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
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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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