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

• Develop enhanced Dynamic Time Warping (DTW) algorithm for calibrating simplified car following model.
• Examine driver heterogeneity and driver’s heterogeneous situation-dependent behavior.
• Conduct numerical experiments with real-world high resolution vehicle trajectory data.
• Propose data reduction methods to produce reasonable results for dynamic car following model calibration.

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.