Vehicle Class, Speed, and Roadway Geometry Based Driver Behavior Identification and Classification
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Over the past decades, the intense emphasis has been placed on the understanding of car-following behavior and the factors that affect it. The car-following process, however, still remains a very complex field of study in spite of all the efforts. This paper focuses on the study of the effect that the class of the vehicle, leading heavy vehicles in particular, causes on the following vehicle behavior, specifically in terms of the following bumper-to-bumper distance (gap) that the following vehicle keeps from the lead vehicle. This was done by extracting and analyzing different car following episodes in the Next Generation Simulation (NGSIM) dataset for Interstate 80 (I 80) in Emeryville, California, USA. The results of the statistical analysis are compared to that of the synthesized literature of research efforts that have been carried out on the topic, then are further assessed utilizing different calibrated behavioral clusters for the Gazis-Herman-Rothery (GHR) car-following model to address the similarities and differences in car-following behavior between drivers of the same vehicle class. The paper also validates the results of the statistical analysis and highlights possible future implementations.
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