基于数据和信息融合的城市交通精细化建模

讲座:基于数据和信息融合的城市交通精细化建模( Fine-Grained Modeling of Arterial Traffic: A Data Fusion and Information Integration Approach)

主讲人:孙湛博

时间:2016-12-21 15:00

地点:交通运输学院207室

讲座摘要:

It is a longstanding goal for transportation researchers to model and understand how traffic distribute and propagate along road networks. In this research, a data fusion and information integration approach is proposed to model and interpret traffic along urban arterial corridors based on heterogeneous traffic data. The method attempts to deduce the “most probable” explanation that can integrate fixed-location data and mobile sensing data and match the vehicle records at upstream with those collected at downstream sensor locations. To make the probabilistic model more realistic, traffic knowledge such as lane choice decision, traffic merging and travel time information are calibrated using the historical dataset and then integrated into the model. By doing so, the model can obtain individual travel times of the matched data pairs, which can be directly used to estimate corridor travel times of individual vehicles. Results from the method can be further applied to estimate vehicle trajectories along arterial corridors, estimate individual vehicle-based fuel consumption/emissions, and help infer real-time queuing processes at signalized intersections.

主讲人简介:

孙湛博博士目前在西南交通大学交通运输与物流学院任教授。他于2009年在清华大学土木工程与建设管理系获得学士学位。2014年在美国伦斯勒理工大学(Rensselaer Polytechnic Institute, RPI)土木与环境工程系获得博士学位。同年8月进入西密歇根大学 (Western Michigan University) 土木与建设工程系任助理教授。孙博士的研究主要集中在基于车联网、自动驾驶和移动交通检测的城市交通系统建模与优化控制,以及智能交通系统。他的研究也涉及了交通与隐私保护,车辆尾气排放及浓度测算,和宜居城市等内容。他最近的研究是基于数据和信息融合的精细化城市交通建模。该研究能够用于提高交通出行时间估算精度,实时交通信号评价与优化,以及考虑到时间和空间演变的交通尾气排放与浓度的精细化评价。

迄今为止,孙博士在国际交通期刊和会议上发表论文30余篇,其中7篇发表在高水平交通期刊Transportation Research Part B, Transportation Research Part C,和IEEE Transactions on Intelligent Transportation Systems上。孙博士是Transportation Research Part C,IEEE Transactions on ITS等多个国际期刊的审稿人。他是美国交通研究委员会 (Transportation Research Board) 下属国际合作(International Cooperation)常委会的年轻委员。