TY - JOUR
T1 - Method for investigating intradriver heterogeneity using vehicle trajectory data
T2 - A Dynamic Time Warping approach
AU - Taylor, Jeffrey
AU - Zhou, Xuesong
AU - Rouphail, Nagui M.
AU - Porter, Richard J.
N1 - Publisher Copyright:
© 2015 Elsevier Ltd.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - 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.
AB - 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.
KW - Car-following model
KW - Driver behavior heterogeneity
KW - Dynamic Time Warping
KW - Vehicle trajectory data
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U2 - 10.1016/j.trb.2014.12.009
DO - 10.1016/j.trb.2014.12.009
M3 - Article
AN - SCOPUS:84921308387
SN - 0191-2615
VL - 73
SP - 59
EP - 80
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -