Browsing by Subject "Freeway traffic"
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Item Accident Prevention Based on Automatic Detection of Accident Prone Traffic Conditions: Phase I(University of Minnesota Center for Transportation Studies, 2008-09) Hourdos, John; Garg, Vishnu; Michalopoulos, PanosGrowing concern over traffic safety as well as rising congestion costs have been recently redirecting research effort from the traditional crash detection and clearance reactive traffic management towards online, proactive crash prevention solutions. In this project such a solution, specifically for high crash areas, is explored by identifying the most relevant real time traffic metrics and incorporating them in a crash likelihood estimation model. Unlike earlier attempts, this one is based on a unique detection and surveillance infrastructure deployed on the freeway section experiencing the highest crash rate in the state of Minnesota. This state-of-the-art infrastructure allowed video recording of 110 live crashes, crash related traffic events, as well as contributing factors while simultaneously measuring traffic variables such as individual vehicle speeds and headways over each lane in several places inside the study area. This crash rich database was combined with visual observations and analyzed extensively to identify the most relevant real-time traffic measurements for detecting crash prone conditions and develop an online crash prone conditions model. This model successfully established a relationship between fast evolving real time traffic conditions and the likelihood of a crash. Testing was performed in real time during 10 days not previously used in the model development, under varying weather and traffic conditions.Item Enhanced Micro-Simulation Models for Accurate Safety Assessment of Traffic Management ITS Solutions(University of Minnesota Center for Transportation Studies, 2008-11) Xin, Wuping; Hourdos, John; Michalopoulos, PanosMuch 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.