/sk9c669t0190x3rx70v09fwp2zwl8p1g},\n\tdoi = {10.1109/TIV.2023.3234261},\n\tnumber = {2},\n\tjournal = {IEEE Transactions on Intelligent Vehicles},\n\tauthor = {Ard, Tyler and Guo, Longxiang and Han, Jihun and Jia, Yunyi and Vahidi, Ardalan and Karbowski, Dominik},\n\tyear = {2023},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control, XIL Workflow},\n\tpages = {1279 -- 1291},\n}\n\n
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\n \n\n \n \n \n \n \n \n Energy Impact of Connecting Multiple Signalized Intersections to Energy-Efficient Driving: Simulation and Experimental Results.\n \n \n \n \n\n\n \n Han, J.; Shen, D.; Jeong, J.; Russo, M. D.; Kim, N.; Grave, J. J.; Karbowski, D.; Rousseau, A.; and Stutenberg, K.\n\n\n \n\n\n\n IEEE Control Systems Letters, 7: 1297 – 1302. 2023.\n \n\n\n\n
\n\n\n\n \n \n \"EnergyPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{han_energy_2023,\n\ttitle = {Energy {Impact} of {Connecting} {Multiple} {Signalized} {Intersections} to {Energy}-{Efficient} {Driving}: {Simulation} and {Experimental} {Results}},\n\tvolume = {7},\n\turl = {https://anl.box.com/s/0cozkl9bxr811kbsh3hbvubx93dpwgir},\n\tdoi = {10.1109/LCSYS.2023.3234808},\n\tabstract = {Vehicle-to-everything (V2X) communication connects vehicles and enables collision-free and energy-efficient driving, such as eco-approaches and departures at signalized intersections. An\nincreased connectivity range can connect multiple signalized intersections and lead to long-term energy-efficient driving using richer information. However, no published studies to date\nprovide insights into the energy saving potential of increasing the connectivity range. In this article, we present a V2X-enabled eco-driving control that can perform multiple traffic signal ecoapproaches, and we systematically design a large-scale simulation study to quantify the energy impact of the increased V2X range for various scenarios. Simulation results show that the V2X-enabled eco-driving control can reduce energy use by up to 40\\%, on average, compared to the baseline, depending on road attributes and vehicle powertrain type. We validate these findings by evaluating the controller through a vehicle-in-theloop (VIL) test platform.},\n\tjournal = {IEEE Control Systems Letters},\n\tauthor = {Han, Jihun and Shen, Daliang and Jeong, Jongryeol and Russo, Miriam Di and Kim, Namdoo and Grave, Julien Jean and Karbowski, Dominik and Rousseau, Aymeric and Stutenberg, Kevin},\n\tyear = {2023},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n\tpages = {1297 -- 1302},\n}\n\n
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\n Vehicle-to-everything (V2X) communication connects vehicles and enables collision-free and energy-efficient driving, such as eco-approaches and departures at signalized intersections. An increased connectivity range can connect multiple signalized intersections and lead to long-term energy-efficient driving using richer information. However, no published studies to date provide insights into the energy saving potential of increasing the connectivity range. In this article, we present a V2X-enabled eco-driving control that can perform multiple traffic signal ecoapproaches, and we systematically design a large-scale simulation study to quantify the energy impact of the increased V2X range for various scenarios. Simulation results show that the V2X-enabled eco-driving control can reduce energy use by up to 40%, on average, compared to the baseline, depending on road attributes and vehicle powertrain type. We validate these findings by evaluating the controller through a vehicle-in-theloop (VIL) test platform.\n
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\n \n\n \n \n \n \n \n Addressing The Quality-Value-Satisfaction-Loyalty Framework In Post-Use Behaviors Of Shared E-Scooter Riders.\n \n \n \n\n\n \n Aksari, S.; Javadinasr, M.; Mohammadian, A.; Khan, N. A.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{aksari_addressing_2023,\n\ttitle = {Addressing {The} {Quality}-{Value}-{Satisfaction}-{Loyalty} {Framework} {In} {Post}-{Use} {Behaviors} {Of} {Shared} {E}-{Scooter} {Riders}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Aksari, Sadjad and Javadinasr, Mohammadjavad and Mohammadian, Abolfazl and Khan, Nazmul Arefin and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n E-scooter Safety: Exploring Attitudinal Factors Affecting Risky Behavior among Shared E-scooter Riders in Chicago.\n \n \n \n\n\n \n Asgharpour, S.; Javadinasr, M.; Mohammadian, A.; Khan, N. A.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{asgharpour_e-scooter_2023,\n\ttitle = {E-scooter {Safety}: {Exploring} {Attitudinal} {Factors} {Affecting} {Risky} {Behavior} among {Shared} {E}-scooter {Riders} in {Chicago}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Asgharpour, Sina and Javadinasr, Mohammadjavad and Mohammadian, Abolfazl and Khan, Nazmul Arefin and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n Determinants of residential mobility: an adaptive retrospective survey method.\n \n \n \n\n\n \n Ghasri, M.; Rashidi, T.; and Auld, J.\n\n\n \n\n\n\n Transportation letters, 15(2): 129–141. 2023.\n Publisher: Taylor & Francis\n\n\n\n
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@article{ghasri_determinants_2023,\n\ttitle = {Determinants of residential mobility: an adaptive retrospective survey method},\n\tvolume = {15},\n\tnumber = {2},\n\tjournal = {Transportation letters},\n\tauthor = {Ghasri, Milad and Rashidi, Taha and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: Taylor \\& Francis},\n\tpages = {129--141},\n}\n\n
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\n \n\n \n \n \n \n \n The co-determination of home and workplace relocation durations using survival copula analysis.\n \n \n \n\n\n \n Bostanara, M.; Rashidi, T. H.; Khan, N. A.; Auld, J.; Ghasri, M.; and Grazian, C.\n\n\n \n\n\n\n Computers, Environment and Urban Systems, 99: 101898. 2023.\n Publisher: Pergamon\n\n\n\n
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@article{bostanara_co-determination_2023,\n\ttitle = {The co-determination of home and workplace relocation durations using survival copula analysis},\n\tvolume = {99},\n\tjournal = {Computers, Environment and Urban Systems},\n\tauthor = {Bostanara, Maryam and Rashidi, Taha Hossein and Khan, Nazmul Arefin and Auld, Joshua and Ghasri, Milad and Grazian, Clara},\n\tyear = {2023},\n\tnote = {Publisher: Pergamon},\n\tpages = {101898},\n}\n\n
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\n \n\n \n \n \n \n \n Do people spend travel time the way they think they would? a comparative study of generic and trip-specific travel time allocation using hybrid multiple discrete continuous (MDC) framework.\n \n \n \n\n\n \n Enam, A.; Auld, J.; and Rashidi, T. H\n\n\n \n\n\n\n Transportation Letters,1–12. 2023.\n Publisher: Taylor & Francis\n\n\n\n
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@article{enam_people_2023,\n\ttitle = {Do people spend travel time the way they think they would? a comparative study of generic and trip-specific travel time allocation using hybrid multiple discrete continuous ({MDC}) framework},\n\tjournal = {Transportation Letters},\n\tauthor = {Enam, Annesha and Auld, Joshua and Rashidi, Taha H},\n\tyear = {2023},\n\tnote = {Publisher: Taylor \\& Francis},\n\tpages = {1--12},\n}\n\n
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\n \n\n \n \n \n \n \n Evidence for Modal Inertia in Multimodal Tours: An Integrated Choice and Latent Variable Modeling Approach.\n \n \n \n\n\n \n Jabbari, P.; Khan, N. A.; and MacKenzie, D.\n\n\n \n\n\n\n Transportation Research Record,03611981231170185. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{jabbari_evidence_2023,\n\ttitle = {Evidence for {Modal} {Inertia} in {Multimodal} {Tours}: {An} {Integrated} {Choice} and {Latent} {Variable} {Modeling} {Approach}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Jabbari, Parastoo and Khan, Nazmul Arefin and MacKenzie, Don},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {03611981231170185},\n}\n\n
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\n \n\n \n \n \n \n \n Coupling shared E-scooters and public transit: a spatial and temporal analysis.\n \n \n \n\n\n \n Javadiansr, M.; Davatgari, A.; Rahimi, E.; Mohammadi, M.; Mohammadian, A.; and Auld, J.\n\n\n \n\n\n\n Transportation Letters,1–18. 2023.\n Publisher: Taylor & Francis\n\n\n\n
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@article{javadiansr_coupling_2023,\n\ttitle = {Coupling shared {E}-scooters and public transit: a spatial and temporal analysis},\n\tjournal = {Transportation Letters},\n\tauthor = {Javadiansr, Mohammadjavad and Davatgari, Amir and Rahimi, Ehsan and Mohammadi, Motahare and Mohammadian, Abolfazl and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: Taylor \\& Francis},\n\tpages = {1--18},\n}\n\n
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\n \n\n \n \n \n \n \n Exploring The Effects Of E-Scooter Adoption In A Region: A Case Study Of Chicago.\n \n \n \n\n\n \n Khan, N. A.; Gurumurthy, K. M.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{khan_exploring_2023,\n\ttitle = {Exploring {The} {Effects} {Of} {E}-{Scooter} {Adoption} {In} {A} {Region}: {A} {Case} {Study} {Of} {Chicago}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Khan, Nazmul Arefin and Gurumurthy, Krishna Murthy and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n Unveiling the Influence of Latent Factors on Micromobility Mode Choice.\n \n \n \n\n\n \n Singh, R.; Oshanreh, M.; Khan, N. A.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{singh_unveiling_2023,\n\ttitle = {Unveiling the {Influence} of {Latent} {Factors} on {Micromobility} {Mode} {Choice}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Singh, Rubina and Oshanreh, Mohammad and Khan, Nazmul Arefin and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n Effects of Trip Attributes on Ridehailing Driver Trip Request Acceptance.\n \n \n \n\n\n \n Tu, Y. T.; Khaloei, M.; Khan, N. A.; and MacKenzie, D.\n\n\n \n\n\n\n International Journal of Sustainable Transportation, accepted: accepted. 2023.\n Publisher: Pergamon\n\n\n\n
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@article{tu_effects_2023,\n\ttitle = {Effects of {Trip} {Attributes} on {Ridehailing} {Driver} {Trip} {Request} {Acceptance}},\n\tvolume = {accepted},\n\tjournal = {International Journal of Sustainable Transportation},\n\tauthor = {Tu, Yuanjie Tukey and Khaloei, Moein and Khan, Nazmul Arefin and MacKenzie, Don},\n\tyear = {2023},\n\tnote = {Publisher: Pergamon},\n\tpages = {accepted},\n}\n\n
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\n \n\n \n \n \n \n \n A freight asset choice model for agent-based simulation models.\n \n \n \n\n\n \n Zuniga-Garcia, N.; Ismael, A.; and Stinson, M.\n\n\n \n\n\n\n Procedia Computer Science, 220: 704–709. 2023.\n Publisher: Elsevier\n\n\n\n
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@article{zuniga-garcia_freight_2023-1,\n\ttitle = {A freight asset choice model for agent-based simulation models},\n\tvolume = {220},\n\tjournal = {Procedia Computer Science},\n\tauthor = {Zuniga-Garcia, Natalia and Ismael, Abdelrahman and Stinson, Monique},\n\tyear = {2023},\n\tnote = {Publisher: Elsevier},\n\tpages = {704--709},\n}\n\n
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\n \n\n \n \n \n \n \n Individual-Level Analysis Of The Integration Of E-Scooters And Transit.\n \n \n \n\n\n \n Javadinasr, M.; Asgharpour, S.; Mohammadian, A.; Khan, N. A.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{javadinasr_individual-level_2023,\n\ttitle = {Individual-{Level} {Analysis} {Of} {The} {Integration} {Of} {E}-{Scooters} {And} {Transit}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Javadinasr, Mohammadjavad and Asgharpour, Sina and Mohammadian, Abolfazl and Khan, Nazmul Arefin and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n Modeling Household-Level Party Choice Behavior for Multiparty Activities: A Random Parameter Nested Logit Modeling Approach.\n \n \n \n\n\n \n Khan, N. A.; and Auld, J.\n\n\n \n\n\n\n Transportation Letters,under–review. 2023.\n Publisher: Taylor & Francis\n\n\n\n
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@article{khan_modeling_2023,\n\ttitle = {Modeling {Household}-{Level} {Party} {Choice} {Behavior} for {Multiparty} {Activities}: {A} {Random} {Parameter} {Nested} {Logit} {Modeling} {Approach}},\n\tjournal = {Transportation Letters},\n\tauthor = {Khan, Nazmul Arefin and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: Taylor \\& Francis},\n\tpages = {under--review},\n}\n\n
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\n \n\n \n \n \n \n \n Investigating Long-Distance Travel Patterns: Factors Shaping Trip Frequency, Start Time Preferences, Mode Choice And Destination Location Choices.\n \n \n \n\n\n \n Mohammadi, M.; Khan, N. A.; Davatgari, A.; Mohammadian, A.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{mohammadi_investigating_2023,\n\ttitle = {Investigating {Long}-{Distance} {Travel} {Patterns}: {Factors} {Shaping} {Trip} {Frequency}, {Start} {Time} {Preferences}, {Mode} {Choice} {And} {Destination} {Location} {Choices}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Mohammadi, Motahare and Khan, Nazmul Arefin and Davatgari, Amir and Mohammadian, Abolfazl and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n Tour-Level Mode Choices and Their Impact on Car Ownership Decisions.\n \n \n \n\n\n \n Oshanreh, M.; Singh, R.; Khan, N. A.; and Auld, J.\n\n\n \n\n\n\n Transportation Research Record,preparing. 2023.\n Publisher: SAGE Publications Sage CA: Los Angeles, CA\n\n\n\n
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@article{oshanreh_tour-level_2023,\n\ttitle = {Tour-{Level} {Mode} {Choices} and {Their} {Impact} on {Car} {Ownership} {Decisions}},\n\tjournal = {Transportation Research Record},\n\tauthor = {Oshanreh, Mohammad and Singh, Rubina and Khan, Nazmul Arefin and Auld, Joshua},\n\tyear = {2023},\n\tnote = {Publisher: SAGE Publications Sage CA: Los Angeles, CA},\n\tpages = {preparing},\n}\n\n
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\n \n\n \n \n \n \n \n Do We Care as Much to Pay to Stay Productive Travelling on an AV.\n \n \n \n\n\n \n Shakeel, K.; Khan, N. A.; Ardeshiri, A.; and Rashidi, T.\n\n\n \n\n\n\n Transportation Research Part A: Policy and Practice,under–review. 2023.\n Publisher: Pergamon\n\n\n\n
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@article{shakeel_we_2023,\n\ttitle = {Do {We} {Care} as {Much} to {Pay} to {Stay} {Productive} {Travelling} on an {AV}},\n\tjournal = {Transportation Research Part A: Policy and Practice},\n\tauthor = {Shakeel, Kiran and Khan, Nazmul Arefin and Ardeshiri, Ali and Rashidi, Taha},\n\tyear = {2023},\n\tnote = {Publisher: Pergamon},\n\tpages = {under--review},\n}\n\n
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\n \n\n \n \n \n \n \n LC-ABM: A Lagrangian Coordinates Model for Large-Scale Heterogeneous Traffic.\n \n \n \n\n\n \n de Souza, F.; Gurumurthy, K. M.; Verbas, O.; and Auld, J.\n\n\n \n\n\n\n In Washington, D.C., August 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{de_souza_lc-abm_2023,\n\taddress = {Washington, D.C.},\n\ttitle = {{LC}-{ABM}: {A} {Lagrangian} {Coordinates} {Model} for {Large}-{Scale} {Heterogeneous} {Traffic}},\n\tabstract = {Agent-based modeling has become increasingly prevalent in the field of transportation systems simulation as the scenarios around new technologies and policies that such models are applied to become increasingly complex. This increased complexity, in terms of traveler behavior, traffic flow, modal operations, system management, and so on, requires increasingly sensitive and detailed representation of the core components of the simulation, especially as it relates to traffic flow. Many future mobility solutions rely on connectivity, communication, advanced sensing and detailed information flows, which all need to be included, while the simulation also need to remain computationally efficient. In this paper we propose an new Lagrangian-Coordinate Agent-based model (LC-ABM) of traffic flow which combines computational efficiency while capturing vehicle interactions within a link through a reparameterization of the fundamental flow diagram as speed-spacing relation. This model enables and individual vehicle’s position to be tracked throughout each link while still maintaining consistency with the overall fundamental flow properties. This model allows multi-class traffic flow by allowing individual vehicle types to maintain different speed-spacing relationships, and also allows for mid-lane blockages and other bottlenecks by allowing the capacity within the link to vary at specified points. This advantage of the LC-ABM traffic flow model is demonstrated through a study of on-demand pick-up and drop-off trips in Bloomington, IL. We demonstrate the impact of different TNC penetration rate, number of trips and drop-off dwell time on overall network speed, and show that the model is capable of representing this phenomenon.},\n\tauthor = {de Souza, Felipe and Gurumurthy, Krishna M. and Verbas, Omer and Auld, Joshua},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n
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\n Agent-based modeling has become increasingly prevalent in the field of transportation systems simulation as the scenarios around new technologies and policies that such models are applied to become increasingly complex. This increased complexity, in terms of traveler behavior, traffic flow, modal operations, system management, and so on, requires increasingly sensitive and detailed representation of the core components of the simulation, especially as it relates to traffic flow. Many future mobility solutions rely on connectivity, communication, advanced sensing and detailed information flows, which all need to be included, while the simulation also need to remain computationally efficient. In this paper we propose an new Lagrangian-Coordinate Agent-based model (LC-ABM) of traffic flow which combines computational efficiency while capturing vehicle interactions within a link through a reparameterization of the fundamental flow diagram as speed-spacing relation. This model enables and individual vehicle’s position to be tracked throughout each link while still maintaining consistency with the overall fundamental flow properties. This model allows multi-class traffic flow by allowing individual vehicle types to maintain different speed-spacing relationships, and also allows for mid-lane blockages and other bottlenecks by allowing the capacity within the link to vary at specified points. This advantage of the LC-ABM traffic flow model is demonstrated through a study of on-demand pick-up and drop-off trips in Bloomington, IL. We demonstrate the impact of different TNC penetration rate, number of trips and drop-off dwell time on overall network speed, and show that the model is capable of representing this phenomenon.\n
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\n \n\n \n \n \n \n \n On-demand Ride-pooling with Walking Legs: Decomposition Approach for Dynamic Matching and Virtual Stops Selection.\n \n \n \n\n\n \n Sarma, N. J.; Gurumurthy, K. M.; Hyland, M.; Bahk, Y.; de Souza, F.; Verbas, O.; and Wang, Z.\n\n\n \n\n\n\n In Washington, D.C., August 2023. \n \n\n\n\n
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@inproceedings{sarma_-demand_2023,\n\taddress = {Washington, D.C.},\n\ttitle = {On-demand {Ride}-pooling with {Walking} {Legs}: {Decomposition} {Approach} for {Dynamic} {Matching} and {Virtual} {Stops} {Selection}},\n\tabstract = {Door-to-door (D2D) ride-hailing services currently dominate the mobility-on-demand (MOD) market, but several alternative MOD service types offer operational and societal benefits. Three such MOD services include D2D ride-pooling, corner-to-corner (C2C) ride-hailing, and C2C ride-pooling. C2C service requires travelers to walk a short distance to/from a pickup/drop-off location. The goals of this study are two-fold. First, we compare these four MOD services in terms of operator costs (e.g., vehicle kilometers per request served) and user costs (e.g., assignment time, wait time, walk time, and invehicle time). Second, we develop an effective and scalable decision policy and solution algorithm for operating a C2C ride-pooling service. At each decision epoch, the operator must dynamically assign vehicles to requests, route and schedule vehicles, and assign travelers to pickup and drop-off (PUDO) locations. To address this problem, we propose decomposing the problem into a matching, routing, and scheduling subproblem, and a PUDO locations selection subproblem. We use geographic, network, and vehicle information, as well as optimization techniques to solve the two subproblems. The computational experiments confirm a clear trade-off across the four services in terms of operator costs and user costs. With D2D ridehailing as the baseline, (i) ride-pooling significantly reduces operator costs, while slightly increasing user costs; (ii) C2C slightly reduces operator costs while increasing user costs; (iii) combining ride-pooling and C2C appears to provide additive benefits in terms of operator costs.},\n\tauthor = {Sarma, Navjyoth J.S and Gurumurthy, Krishna M. and Hyland, Michael and Bahk, Younghun and de Souza, Felipe and Verbas, Omer and Wang, Zifan},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n
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\n Door-to-door (D2D) ride-hailing services currently dominate the mobility-on-demand (MOD) market, but several alternative MOD service types offer operational and societal benefits. Three such MOD services include D2D ride-pooling, corner-to-corner (C2C) ride-hailing, and C2C ride-pooling. C2C service requires travelers to walk a short distance to/from a pickup/drop-off location. The goals of this study are two-fold. First, we compare these four MOD services in terms of operator costs (e.g., vehicle kilometers per request served) and user costs (e.g., assignment time, wait time, walk time, and invehicle time). Second, we develop an effective and scalable decision policy and solution algorithm for operating a C2C ride-pooling service. At each decision epoch, the operator must dynamically assign vehicles to requests, route and schedule vehicles, and assign travelers to pickup and drop-off (PUDO) locations. To address this problem, we propose decomposing the problem into a matching, routing, and scheduling subproblem, and a PUDO locations selection subproblem. We use geographic, network, and vehicle information, as well as optimization techniques to solve the two subproblems. The computational experiments confirm a clear trade-off across the four services in terms of operator costs and user costs. With D2D ridehailing as the baseline, (i) ride-pooling significantly reduces operator costs, while slightly increasing user costs; (ii) C2C slightly reduces operator costs while increasing user costs; (iii) combining ride-pooling and C2C appears to provide additive benefits in terms of operator costs.\n
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\n \n\n \n \n \n \n \n Heuristic Approaches for The Electric Vehicle Scheduling Problem: Large-Scale Application With Next Day Operability Constraints.\n \n \n \n\n\n \n Davatgari, A.; Cokyasar, T.; Verbas, O.; and Mohammadian, A. (.\n\n\n \n\n\n\n In Washington, D.C., August 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{davatgari_heuristic_2023,\n\taddress = {Washington, D.C.},\n\ttitle = {Heuristic {Approaches} for {The} {Electric} {Vehicle} {Scheduling} {Problem}: {Large}-{Scale} {Application} {With} {Next} {Day} {Operability} {Constraints}},\n\tabstract = {This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming (MILP), we generate bus blocks (tours) that accommodate EVs, ensuring successful completion of each block while considering recharging requirements between blocks and during offhours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP with time constraints (SDVSPTC), and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints.},\n\tauthor = {Davatgari, Amir and Cokyasar, Taner and Verbas, Omer and Mohammadian, Abolfazl (Kouros)},\n\tmonth = aug,\n\tyear = {2023},\n}\n\n
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\n This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming (MILP), we generate bus blocks (tours) that accommodate EVs, ensuring successful completion of each block while considering recharging requirements between blocks and during offhours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP with time constraints (SDVSPTC), and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints.\n
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\n \n\n \n \n \n \n \n Framework For Analyzing Equity-Concerns Related to Mobility On-Demand.\n \n \n \n\n\n \n Kagho, G. O.; Gurumurthy, K. M.; Verbas, O.; and Auld, J.\n\n\n \n\n\n\n In Washington, D.C., August 2023. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{kagho_framework_2023,\n\taddress = {Washington, D.C.},\n\ttitle = {Framework {For} {Analyzing} {Equity}-{Concerns} {Related} to {Mobility} {On}-{Demand}},\n\tabstract = {This study looks at the concept of equity in the context of urban transportation, particularly focusing on Mobility on-Demand (MoD) simulations. MoD services, such as ride-hailing and shared autonomous vehicles, have the potential to transform transportation systems, offering new opportunities to enhance equity while reducing transport externalities. However, a comprehensive assessment of the equity impacts resulting from the integration of these services into various transport scenarios is lacking in most MoD simulations. Even though this is essential in informing policy and decision-making, as MoD impacts on equity are complex and depend on various factors, including service availability, affordability, accessibility, and their interaction with existing transportation modes. Therefore, this study presents a framework for evaluating and quantifying equity impact in MoD simulations. Several metrics are defined, such as demographic equity, spatial equity distribution, spatial regression analysis, hotspots and coldspots cluster identification and social inclusion analysis. The effectiveness of this framework is presented using a first-mile-last-mile subsidy case study for two US regions, Austin and Chicago. The findings from these case studies underscored the importance of equityfocused assessments in simulating MoD policy scenarios. And how it is essential to take into account the varying needs of different sociodemographic groups and ensure that the services are distributed in a manner that improves accessibility and affordability for all, particularly the most vulnerable groups.},\n\tauthor = {Kagho, Grace O. and Gurumurthy, Krishna M. and Verbas, Omer and Auld, Joshua},\n\tmonth = aug,\n\tyear = {2023},\n}\n
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\n This study looks at the concept of equity in the context of urban transportation, particularly focusing on Mobility on-Demand (MoD) simulations. MoD services, such as ride-hailing and shared autonomous vehicles, have the potential to transform transportation systems, offering new opportunities to enhance equity while reducing transport externalities. However, a comprehensive assessment of the equity impacts resulting from the integration of these services into various transport scenarios is lacking in most MoD simulations. Even though this is essential in informing policy and decision-making, as MoD impacts on equity are complex and depend on various factors, including service availability, affordability, accessibility, and their interaction with existing transportation modes. Therefore, this study presents a framework for evaluating and quantifying equity impact in MoD simulations. Several metrics are defined, such as demographic equity, spatial equity distribution, spatial regression analysis, hotspots and coldspots cluster identification and social inclusion analysis. The effectiveness of this framework is presented using a first-mile-last-mile subsidy case study for two US regions, Austin and Chicago. The findings from these case studies underscored the importance of equityfocused assessments in simulating MoD policy scenarios. And how it is essential to take into account the varying needs of different sociodemographic groups and ensure that the services are distributed in a manner that improves accessibility and affordability for all, particularly the most vulnerable groups.\n
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\n \n\n \n \n \n \n \n \n Modernizing the U.S. electric grid: A proposal to update transmission infrastructure for the future of electricity.\n \n \n \n \n\n\n \n Murphy, S.\n\n\n \n\n\n\n Environmental Progress & Sustainable Energy, 41(2): e13798. March 2022.\n Publisher: John Wiley & Sons, Ltd\n\n\n\n
\n\n\n\n \n \n \"ModernizingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{murphy_modernizing_2022,\n\ttitle = {Modernizing the {U}.{S}. electric grid: {A} proposal to update transmission infrastructure for the future of electricity},\n\tvolume = {41},\n\tissn = {1944-7442},\n\turl = {https://doi.org/10.1002/ep.13798},\n\tdoi = {10.1002/ep.13798},\n\tabstract = {Abstract Electric transmission infrastructure is often undervalued in the United States. However, a recent surge of electrical blackouts due to extreme weather conditions and natural disasters has brought the United States' aging transmission infrastructure to the spotlight. Recent cyberattacks on critical energy infrastructure have also brought attention to the vulnerabilities in the U.S. electric infrastructure. Advancements such as the growth of electrification in the transportation sector and the integration of variable renewable energy critical to the decarbonization of the energy sector, further challenge the capacity of current electrical infrastructure. Addressing deficiencies within U.S. electric infrastructure requires the collaboration of many stakeholders including federal agencies, state governments, local communities, and electricity generation and distribution entities. To properly undertake the challenges to the United States transmission infrastructure, it is recommended that potential solutions are accurately modeled, that the FERC uses its rulemaking capacity to foster collaboration, and that the federal government creates a national transmission loan program to specifically fund nationally significant transmission projects.},\n\tnumber = {2},\n\turldate = {2024-07-15},\n\tjournal = {Environmental Progress \\& Sustainable Energy},\n\tauthor = {Murphy, Sara},\n\tmonth = mar,\n\tyear = {2022},\n\tnote = {Publisher: John Wiley \\& Sons, Ltd},\n\tpages = {e13798},\n}\n\n
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\n Abstract Electric transmission infrastructure is often undervalued in the United States. However, a recent surge of electrical blackouts due to extreme weather conditions and natural disasters has brought the United States' aging transmission infrastructure to the spotlight. Recent cyberattacks on critical energy infrastructure have also brought attention to the vulnerabilities in the U.S. electric infrastructure. Advancements such as the growth of electrification in the transportation sector and the integration of variable renewable energy critical to the decarbonization of the energy sector, further challenge the capacity of current electrical infrastructure. Addressing deficiencies within U.S. electric infrastructure requires the collaboration of many stakeholders including federal agencies, state governments, local communities, and electricity generation and distribution entities. To properly undertake the challenges to the United States transmission infrastructure, it is recommended that potential solutions are accurately modeled, that the FERC uses its rulemaking capacity to foster collaboration, and that the federal government creates a national transmission loan program to specifically fund nationally significant transmission projects.\n
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\n \n\n \n \n \n \n \n \n Potential Energy Saving of V2V-Connected Vehicles in Large-Scale Traffic.\n \n \n \n \n\n\n \n Hyeon, E.; Han, J.; Shen, D.; Karbowski, D.; Kim, N.; and Rousseau, A.\n\n\n \n\n\n\n In 10th IFAC International Symposium on Advances in Automotive Control (in review), Columbus, OH, USA, August 2022. \n ANL\n\n\n\n
\n\n\n\n \n \n \"PotentialPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{hyeon_potential_2022,\n\taddress = {Columbus, OH, USA},\n\ttitle = {Potential {Energy} {Saving} of {V2V}-{Connected} {Vehicles} in {Large}-{Scale} {Traffic}},\n\turl = {https://anl.box.com/s/ax45bc5dz01h9gxtu84ewkhe03y7mc8i},\n\tdoi = {https://doi.org/10.1016/j.ifacol.2022.10.265},\n\tabstract = {In realistic car-following eco-driving scenarios, preceding vehicles’ presence and behavior can significantly impact vehicle energy efficiency. Increasing vehicle-to-vehicle (V2V) connectivity, more accurate prediction of the preceding vehicle’s future trajectory becomes possible, bringing further energy savings. However, prediction accuracy and the corresponding energy savings depend on the specific scenario. This paper identifies factors impacting prediction accuracy and investigates how these impacts lead to energy savings for the eco-driving control system that we focus on. First, we integrate a trajectory predictor using V2V information with an eco-driving controller. Then, we design a systematic parameter study that generates extensive downstream traffic environments with various V2V connectivity ranges, and we conduct initial investigations into the relationship between the influential factors and eco-driving behaviors. Finally, we validate our findings from the parameter study by evaluating a V2V-enabled ecodriving control system using a multi-vehicle simulation tool with high-fidelity powertrain models. Results show that a longer-term prediction horizon, equivalent to a look-ahead horizon for eco-driving control, saves more energy. Furthermore, increasing the prediction accuracy with sufficient V2V information from more V2V-connected vehicles enables near-optimal energy savings for various car-following behaviors in downstream traffic.},\n\tbooktitle = {10th {IFAC} {International} {Symposium} on {Advances} in {Automotive} {Control} (in review)},\n\tauthor = {Hyeon, Eunjeong and Han, Jihun and Shen, Daliang and Karbowski, Dominik and Kim, Namwook and Rousseau, Aymeric},\n\tmonth = aug,\n\tyear = {2022},\n\tnote = {ANL},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n}\n\n
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\n In realistic car-following eco-driving scenarios, preceding vehicles’ presence and behavior can significantly impact vehicle energy efficiency. Increasing vehicle-to-vehicle (V2V) connectivity, more accurate prediction of the preceding vehicle’s future trajectory becomes possible, bringing further energy savings. However, prediction accuracy and the corresponding energy savings depend on the specific scenario. This paper identifies factors impacting prediction accuracy and investigates how these impacts lead to energy savings for the eco-driving control system that we focus on. First, we integrate a trajectory predictor using V2V information with an eco-driving controller. Then, we design a systematic parameter study that generates extensive downstream traffic environments with various V2V connectivity ranges, and we conduct initial investigations into the relationship between the influential factors and eco-driving behaviors. Finally, we validate our findings from the parameter study by evaluating a V2V-enabled ecodriving control system using a multi-vehicle simulation tool with high-fidelity powertrain models. Results show that a longer-term prediction horizon, equivalent to a look-ahead horizon for eco-driving control, saves more energy. Furthermore, increasing the prediction accuracy with sufficient V2V information from more V2V-connected vehicles enables near-optimal energy savings for various car-following behaviors in downstream traffic.\n
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\n \n\n \n \n \n \n \n \n Analytical Anticipative Optimal Drivability Car-Following Model.\n \n \n \n \n\n\n \n Han, J.; Karbowski, D.; and Rousseau, A.\n\n\n \n\n\n\n In 2022 American Control Conference (ACC), Atlanta, GA, USA, June 2022. \n \n\n\n\n
\n\n\n\n \n \n \"AnalyticalPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{han_analytical_2022,\n\taddress = {Atlanta, GA, USA},\n\ttitle = {Analytical {Anticipative} {Optimal} {Drivability} {Car}-{Following} {Model}},\n\turl = {https://anl.box.com/s/0vpi4f5hj4ouiostw6tyzaymib26fmm4},\n\tdoi = {https://doi.org/10.23919/ACC53348.2022.9867588},\n\tabstract = {Replicating a human car-following behavior becomes more important for developing either a human driver model or human-like adaptive cruise control. The human driver model is especially a key element in simulation tools used for the development and evaluation of connected and automated vehicle driving controls. In this paper, we propose an analytical\nanticipative optimal drivability car-following model that can capture a dynamic car-following behavior while maximizing driving comfort without collisions in a computational-efficient\nway. We formulate drivability-oriented car-following as an optimal control problem, and then reformulate it to a bi-level optimization problem in order to facilitate analytical treatment.\nBy employing optimal control theory, we can transform the bi-level optimization problem into a nonlinear programming problem and derive its analytical solutions. To validate the\nproposed model, we post-processed and used Next Generation SIMulation (NGSIM) data. Results show that the proposed parametric model can generate stable car-following behaviors and its vehicle state trajectories are well matched with NGSIM data, thereby significantly improving root-mean-square error of\nspeed and distance gap compared to the existing car-following model.},\n\tbooktitle = {2022 {American} {Control} {Conference} ({ACC})},\n\tauthor = {Han, Jihun and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = jun,\n\tyear = {2022},\n\tkeywords = {DOE SMART, Driver Modeling, RoadRunner},\n}\n\n
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\n Replicating a human car-following behavior becomes more important for developing either a human driver model or human-like adaptive cruise control. The human driver model is especially a key element in simulation tools used for the development and evaluation of connected and automated vehicle driving controls. In this paper, we propose an analytical anticipative optimal drivability car-following model that can capture a dynamic car-following behavior while maximizing driving comfort without collisions in a computational-efficient way. We formulate drivability-oriented car-following as an optimal control problem, and then reformulate it to a bi-level optimization problem in order to facilitate analytical treatment. By employing optimal control theory, we can transform the bi-level optimization problem into a nonlinear programming problem and derive its analytical solutions. To validate the proposed model, we post-processed and used Next Generation SIMulation (NGSIM) data. Results show that the proposed parametric model can generate stable car-following behaviors and its vehicle state trajectories are well matched with NGSIM data, thereby significantly improving root-mean-square error of speed and distance gap compared to the existing car-following model.\n
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\n \n\n \n \n \n \n \n \n Koopman Model Predictive Control for Eco-Driving of Automated Vehicles.\n \n \n \n \n\n\n \n Gupta, S.; Shen, D.; Karbowski, D.; and Rousseau, A.\n\n\n \n\n\n\n In 2022 American Control Conference (ACC), Atlanta, GA, USA, June 2022. IEEE\n ANL\n\n\n\n
\n\n\n\n \n \n \"KoopmanPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{gupta_koopman_2022,\n\taddress = {Atlanta, GA, USA},\n\ttitle = {Koopman {Model} {Predictive} {Control} for {Eco}-{Driving} of {Automated} {Vehicles}},\n\turl = {https://anl.box.com/s/767hekd8hze41g96eaj3eqcea1cr5jzk},\n\tdoi = {https://doi.org/10.23919/ACC53348.2022.9867636},\n\tabstract = {In this paper, we develop a data-driven process for building a model predictive control (MPC) for eco-driving of automated vehicles. The process involves performing system identification in which the non-linear vehicle dynamics model is approximated by the Koopman operator, a linear predictor of higher state-dimension, in a data-driven framework. This approach allows us to formulate the eco-driving problem in a constrained quadratic program that leads to a computationally fast MPC. The MPC is then implemented as a closed-loop control of an electric vehicle in numerical simulations for demonstration.},\n\tbooktitle = {2022 {American} {Control} {Conference} ({ACC})},\n\tpublisher = {IEEE},\n\tauthor = {Gupta, Shobhit and Shen, Daliang and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = jun,\n\tyear = {2022},\n\tnote = {ANL},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n}\n\n
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\n In this paper, we develop a data-driven process for building a model predictive control (MPC) for eco-driving of automated vehicles. The process involves performing system identification in which the non-linear vehicle dynamics model is approximated by the Koopman operator, a linear predictor of higher state-dimension, in a data-driven framework. This approach allows us to formulate the eco-driving problem in a constrained quadratic program that leads to a computationally fast MPC. The MPC is then implemented as a closed-loop control of an electric vehicle in numerical simulations for demonstration.\n
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\n \n\n \n \n \n \n \n \n Data-Driven Design of Model Predictive Control for Powertrain-Aware Eco-Driving Considering Nonlinearities Using Koopman Analysis.\n \n \n \n \n\n\n \n Shen, D.; Han, J.; Karbowski, D.; and Rousseau, A.\n\n\n \n\n\n\n In 10th IFAC International Symposium on Advances in Automotive Control (accepted), Columbus, OH, USA, August 2022. \n ANL\n\n\n\n
\n\n\n\n \n \n \"Data-DrivenPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{shen_data-driven_2022,\n\taddress = {Columbus, OH, USA},\n\ttitle = {Data-{Driven} {Design} of {Model} {Predictive} {Control} for {Powertrain}-{Aware} {Eco}-{Driving} {Considering} {Nonlinearities} {Using} {Koopman} {Analysis}},\n\turl = {https://anl.box.com/s/z10wanslss63r43ijydihby9izbkfm31},\n\tdoi = {https://doi.org/10.1016/j.ifacol.2022.10.271},\n\tabstract = {Eco-driving is a highly nonlinear control problem. The\nnonlinearities include the complex energy\nconversion/dissipation in the powertrain, environmental\ninfluences such as road grade and aerodynamic drag, and\nconstraints due to traffic signs, safety issues, and\nphysical limits of the vehicle system. In recent years,\nresearchers have increasingly revisited the Koopman\noperator to linearize nonlinear dynamics so that the lifted\nsystem evolves linearly in a higher dimensional space. This\npaper adopts such an approximation technique to construct\nthe lifted state space in a data-driven procedure that\nallows us to incorporate in the cost function the\npowertrain nonlinear inefficiency in vehicle speed, and\nsystem perturbations due to the nonlinear road grade\nnonlinear in position. In addition, the nonlinear\nconstraints in states can also be handled linearly. The\nresultant formulation of a linearly constrained quadratic\nprogram can be readily applied to design a model predictive\ncontrol that enjoys a low computation load as with a linear\ndynamic system. Simulation results demonstrate additional\nenergy saving potential compared to a linear approach.},\n\tbooktitle = {10th {IFAC} {International} {Symposium} on {Advances} in {Automotive} {Control} (accepted)},\n\tauthor = {Shen, Daliang and Han, Jihun and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = aug,\n\tyear = {2022},\n\tnote = {ANL},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n}\n\n
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\n Eco-driving is a highly nonlinear control problem. The nonlinearities include the complex energy conversion/dissipation in the powertrain, environmental influences such as road grade and aerodynamic drag, and constraints due to traffic signs, safety issues, and physical limits of the vehicle system. In recent years, researchers have increasingly revisited the Koopman operator to linearize nonlinear dynamics so that the lifted system evolves linearly in a higher dimensional space. This paper adopts such an approximation technique to construct the lifted state space in a data-driven procedure that allows us to incorporate in the cost function the powertrain nonlinear inefficiency in vehicle speed, and system perturbations due to the nonlinear road grade nonlinear in position. In addition, the nonlinear constraints in states can also be handled linearly. The resultant formulation of a linearly constrained quadratic program can be readily applied to design a model predictive control that enjoys a low computation load as with a linear dynamic system. Simulation results demonstrate additional energy saving potential compared to a linear approach.\n
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\n \n\n \n \n \n \n \n \n Joint routing of conventional and range-extended electric vehicles in a large metropolitan network.\n \n \n \n \n\n\n \n Subramanyam, A.; Cokyasar, T.; Larson, J.; and Stinson, M.\n\n\n \n\n\n\n Transportation Research Part C: Emerging Technologies, 144(103830). 2022.\n \n\n\n\n
\n\n\n\n \n \n \"JointPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{subramanyam_joint_2022,\n\ttitle = {Joint routing of conventional and range-extended electric vehicles in a large metropolitan network},\n\tvolume = {144},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0968090X22002510},\n\tdoi = {https://doi.org/10.1016/j.trc.2022.103830},\n\tnumber = {103830},\n\tjournal = {Transportation Research Part C: Emerging Technologies},\n\tauthor = {Subramanyam, Anirudh and Cokyasar, Taner and Larson, Jeffrey and Stinson, Monique},\n\tyear = {2022},\n}\n\n
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\n \n\n \n \n \n \n \n \n Dynamic Ride-Matching for Large-Scale Transportation Systems.\n \n \n \n \n\n\n \n Cokyasar, T.; de Souza, F.; Auld, J.; and Verbas, O.\n\n\n \n\n\n\n Transportation Research Record, 2676(3): 172–182. March 2022.\n \n\n\n\n
\n\n\n\n \n \n \"DynamicPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{cokyasar_dynamic_2022,\n\ttitle = {Dynamic {Ride}-{Matching} for {Large}-{Scale} {Transportation} {Systems}},\n\tvolume = {2676},\n\tissn = {0361-1981},\n\turl = {https://doi.org/10.1177/03611981211049422},\n\tdoi = {10.1177/03611981211049422},\n\tabstract = {Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58\\% at the cost of increasing the average pick up time by 26\\% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62\\% less computational time.},\n\tlanguage = {en},\n\tnumber = {3},\n\turldate = {2023-08-01},\n\tjournal = {Transportation Research Record},\n\tauthor = {Cokyasar, Taner and de Souza, Felipe and Auld, Joshua and Verbas, Omer},\n\tmonth = mar,\n\tyear = {2022},\n\tpages = {172--182},\n}\n\n
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\n Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.\n
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\n \n\n \n \n \n \n \n How do perceptions of safety and car ownership importance affect autonomous vehicle adoption?.\n \n \n \n\n\n \n Jabbari, P.; Auld, J.; and MacKenzie, D.\n\n\n \n\n\n\n Travel behaviour and society, 28: 128–140. 2022.\n Publisher: Elsevier\n\n\n\n
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@article{jabbari_how_2022,\n\ttitle = {How do perceptions of safety and car ownership importance affect autonomous vehicle adoption?},\n\tvolume = {28},\n\tjournal = {Travel behaviour and society},\n\tauthor = {Jabbari, Parastoo and Auld, Joshua and MacKenzie, Don},\n\tyear = {2022},\n\tnote = {Publisher: Elsevier},\n\tpages = {128--140},\n}\n\n
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\n \n\n \n \n \n \n \n Do automated vehicle (AV) enthusiasts value travel time differently from cautious travelers? an exploration of travelers’ attitudes towards AV.\n \n \n \n\n\n \n Enam, A.; Ardeshiri, A.; Rashidi, T. H; and Auld, J.\n\n\n \n\n\n\n Transportation planning and technology, 45(1): 19–38. 2022.\n Publisher: Routledge\n\n\n\n
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@article{enam_automated_2022,\n\ttitle = {Do automated vehicle ({AV}) enthusiasts value travel time differently from cautious travelers? an exploration of travelers’ attitudes towards {AV}},\n\tvolume = {45},\n\tnumber = {1},\n\tjournal = {Transportation planning and technology},\n\tauthor = {Enam, Annesha and Ardeshiri, Ali and Rashidi, Taha H and Auld, Joshua},\n\tyear = {2022},\n\tnote = {Publisher: Routledge},\n\tpages = {19--38},\n}\n\n
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\n \n\n \n \n \n \n \n Effects of trip-level characteristics on autonomous vehicle ownership: A US analysis.\n \n \n \n\n\n \n Tu, Y. T.; Jabbari, P.; Khan, N. A.; and MacKenzie, D.\n\n\n \n\n\n\n Transportation research part D: transport and environment, 108: 103321. 2022.\n Publisher: Pergamon\n\n\n\n
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@article{tu_effects_2022,\n\ttitle = {Effects of trip-level characteristics on autonomous vehicle ownership: {A} {US} analysis},\n\tvolume = {108},\n\tjournal = {Transportation research part D: transport and environment},\n\tauthor = {Tu, Yuanjie Tukey and Jabbari, Parastoo and Khan, Nazmul Arefin and MacKenzie, Don},\n\tyear = {2022},\n\tnote = {Publisher: Pergamon},\n\tpages = {103321},\n}\n\n
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\n \n\n \n \n \n \n \n Eliciting attitudinal factors affecting the continuance use of E-scooters: An empirical study in Chicago.\n \n \n \n\n\n \n Javadinasr, M.; Asgharpour, S.; Rahimi, E.; Choobchian, P.; Mohammadian, A. K.; and Auld, J.\n\n\n \n\n\n\n Transportation research part F: traffic psychology and behaviour, 87: 87–101. 2022.\n Publisher: Pergamon\n\n\n\n
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@article{javadinasr_eliciting_2022,\n\ttitle = {Eliciting attitudinal factors affecting the continuance use of {E}-scooters: {An} empirical study in {Chicago}},\n\tvolume = {87},\n\tjournal = {Transportation research part F: traffic psychology and behaviour},\n\tauthor = {Javadinasr, Mohammadjavad and Asgharpour, Sina and Rahimi, Ehsan and Choobchian, Pooria and Mohammadian, Abolfazl Kouros and Auld, Joshua},\n\tyear = {2022},\n\tnote = {Publisher: Pergamon},\n\tpages = {87--101},\n}\n\n
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\n  \n 2021\n \n \n (7)\n \n \n
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\n \n\n \n \n \n \n \n \n Cost of long-distance energy transmission by different carriers.\n \n \n \n \n\n\n \n DeSantis, D.; James, B. D.; Houchins, C.; Saur, G.; and Lyubovsky, M.\n\n\n \n\n\n\n iScience, 24(12). December 2021.\n Publisher: Elsevier\n\n\n\n
\n\n\n\n \n \n \"CostPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{desantis_cost_2021,\n\ttitle = {Cost of long-distance energy transmission by different carriers},\n\tvolume = {24},\n\tissn = {2589-0042},\n\turl = {https://doi.org/10.1016/j.isci.2021.103495},\n\tdoi = {10.1016/j.isci.2021.103495},\n\tabstract = {This paper compares the relative cost of long-distance, large-scale energy transmission by electricity, gaseous, and liquid carriers (e-fuels). The results indicate that the cost of electrical transmission per delivered MWh can be up to eight times higher than for hydrogen pipelines, about eleven times higher than for natural gas pipelines, and twenty to fifty times higher than for liquid fuels pipelines. These differences generally hold for shorter distances as well. The higher cost of electrical transmission is primarily because of lower carrying capacity (MW per line) of electrical transmission lines compared to the energy carrying capacity of the pipelines for gaseous and liquid fuels. The differences in the cost of transmission are important but often unrecognized and should be considered as a significant cost component in the analysis of various renewable energy production, distribution, and utilization scenarios.},\n\tnumber = {12},\n\turldate = {2024-07-15},\n\tjournal = {iScience},\n\tauthor = {DeSantis, Daniel and James, Brian D. and Houchins, Cassidy and Saur, Genevieve and Lyubovsky, Maxim},\n\tmonth = dec,\n\tyear = {2021},\n\tnote = {Publisher: Elsevier},\n}\n\n
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\n This paper compares the relative cost of long-distance, large-scale energy transmission by electricity, gaseous, and liquid carriers (e-fuels). The results indicate that the cost of electrical transmission per delivered MWh can be up to eight times higher than for hydrogen pipelines, about eleven times higher than for natural gas pipelines, and twenty to fifty times higher than for liquid fuels pipelines. These differences generally hold for shorter distances as well. The higher cost of electrical transmission is primarily because of lower carrying capacity (MW per line) of electrical transmission lines compared to the energy carrying capacity of the pipelines for gaseous and liquid fuels. The differences in the cost of transmission are important but often unrecognized and should be considered as a significant cost component in the analysis of various renewable energy production, distribution, and utilization scenarios.\n
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\n \n\n \n \n \n \n \n \n Forecasting Short to Mid-length Speed Trajectories of Preceding Vehicle using V2X Connectivity for Eco-Driving of Electric Vehicles.\n \n \n \n \n\n\n \n Hyeon, E.; Shen, D.; Karbowski, D.; and Rousseau, A.\n\n\n \n\n\n\n SAE International Journal of Advances and Current Practices in Mobility, 3(4): 1801–1809. April 2021.\n ANL\n\n\n\n
\n\n\n\n \n \n \"ForecastingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 14 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{hyeon_forecasting_2021,\n\ttitle = {Forecasting {Short} to {Mid}-length {Speed} {Trajectories} of {Preceding} {Vehicle} using {V2X} {Connectivity} for {Eco}-{Driving} of {Electric} {Vehicles}},\n\tvolume = {3},\n\turl = {https://anl.box.com/s/gganzlbo4iaj70fxqpcsiro59hmfcmqt},\n\tdoi = {https://doi.org/10.4271/2021-01-0431},\n\tabstract = {In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory. The simulation results show that the proposed predictor’s improved predictions can reduce energy consumption by 5\\% over eco-driving with the baseline predictor.},\n\tnumber = {4},\n\tjournal = {SAE International Journal of Advances and Current Practices in Mobility},\n\tauthor = {Hyeon, Eunjeong and Shen, Daliang and Karbowski, Dominik and Rousseau, Aymeric},\n\tmonth = apr,\n\tyear = {2021},\n\tnote = {ANL},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, Driver Modeling, RoadRunner, Vehicle control},\n\tpages = {1801--1809},\n}\n\n
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\n In recent studies, optimal control has shown promise as a strategy for enhancing the energy efficiency of connected autonomous vehicles. To maximize optimization performance, it is important to accurately predict constraints, especially separation from a vehicle in front. This paper proposes a novel prediction method for forecasting the trajectory of the nearest preceding car. The proposed predictor is designed to produce short to medium-length speed trajectories using a locally weighted polynomial regression algorithm. The polynomial coefficients are trained by using two types of information: (1) vehicle-to-vehicle (V2V) messages transmitted by multiple preceding vehicles and (2) vehicle-to-infrastructure (V2I) information broadcast by roadside equipment. The predictor’s performance was tested in a multi-vehicle traffic simulation platform, RoadRunner, previously developed by Argonne National Laboratory. The simulation results show that the proposed predictor’s improved predictions can reduce energy consumption by 5% over eco-driving with the baseline predictor.\n
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\n \n\n \n \n \n \n \n \n A Real-Time Intelligent Speed Optimization Planner Using Reinforcement Learning.\n \n \n \n \n\n\n \n Lee, W.; Han, J.; Zhang, Y.; Karbowski, D.; Rousseau, A.; and Kim, N.\n\n\n \n\n\n\n In SAE Technical Paper 2021-01-0434, Detroit, MI, USA, April 2021. SAE International\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{lee_real-time_2021,\n\taddress = {Detroit, MI, USA},\n\ttitle = {A {Real}-{Time} {Intelligent} {Speed} {Optimization} {Planner} {Using} {Reinforcement} {Learning}},\n\turl = {https://anl.box.com/s/c8ulclbcvrl5dve3yqans6vpv5onpz4v},\n\tdoi = {https://doi.org/10.4271/2021-01-0434},\n\tabstract = {As connectivity and sensing technologies become more mature,\nautomated vehicles can predict future driving situations and utilize this\ninformation to drive more energy-efficiently than human-driven\nvehicles. However, future information beyond the limited connectivity\nand sensing range is difficult to predict and utilize, limiting the energysaving\npotential of energy-efficient driving. Thus, we combine a\nconventional speed optimization planner, developed in our previous\nwork, and reinforcement learning to propose a real-time intelligent\nspeed optimization planner for connected and automated vehicles. We\nbriefly summarize the conventional speed optimization planner with\nlimited information, based on closed-form energy-optimal solutions,\nand present its multiple parameters that determine reference speed\ntrajectories. Then, we use a deep reinforcement learning (DRL)\nalgorithm, such as a deep Q-learning algorithm, to find the policy of\nhow to adjust these parameters in real-time to dynamically changing\nsituations in order to realize the full potential of energy-efficient\ndriving. The model-free DRL algorithm, based on the experience of\nthe system, can learn the optimal policy through iteratively interacting\nwith different driving scenarios without increasing the limited\nconnectivity and sensing range. The training process of the parameter\nadaptation policy exploits a high-fidelity simulation framework that\ncan simulate multiple vehicles with full powertrain models and the\ninteractions between vehicles and their environment. We consider\nintersection-approaching scenarios where there is one traffic light with\ndifferent signal phase and timing setup. Results show that the learned\noptimal policy enables the proposed intelligent speed optimization\nplanner to properly adjust the parameters in a piecewise constant\nmanner, leading to additional energy savings without increasing total\ntravel time compared to the conventional speed optimization planner.},\n\tbooktitle = {{SAE} {Technical} {Paper} 2021-01-0434},\n\tpublisher = {SAE International},\n\tauthor = {Lee, Woong and Han, Jihun and Zhang, Yaozhong and Karbowski, Dominik and Rousseau, Aymeric and Kim, Namwook},\n\tmonth = apr,\n\tyear = {2021},\n\tkeywords = {Connected and Automated Vehicles, DOE SMART, RoadRunner, Vehicle control},\n}\n\n
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\n As connectivity and sensing technologies become more mature, automated vehicles can predict future driving situations and utilize this information to drive more energy-efficiently than human-driven vehicles. However, future information beyond the limited connectivity and sensing range is difficult to predict and utilize, limiting the energysaving potential of energy-efficient driving. Thus, we combine a conventional speed optimization planner, developed in our previous work, and reinforcement learning to propose a real-time intelligent speed optimization planner for connected and automated vehicles. We briefly summarize the conventional speed optimization planner with limited information, based on closed-form energy-optimal solutions, and present its multiple parameters that determine reference speed trajectories. Then, we use a deep reinforcement learning (DRL) algorithm, such as a deep Q-learning algorithm, to find the policy of how to adjust these parameters in real-time to dynamically changing situations in order to realize the full potential of energy-efficient driving. The model-free DRL algorithm, based on the experience of the system, can learn the optimal policy through iteratively interacting with different driving scenarios without increasing the limited connectivity and sensing range. The training process of the parameter adaptation policy exploits a high-fidelity simulation framework that can simulate multiple vehicles with full powertrain models and the interactions between vehicles and their environment. We consider intersection-approaching scenarios where there is one traffic light with different signal phase and timing setup. Results show that the learned optimal policy enables the proposed intelligent speed optimization planner to properly adjust the parameters in a piecewise constant manner, leading to additional energy savings without increasing total travel time compared to the conventional speed optimization planner.\n
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\n \n\n \n \n \n \n \n A system of shared autonomous vehicles for Chicago.\n \n \n \n\n\n \n Gurumurthy, K. M.; Kockelman, K. M; and Auld, J.\n\n\n \n\n\n\n Journal of Transport and Land Use, 14(1): 933–948. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{gurumurthy_system_2021,\n\ttitle = {A system of shared autonomous vehicles for {Chicago}},\n\tvolume = {14},\n\tnumber = {1},\n\tjournal = {Journal of Transport and Land Use},\n\tauthor = {Gurumurthy, Krishna Murthy and Kockelman, Kara M and Auld, Joshua},\n\tyear = {2021},\n\tpages = {933--948},\n}\n\n
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\n \n\n \n \n \n \n \n A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow.\n \n \n \n\n\n \n Parsa, A. B.; Shabanpour, R.; Mohammadian, A.; Auld, J.; and Stephens, T.\n\n\n \n\n\n\n Transportation letters, 13(10): 687–695. 2021.\n Publisher: Taylor & Francis\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{parsa_data-driven_2021,\n\ttitle = {A data-driven approach to characterize the impact of connected and autonomous vehicles on traffic flow},\n\tvolume = {13},\n\tnumber = {10},\n\tjournal = {Transportation letters},\n\tauthor = {Parsa, Amir Bahador and Shabanpour, Ramin and Mohammadian, Abolfazl and Auld, Joshua and Stephens, Thomas},\n\tyear = {2021},\n\tnote = {Publisher: Taylor \\& Francis},\n\tpages = {687--695},\n}\n\n
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\n \n\n \n \n \n \n \n A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis.\n \n \n \n\n\n \n Bostanara, M.; Rashidi, T. H.; Auld, J.; and Ghasri, M.\n\n\n \n\n\n\n Computers, Environment and Urban Systems, 89: 101659. 2021.\n Publisher: Pergamon\n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{bostanara_comparison_2021,\n\ttitle = {A comparison between residential relocation timing of {Sydney} and {Chicago} residents: {A} {Bayesian} survival analysis},\n\tvolume = {89},\n\tjournal = {Computers, Environment and Urban Systems},\n\tauthor = {Bostanara, Maryam and Rashidi, Taha Hossein and Auld, Joshua and Ghasri, Milad},\n\tyear = {2021},\n\tnote = {Publisher: Pergamon},\n\tpages = {101659},\n}\n\n
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\n \n\n \n \n \n \n \n \n Designing a drone delivery network with automated battery swapping machines.\n \n \n \n \n\n\n \n Cokyasar, T.; Dong, W.; Jin, M.; and Verbas, İ. Ö.\n\n\n \n\n\n\n Computers & Operations Research, 129: 105177. May 2021.\n \n\n\n\n
\n\n\n\n \n \n \"DesigningPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{cokyasar_designing_2021,\n\ttitle = {Designing a drone delivery network with automated battery swapping machines},\n\tvolume = {129},\n\tissn = {0305-0548},\n\turl = {https://www.sciencedirect.com/science/article/pii/S030505482030294X},\n\tdoi = {10.1016/j.cor.2020.105177},\n\tabstract = {Drones are projected to alter last-mile delivery, but their short travel range is a concern. This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery.},\n\tlanguage = {en},\n\turldate = {2023-08-01},\n\tjournal = {Computers \\& Operations Research},\n\tauthor = {Cokyasar, Taner and Dong, Wenquan and Jin, Mingzhou and Verbas, İsmail Ömer},\n\tmonth = may,\n\tyear = {2021},\n\tkeywords = {Drone delivery, Mixed-integer nonlinear programming, Network optimization, Queueing theory},\n\tpages = {105177},\n}\n\n
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\n Drones are projected to alter last-mile delivery, but their short travel range is a concern. This study proposes a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery.\n
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\n \n\n \n \n \n \n \n \n The Lagrangian Coordinates and What it Means for First Order Traffic Flow Models.\n \n \n \n \n\n\n \n Leclercq, L.; Laval, J. A.; and Chevallier, E.\n\n\n \n\n\n\n In 2007. \n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{leclercq_lagrangian_2007,\n\ttitle = {The {Lagrangian} {Coordinates} and {What} it {Means} for {First} {Order} {Traffic} {Flow} {Models}},\n\tisbn = {978-0-08-045375-0},\n\turl = {https://trid.trb.org/View/820161},\n\turldate = {2024-01-31},\n\tauthor = {Leclercq, Ludovic and Laval, Jorge Andres and Chevallier, Estelle},\n\tyear = {2007},\n}\n\n
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\n \n\n \n \n \n \n \n \n The Link Transmission Model for dynamic network loading.\n \n \n \n \n\n\n \n Yperman, I.\n\n\n \n\n\n\n . June 2007.\n \n\n\n\n
\n\n\n\n \n \n \"ThePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{yperman_link_2007,\n\ttitle = {The {Link} {Transmission} {Model} for dynamic network loading},\n\turl = {https://lirias.kuleuven.be/1749225},\n\tabstract = {Dynamische verkeerstoedelingsmodellen worden gebruikt om de impact van infrastructuuraanpassingen in verkeersnetwerken te voorspellen en om de effecten van informatieverstrekking en verkeesbeheersingsmaatregelen in te schatten. Een verkeerssimulatiemodel is een basiscomponent van het dynamisch toedelingsmodel. In dit eindwerk wordt een verkeerssimulatiemodel ontwikkeld. Het Link Transmissie Model simuleert verkeersstromen in grote feitelijke netwerken die zowel snelwegen als stedelijke regio’s omvatten. Het gemodelleerde file-opbouw en file-afbouw proces sluit nauwer aan bij de realiteit dan in state-of-the-art macroscopische verkeerstoedelingsmodellen. Op kruispunten worden lokale capaciteitsbeperkingen en knoopvertragingen gedetailleerd in rekening gebracht. Het Link Transmissie Model stoelt op een rekenefficiënt algoritme waardoor verkeersstromen in grote netwerken gesimuleerd kunnen worden in een beperkte rekentijd},\n\tlanguage = {dut},\n\turldate = {2024-01-31},\n\tauthor = {Yperman, Isaak},\n\tmonth = jun,\n\tyear = {2007},\n}\n\n
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\n Dynamische verkeerstoedelingsmodellen worden gebruikt om de impact van infrastructuuraanpassingen in verkeersnetwerken te voorspellen en om de effecten van informatieverstrekking en verkeesbeheersingsmaatregelen in te schatten. Een verkeerssimulatiemodel is een basiscomponent van het dynamisch toedelingsmodel. In dit eindwerk wordt een verkeerssimulatiemodel ontwikkeld. Het Link Transmissie Model simuleert verkeersstromen in grote feitelijke netwerken die zowel snelwegen als stedelijke regio’s omvatten. Het gemodelleerde file-opbouw en file-afbouw proces sluit nauwer aan bij de realiteit dan in state-of-the-art macroscopische verkeerstoedelingsmodellen. Op kruispunten worden lokale capaciteitsbeperkingen en knoopvertragingen gedetailleerd in rekening gebracht. Het Link Transmissie Model stoelt op een rekenefficiënt algoritme waardoor verkeersstromen in grote netwerken gesimuleerd kunnen worden in een beperkte rekentijd\n
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\n \n\n \n \n \n \n \n \n Service network design in freight transportation.\n \n \n \n \n\n\n \n Crainic, T. G.\n\n\n \n\n\n\n European Journal of Operational Research, 122(2): 272–288. April 2000.\n \n\n\n\n
\n\n\n\n \n \n \"ServicePaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{crainic_service_2000,\n\ttitle = {Service network design in freight transportation},\n\tvolume = {122},\n\tissn = {0377-2217},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0377221799002337},\n\tdoi = {10.1016/S0377-2217(99)00233-7},\n\tabstract = {Tactical planning of operations is comprised of a set of interrelated decisions that aim to ensure an optimal allocation and utilization of resources to achieve the economic and customer service goals of the company. Tactical planning is particularly vital for intercity freight carriers that make intensive use of consolidation operations. Railways and less-than-truckload motor carriers are typical examples of such systems. Service Network Design is increasingly used to designate the main tactical issues for this type of carriers: selection and scheduling of services, specification of terminal operations, routing of freight. The corresponding models usually take the form of network design formulations that are difficult to solve, except in the simplest of cases. The paper presents a state-of-the-art review of service network design modelling efforts and mathematical programming developments for network design. A new classification of service network design problems and formulations is also introduced.},\n\tnumber = {2},\n\tjournal = {European Journal of Operational Research},\n\tauthor = {Crainic, Teodor Gabriel},\n\tmonth = apr,\n\tyear = {2000},\n\tkeywords = {Freight transportation, Modelling, Service network design, Tactical planning, Transportation},\n\tpages = {272--288},\n}\n\n
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\n Tactical planning of operations is comprised of a set of interrelated decisions that aim to ensure an optimal allocation and utilization of resources to achieve the economic and customer service goals of the company. Tactical planning is particularly vital for intercity freight carriers that make intensive use of consolidation operations. Railways and less-than-truckload motor carriers are typical examples of such systems. Service Network Design is increasingly used to designate the main tactical issues for this type of carriers: selection and scheduling of services, specification of terminal operations, routing of freight. The corresponding models usually take the form of network design formulations that are difficult to solve, except in the simplest of cases. The paper presents a state-of-the-art review of service network design modelling efforts and mathematical programming developments for network design. A new classification of service network design problems and formulations is also introduced.\n
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\n \n\n \n \n \n \n \n \n National Transmission Needs Study.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
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@misc{noauthor_national_nodate,\n\ttitle = {National {Transmission} {Needs} {Study}},\n\turl = {https://www.energy.gov/gdo/national-transmission-needs-study},\n\tabstract = {Formally known as the National Electric Transmission Congestion Study, the National Transmission Needs Study (Needs Study) is DOE’s triennial state of the grid report.},\n\tlanguage = {en},\n\turldate = {2024-07-16},\n\tjournal = {Energy.gov},\n}\n\n
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\n Formally known as the National Electric Transmission Congestion Study, the National Transmission Needs Study (Needs Study) is DOE’s triennial state of the grid report.\n
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\n \n\n \n \n \n \n \n \n 100% Clean Electricity by 2035 Study.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
\n\n\n\n \n \n \"100%Paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{noauthor_100_nodate,\n\ttitle = {100\\% {Clean} {Electricity} by 2035 {Study}},\n\turl = {https://www.nrel.gov/analysis/100-percent-clean-electricity-by-2035-study.html},\n\tlanguage = {en},\n\turldate = {2024-07-15},\n}\n\n
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\n \n\n \n \n \n \n \n \n Hourly electricity consumption varies throughout the day and across seasons - U.S. Energy Information Administration (EIA).\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
\n\n\n\n \n \n \"HourlyPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{noauthor_hourly_nodate,\n\ttitle = {Hourly electricity consumption varies throughout the day and across seasons - {U}.{S}. {Energy} {Information} {Administration} ({EIA})},\n\turl = {https://www.eia.gov/todayinenergy/detail.php?id=42915},\n\tabstract = {Energy Information Administration - EIA - Official Energy Statistics from the U.S. Government},\n\turldate = {2024-07-15},\n}\n\n
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\n Energy Information Administration - EIA - Official Energy Statistics from the U.S. Government\n
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\n \n\n \n \n \n \n \n \n Modernizing the U.S. electric grid: A proposal to update transmission infrastructure for the future of electricity - Murphy - 2022 - Environmental Progress & Sustainable Energy - Wiley Online Library.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
\n\n\n\n \n \n \"ModernizingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{noauthor_modernizing_nodate,\n\ttitle = {Modernizing the {U}.{S}. electric grid: {A} proposal to update transmission infrastructure for the future of electricity - {Murphy} - 2022 - {Environmental} {Progress} \\& {Sustainable} {Energy} - {Wiley} {Online} {Library}},\n\turl = {https://aiche.onlinelibrary.wiley.com/doi/10.1002/ep.13798},\n\turldate = {2024-07-15},\n}\n\n
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\n \n\n \n \n \n \n \n \n Integrating renewable energy sources into grids \\textbar McKinsey.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
\n\n\n\n \n \n \"IntegratingPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{noauthor_integrating_nodate,\n\ttitle = {Integrating renewable energy sources into grids {\\textbar} {McKinsey}},\n\turl = {https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/how-grid-operators-can-integrate-the-coming-wave-of-renewable-energy#/},\n\turldate = {2024-07-15},\n}\n\n
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\n \n\n \n \n \n \n \n TRANSMISSION CONGESTION COSTS RISE AGAIN IN U.S. RTOS.\n \n \n \n\n\n \n Doying, R.; Goggin, M.; and Sherman, A.\n\n\n \n\n\n\n . .\n \n\n\n\n
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@article{doying_transmission_nodate,\n\ttitle = {{TRANSMISSION} {CONGESTION} {COSTS} {RISE} {AGAIN} {IN} {U}.{S}. {RTOS}},\n\tlanguage = {en},\n\tauthor = {Doying, Richard and Goggin, Michael and Sherman, Abby},\n}\n\n
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\n \n\n \n \n \n \n \n \n NREL Analysis Explores Demand-Side Impacts of a Highly Electrified Future.\n \n \n \n \n\n\n \n \n\n\n \n\n\n\n \n \n\n\n\n
\n\n\n\n \n \n \"NRELPaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{noauthor_nrel_nodate,\n\ttitle = {{NREL} {Analysis} {Explores} {Demand}-{Side} {Impacts} of a {Highly} {Electrified} {Future}},\n\turl = {https://www.nrel.gov/news/program/2018/analysis-demand-side-electrification-futures.html},\n\tabstract = {The second report in the Electrification Futures Study series presents scenarios reflecting a wide range of possible electricity demand growth through 2050 driven by the adoption of end-use electric technologies.},\n\tlanguage = {en},\n\turldate = {2024-07-15},\n}\n\n
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\n The second report in the Electrification Futures Study series presents scenarios reflecting a wide range of possible electricity demand growth through 2050 driven by the adoption of end-use electric technologies.\n
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\n \n\n \n \n \n \n \n \n Justice40 Initiative.\n \n \n \n \n\n\n \n of Energy, D.\n\n\n \n\n\n\n \n \n\n\n\n
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@misc{department_of_energy_justice40_nodate,\n\ttitle = {Justice40 {Initiative}},\n\turl = {https://www.energy.gov/justice/justice40-initiative},\n\tabstract = {On January 27, 2021, President Biden issued Executive Order 14008, Tackling the Climate Crisis at Home and Abroad. Section 223 of that EO establishes the Justice40 Initiative, which directs 40\\% of the overall benefits of certain...},\n\tlanguage = {en},\n\turldate = {2024-02-01},\n\tjournal = {Energy.gov},\n\tauthor = {Department of Energy},\n}\n\n
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\n On January 27, 2021, President Biden issued Executive Order 14008, Tackling the Climate Crisis at Home and Abroad. Section 223 of that EO establishes the Justice40 Initiative, which directs 40% of the overall benefits of certain...\n
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