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Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach

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  • Taylor, Jeffrey
  • Zhou, Xuesong
  • Rouphail, Nagui M.
  • Porter, Richard J.

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.

Suggested Citation

  • Taylor, Jeffrey & Zhou, Xuesong & Rouphail, Nagui M. & Porter, Richard J., 2015. "Method for investigating intradriver heterogeneity using vehicle trajectory data: A Dynamic Time Warping approach," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 59-80.
    • Handle: RePEc:eee:transb:v:73:y:2015:i:c:p:59-80
    DOI: 10.1016/j.trb.2014.12.009
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    1. Ahn, Soyoung & Cassidy, Michael J. & Laval, Jorge, 2004. "Verification of a simplified car-following theory," Transportation Research Part B: Methodological, Elsevier, vol. 38(5), pages 431-440, June.
    2. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    3. Zheng, Zuduo & Ahn, Soyoung & Chen, Danjue & Laval, Jorge, 2011. "Freeway traffic oscillations: Microscopic analysis of formations and propagations using Wavelet Transform," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1378-1388.
    4. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part II: Queueing at freeway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 289-303, August.
    5. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part I: General theory," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 281-287, August.
    6. Chen, Danjue & Laval, Jorge & Zheng, Zuduo & Ahn, Soyoung, 2012. "A behavioral car-following model that captures traffic oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 46(6), pages 744-761.
    7. Chiabaut, Nicolas & Leclercq, Ludovic & Buisson, Christine, 2010. "From heterogeneous drivers to macroscopic patterns in congestion," Transportation Research Part B: Methodological, Elsevier, vol. 44(2), pages 299-308, February.
    8. Newell, G. F., 1993. "A simplified theory of kinematic waves in highway traffic, part III: Multi-destination flows," Transportation Research Part B: Methodological, Elsevier, vol. 27(4), pages 305-313, August.
    9. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
    10. Wagner, Peter, 2012. "Analyzing fluctuations in car-following," Transportation Research Part B: Methodological, Elsevier, vol. 46(10), pages 1384-1392.
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    Cited by:

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    4. Liang Chen & Jiming Xie & Simin Wu & Fengxiang Guo & Zheng Chen & Wenqi Tan, 2021. "Validation of Vehicle Driving Simulator from Perspective of Velocity and Trajectory Based Driving Behavior under Curve Conditions," Energies, MDPI, vol. 14(24), pages 1-23, December.
    5. Rongjun Cheng & Qinyin Li & Fuzhou Chen & Baobin Miao, 2024. "A Dual-Stage Attention-Based Vehicle Speed Prediction Model Considering Driver Heterogeneity with Fuel Consumption and Emissions Analysis," Sustainability, MDPI, vol. 16(4), pages 1-24, February.
    6. Li, Qinyin & Cheng, Rongjun & Ge, Hongxia, 2023. "Short-term vehicle speed prediction based on BiLSTM-GRU model considering driver heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    7. Nishi, Ryosuke & Watanabe, Takashi, 2022. "System-size dependence of a jam-absorption driving strategy to remove traffic jam caused by a sag under the presence of traffic instability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    8. Wei, Yuguang & Avcı, Cafer & Liu, Jiangtao & Belezamo, Baloka & Aydın, Nizamettin & Li, Pengfei(Taylor) & Zhou, Xuesong, 2017. "Dynamic programming-based multi-vehicle longitudinal trajectory optimization with simplified car following models," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 102-129.
    9. Ruru Xing & Yihan Zhang & Xiaoyu Cai & Jupeng Lu & Bo Peng & Tao Yang, 2023. "Vehicle-Trajectory Prediction Method for an Extra-Long Tunnel Based on Section Traffic Data," Sustainability, MDPI, vol. 15(8), pages 1-30, April.

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