A panel data-based discrete-continuous modelling framework to analyze longitudinal driver behavior in homogeneous and heterogeneous disordered traffic conditions

Abstract

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