Extraction and analysis of microscopic traffic data in disordered heterogeneous traffic conditions

Abstract

ABSTRACT This paper discusses the development of a novel off-line vision-based system to obtain the naturalistic trajectory database of traffic streams from recorded video footages. The developed system uses a semi-automatic mechanism that provides manual interference for vehicle identification and classification and executes automated tracking of the identified vehicles. A trajectory database of typical disordered heterogeneous traffic stream was collected to evaluate the performance of the developed system. Results show that the developed system significantly enhances the process of trajectory data collection in such traffic conditions. The collected trajectory database is then used to investigate two crucial aspects that characterize the disordered heterogeneous traffic (i) interaction of different types of vehicles in the longitudinal and staggered following scenario, and (ii) lateral shift propensity of different types of vehicles. The analysis emphasizes the behavioral difference between different types of vehicles, and utility of the developed system to address the prevailing research gaps.