题目：交通流基本图理论以及自动驾驶环境下车辆轨迹控制方法
The Fundamental Diagram for Freeway Traffic and the Trajectory Control of Connected and Automated Vehicles
时间：2018年5月18日（星期五）上午9:30-11:30
地点：交通大楼604室
报告人：曲小波教授（瑞典查尔莫斯理工大学）
欢迎广大师生参加
土木与交通学院
2018年5月15日
报告人简介：
曲小波博士，男，1983年，现任瑞典查尔莫斯理工大学建筑与土木工程学院正教授，以及城市交通研究中心主任。他分别于新加坡国立大学、清华大学和吉林大学获得博士、硕士及学士学位。研究方向主要为交通流理论及利用交通建模提高交通设施的运行效率。曲教授在交通行业顶级期刊Transportation Research Part A, Part B, Part C, Part E, European Journal of Operational Research等发表论文六十余篇，获得澳大利亚基金委，国家研究中心，昆士兰交通厅，新州交通厅等资助150余万澳元。现任职世界交通大会交通建模委员会主席，并担任四个著名期刊副主编，包括IEEE Transactions on Cybernetics (IF = 7.384)，ASCE Journal of Transportation Engineering (IF = 0.962)，及IEEE Intelligent Transportation Systems (IF = 3.654)。迄今，曲教授作为主导师或者唯一导师已经培养了五位博士生毕业，另有三名博士生在读。
报告摘要：
In this research, we apply a new calibration approach to generate stochastic traffic flow fundamental diagrams. We first prove that the percentile based fundamental diagrams are obtainable based on the proposed model. We further prove the proposed model has continuity, differentiability and convexity properties so that it can be easily solved by Gauss-Newton method. By selecting different percentile values from 0 to 1, the speed distributions at any given densities can be derived. The model has been validated based on the GA400 data and the calibrated speed distributions perfectly fit the speed-density data. This proposed methodology has wide applications. First, new approaches can be proposed to evaluate the performance of calibrated fundamental diagrams by taking into account not only the residual but also ability to reflect the stochasticity of samples. Secondly, stochastic fundamental diagrams can be used to develop and evaluate traffic control strategies. In particular, the proposed stochastic fundamental diagram is applicable to model and optimize the connected and automated vehicles at the macroscopic level with an objective to reduce the stochasticity of traffic flow. Last but not the least, this proposed methodology can be applied to generate the stochastic models for most regression models with scattered samples.
