@TechReport{Heidemann05d,
	author = 	"John Heidemann and Fabio Silva and Xi Wang
 and Genevieve Giuliano and Mengzhao Hu",
	title = 	"SURE-SE: Sensors for Unplanned Roadway
                         Events---Simulation and Evaluation: Final Report",
	institution = 	"USC/Information Sciences Institute",
	year = 		2005,
	 sortdate = 		"2005-07-01",
	project = "ilense, surese",
	jsubject = "sensornet_fusion",
	type =		"METRANS Project 05-08 final report",
	month =		may,
	note =		"finalized July 2007; typographic corrections April 2008",
	location =	"johnh: pafile",
	keywords =	"sure-se final report, sensors, vehicle classification",
	url =		"http://www.isi.edu/%7ejohnh/PAPERS/Heidemann05d.html",
	pdfurl =		"http://www.isi.edu/%7ejohnh/PAPERS/Heidemann05d.pdf",
	myorganization =	"USC/Information Sciences Institute",
	abstract = "
The purpose of this research was to demonstrate the feasibility of
using sensor networks in traffic monitoring applications, specifically
a rapidly deployable network of traffic sensors (NOTS) for short-term
monitoring and data collection.  A sensor network is an array of
sensors attached to small computer nodes that have communications
capabilities via wireless network. Our application problem is heavy
duty truck data:  vehicle classification and reidentification,
particularly under slow or varying speed conditions.  An experimental
sensor, the IST Blade sensor, is essentially a portable inductive loop
sensor that provides high resolution data. We used the Blade sensor
for our initial experiments. We conducted a field experiment on the
USC campus in order to collect data for development of classification
algorithms.  Our results are encouraging; classification accuracy is
comparable to that of other recent research efforts. Once we have
developed acceptable classification algorithms, two directions are
apparent for future research: use of multiple sensors with the goal of
improving classification results, and the use of vehicle signatures to
allow re-identification of vehicles across multiple sensors.
",
}

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A sensor network is an array of sensors attached to small computer nodes that have communications capabilities via wireless network. Our application problem is heavy duty truck data: vehicle classification and reidentification, particularly under slow or varying speed conditions. An experimental sensor, the IST Blade sensor, is essentially a portable inductive loop sensor that provides high resolution data. We used the Blade sensor for our initial experiments. We conducted a field experiment on the USC campus in order to collect data for development of classification algorithms. Our results are encouraging; classification accuracy is comparable to that of other recent research efforts. Once we have developed acceptable classification algorithms, two directions are apparent for future research: use of multiple sensors with the goal of improving classification results, and the use of vehicle signatures to allow re-identification of vehicles across multiple sensors. ","bibtex":"@TechReport{Heidemann05d,\n\tauthor = \t\"John Heidemann and Fabio Silva and Xi Wang\n and Genevieve Giuliano and Mengzhao Hu\",\n\ttitle = \t\"SURE-SE: Sensors for Unplanned Roadway\n Events---Simulation and Evaluation: Final Report\",\n\tinstitution = \t\"USC/Information Sciences Institute\",\n\tyear = \t\t2005,\n\t sortdate = \t\t\"2005-07-01\",\n\tproject = \"ilense, surese\",\n\tjsubject = \"sensornet_fusion\",\n\ttype =\t\t\"METRANS Project 05-08 final report\",\n\tmonth =\t\tmay,\n\tnote =\t\t\"finalized July 2007; typographic corrections April 2008\",\n\tlocation =\t\"johnh: pafile\",\n\tkeywords =\t\"sure-se final report, sensors, vehicle classification\",\n\turl =\t\t\"http://www.isi.edu/%7ejohnh/PAPERS/Heidemann05d.html\",\n\tpdfurl =\t\t\"http://www.isi.edu/%7ejohnh/PAPERS/Heidemann05d.pdf\",\n\tmyorganization =\t\"USC/Information Sciences Institute\",\n\tabstract = \"\nThe purpose of this research was to demonstrate the feasibility of\nusing sensor networks in traffic monitoring applications, specifically\na rapidly deployable network of traffic sensors (NOTS) for short-term\nmonitoring and data collection. A sensor network is an array of\nsensors attached to small computer nodes that have communications\ncapabilities via wireless network. Our application problem is heavy\nduty truck data: vehicle classification and reidentification,\nparticularly under slow or varying speed conditions. An experimental\nsensor, the IST Blade sensor, is essentially a portable inductive loop\nsensor that provides high resolution data. We used the Blade sensor\nfor our initial experiments. We conducted a field experiment on the\nUSC campus in order to collect data for development of classification\nalgorithms. Our results are encouraging; classification accuracy is\ncomparable to that of other recent research efforts. Once we have\ndeveloped acceptable classification algorithms, two directions are\napparent for future research: use of multiple sensors with the goal of\nimproving classification results, and the use of vehicle signatures to\nallow re-identification of vehicles across multiple sensors.\n\",\n}\n\n","author_short":["Heidemann, J.","Silva, F.","Wang, X.","Giuliano, G.","Hu, M."],"bibbaseid":"heidemann-silva-wang-giuliano-hu-suresesensorsforunplannedroadwayeventssimulationandevaluationfinalreport-2005","role":"author","urls":{"Paper":"http://www.isi.edu/%7ejohnh/PAPERS/Heidemann05d.html"},"keyword":["sure-se final report","sensors","vehicle classification"],"metadata":{"authorlinks":{}}},"bibtype":"techreport","biburl":"https://bibbase.org/f/dHevizJoWEhWowz8q/johnh-2023-2.bib","dataSources":["YLyu3mj3xsBeoqiHK","fLZcDgNSoSuatv6aX","fxEParwu2ZfurScPY","7nuQvtHTqKrLmgu99"],"keywords":["sure-se final report","sensors","vehicle classification"],"search_terms":["sure","sensors","unplanned","roadway","events","simulation","evaluation","final","report","heidemann","silva","wang","giuliano","hu"],"title":"SURE-SE: Sensors for Unplanned Roadway Events—Simulation and Evaluation: Final Report","year":2005}