University of Minnesota Center for Transportation Studies
Much research has been conducted in the development, implementation, and evaluation of innovative ITS technologies aiming to improve traffic operations and driving safety. Existing micro-simulation modeling only describes normative car-following behaviors devoid of weakness and risks associated with real-life everyday driving. This research aims to develop a new behavioral car-following model that is pertinent to the true nature of everyday human driving. Unlike traditional car-following models that deliberately prohibit vehicle collisions, this new model builds upon multi-disciplinary findings explicitly taking into account perceptual thresholds, judgment errors, anisotropy of reaction times and driver inattention, in order to replicate “less-than-perfect” driving behavior with all its weakness and risks. Most importantly, all parameters of this model have direct physical meaning; this ensures vehicle collisions are replicated as a result of behavioral patterns rather than simply being numerical artifacts of the model. Meanwhile, vehicle trajectories were extracted from real-life crashes collected from a freeway section of I-94WB This is by far the first data collection efforts that aim to collect vehicle trajectories from real-life crashes to aid car-following modeling. These data were employed in this study to test, calibrate and validate the model. This new model is successful in replicating these vehicle trajectories as well as crashes.
Xin, Wuping; Hourdos, John; Michalopoulos, Panos.
Enhanced Micro-Simulation Models for Accurate Safety Assessment of Traffic Management ITS Solutions.
University of Minnesota Center for Transportation Studies.
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