主题：On the Fundamental Diagram for Freeway Traffic

主讲人：Dr. Xiaobo Qu

邀请方联络人：杨超 教授

时间：2015年12月17日（周四）15:00-17:00

地点：交通运输工程学院102会议室

主讲人简介：

Dr. Xiaobo Qu is a Senior Lecturer in Griffith School of Engineering, Griffith University, Gold Coast, Australia. He received his Bachelor of Engineering in transport engineering from Jilin University, Master of Science in industrial engineering from Tsinghua University, and PhD from National University of Singapore. His research is focused on traffic flow theory and its applications. He also works on public transport, maritime transportation, and infrastructure resilience. In the past five years, Dr Qu has published over 40 journal articles at leading journals in Transportation Engineering such as Transportation Research Part B, Part A, Part C, Part E, Accident Analysis and Prevention, Journal of Transportation Engineering - ASCE, Risk Analysis, IEEE Transactions. He is currently supervising seven PhD students, and is a chief investigator for research funding well over AUD 500,000. He is a recipient of Griffith Sciences Pro Vice Chancellor Early Career Research Excellence Award in 2015, Griffith Sciences Pro Vice Chancellor Excellent Research Team Award in 2015, the best paper award in EPPM 2015, the Griffith Sciences Learning & Teaching Citation award in 2015, the Griffith Sciences Learning & Teaching commendation award in 2014, ASCE Journal of Transportation Engineering Outstanding Reviewer award in 2013, and Ministry of Transport Minister’s Innovation award in 2010.

主讲内容简介

      The speed-density or flow-density relationship has been considered as the foundation of traffic flow theory. Existing single-regime models calibrated by the least square method (LSM) could not fit the empirical data consistently well both in light-traffic/free-flow conditions and congested/jam conditions. In this paper, first, we point out that the inaccuracy of single-regime models is not caused solely by their functional forms, but also by the sample selection bias. Second, we apply a weighted least square method (WLSM) that addresses the sample selection bias problem. The calibration results for six well-known single-regime models using the WLSM fit the empirical data reasonably well both in light-traffic/free-flow conditions and congested/jam conditions. Third, we conduct a theoretical investigation that reveals the deficiency associated with the LSM is because the expected value of speed (or a function of it) is nonlinear with regard to the density (or a function of it).

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