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\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
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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 National Transmission Needs Study.\n \n \n \n\n\n \n \n\n\n \n\n\n\n . 2023.\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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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 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
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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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