Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach |
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| Institution: | 1. Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, UT 84112-0561, USA;2. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85287, USA;3. Institute for Transportation Research and Education (ITRE), Civil Engineering, North Carolina State University, Centennial Campus, Box 8601, Raleigh, NC 27695-8601, USA;1. Université de Lyon, Lyon, France;2. IFSTTAR, COSYS-LICIT, Bron, France;3. ENTPE, LICIT, Vaulx-en-Velin, France;4. Department of Transportation Engineering, Università di Napoli Federico II, Italy;5. Institute for the Energy and Transport, European Commission – Joint Research Centre, Italy;1. MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China;2. State Key Laboratory of Fire Science and School of Engineering Science, University of Science and Technology of China, Hefei 230026, China;3. Department of Civil and Environmental Engineering, University of California Davis, Davis, CA 95616, United States;4. Department of Traffic Engineering, School of Transportation Engineering, Tongji University, Shanghai 200092, China |
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| Abstract: | After first extending Newell’s car-following model to incorporate time-dependent parameters, this paper describes the Dynamic Time Warping (DTW) algorithm and its application for calibrating this microscopic simulation model by synthesizing driver trajectory data. Using the unique capabilities of the DTW algorithm, this paper attempts to examine driver heterogeneity in car-following behavior, as well as the driver’s heterogeneous situation-dependent behavior within a trip, based on the calibrated time-varying response times and critical jam spacing. The standard DTW algorithm is enhanced to address a number of estimation challenges in this specific application, and a numerical experiment is presented with vehicle trajectory data extracted from the Next Generation Simulation (NGSIM) project for demonstration purposes. The DTW algorithm is shown to be a reasonable method for processing large vehicle trajectory datasets, but requires significant data reduction to produce reasonable results when working with high resolution vehicle trajectory data. Additionally, singularities present an interesting match solution set to potentially help identify changing driver behavior; however, they must be avoided to reduce analysis complexity. |
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| Keywords: | Dynamic Time Warping Car-following model Driver behavior heterogeneity Vehicle trajectory data |
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