Citation

Nirmale SK, Pinjari AR, Sharma A. Transp. Lett. 2023; 15(9): 1100-1113.

Copyright

(Copyright © 2023, Maney Publishing, Publisher Informa - Taylor and Francis Group)

DOI

10.1080/19427867.2022.2132058

PMID

unavailable

Abstract

We propose a panel data-based discrete-continuous modeling framework to analyze driver behavior in two disparate trajectory datasets - one from a heterogeneous disorderly (HD) traffic stream in India and another from a homogeneous traffic stream in the United States. The panel data-based framework allows the analyst to isolate the subject vehicle- and driver-specific unobserved factors that influence driver behavior. Doing so helps reduce the confounding effects of such unobserved factors on analyzing the influence of observed factors, such as relative speeds and spacing between the subject vehicle and other vehicles, on driver behavior. The empirical results reveal both similarities and differences in driver behavior between the two trajectory datasets. In addition, the analysis sheds light on the suitability of different lengths of influence zones on driver behavior in the two datasets. The insights from this study can help improve driver behavior models and traffic simulation frameworks for both traffic conditions..

Language: en

Keywords

Driver behavior; heterogeneous disorderly traffic conditions; homogeneous traffic conditions; multi-vehicle anticipation; panel data models