主讲人：Mohamed Abdel-Aty

邀请人：王雪松教授

时间：2024年11月5日（周二）下午15:00-16:30

地点：通达馆103会议室

主讲人简介：

Dr. Mohamed Abdel-Aty, PE is a Trustee Endowed Chair at the University of Central Florida (UCF). He is a Pegasus Distinguished Professor and former Chair of the Civil, Environmental and Construction Engineering Department at UCF. His main expertise and interests are in the areas of traffic safety, computer vision, AI, transportation technology, simulation, big data and data analytics, ITS and CAV. He has published more than 900 papers, 470 in journals (Google Scholar citations 35,000 H-Index 104). Dr. Abdel-Aty is the Editor Emeritus of Accident Analysis and Prevention, the premier journal in safety. Dr. Aty has received the 2020 Roy W. Crum Distinguished Service Award from the Transportation Research Board of the National Academies, National Safety Council’s Distinguished Service to Safety Award for his “outstanding leadership in the field of road safety nationally and internationally”. He has also received the 2019 Transportation Safety Council Edmund R. Ricker Award, Institute of Transportation Engineering (ITE) and the Lifetime Achievement Safety Award and S.S. Steinberg Award from ARTBA in 2019 and 2022, respectively.

主讲内容简介：

Traffic safety researchers usually deal with highly aggregated data when analyzing traffic crashes. Nevertheless, a high level of aggregation may lead to a failure of capturing the temporal variation in traffic volume, speed, weather, crashes, other factors, and their relationship. Hence, this presentation aims to discuss the explanatory variables measured over short-durations and include more precise measures of exposure rather than AADT, and utilize factors such as speed and speed variability to predict crashes for different time periods. The research team developed short-term crash prediction models for different time periods and crash severity levels to estimate the safety performance functions (SPFs) of freeways considering operational and exposure characteristics. High resolution traffic data were utilized to provide detailed microscopic flow information (i.e., data aggregated at a small-time interval (such as 20-60 seconds)) from multiple sources (e.g., Microwave Vehicle Detection Systems (MVDS) and Loop Detectors (LD)). The team developed SPFs for all mainline freeway segment types (i.e., basic, weaving, merge, diverge and ramps) and different ATM scenarios. In conclusion, the results of this study indicated the advantages of using short-term temporal SPFs. The dynamic hotspots identification using the developed short-term SPFs could enable practitioners and policymakers to better understand the temporal variation in crash risk assessment and to provide effective countermeasures.

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