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刘志远:Distributed computing approaches for large-scale traffic assignment problem

发布时间:2020-12-15  

报告时间20201218日(周五)下午2:00-3:30

报告地点屯溪路校区三立苑324

报告人刘志远 教授

工作单位东南大学交通学院
举办单位宝马娱乐

个人简介

刘志远,东南大学交通学院教授、博导、副院长,复杂交通网络研究中心主任,东南大学网络空间安全学院博导。入选国家自科基金优青、江苏省“青年双创英才”、东南大学“青年首席教授”,获评东南大学“五四青年奖章”。2011年毕业于新加坡国立大学,获博士学位,并随后留校进行博士后研究一年。自2015年回到东南大学交通学院工作。归国前就职于澳大利亚蒙纳士大学土木工程系,任讲师、博导。201712月至20181月,澳大利亚墨尔本大学数学系访问学者。主要研究领域包括交通网络规划与管理、交通大数据分析与建模、公共交通、多模式物流网络等。迄今为止在这些领域中发表学术论文百余篇,其中被SCI/SSCI期刊检索70余篇,论文被引用2000余次。担任交通研究领域知名SCI期刊ASCE Journal of Transportation Engineering以及IET Intelligent Transport Systems副主编,担任国际期刊Transportation Research Part ESCI/SSCI)、Transportation Research RecordSCI)、Journal of Transport and Land UseSSCI)编委。指导学生获得多项国内外大数据算法比赛奖项(皆为前三名),包括被誉为“大数据比赛世界杯”的KDD CUP冠军(2020年)、与第二名(2019)年,及其他同为人工智能三大国际顶级赛事的IJCAI(冠军,2019年)。此外还包括,2016年阿里巴巴天池大赛算法挑战赛冠军、2016年首届滴滴算法大赛-亚军、2017年美国TRB大会数据分析比赛优秀论文奖、2017CCF大数据与计算智能大赛亚军、2017Ucar Artificial Intelligence Cup冠军(IEEE computer society)2018年阿里巴巴天池大赛冠军、2019年数字中国创新大赛大数据比赛一等奖等。

报告简介

  Traffic assignment is a fundamental tool to evaluate the flow distribution pattern in a transport network. As one of the most recognized theory for traffic assignment, user equilibrium is widely investigated and implemented. Most of the existing algorithms for the user equilibrium-based traffic assignment problem are developed and implemented sequentially. This study aims to study and investigate the parallel computing approach to utilize the widely available parallel computing resources. The Parallel Block-Coordinate Descent (PBCD) algorithm is developed based on the path-based algorithm, i.e, the improved path-based gradient projection algorithm (iGP). A parallel block-coordinate method is proposed to replace the widely used Gauss-Seidel method for the procedure of path flow adjustment. To further improve the robustness/performance of the algorithm, the iPBCD algorithm is developed based on the state-of-the-art PBCD algorithm. A different type of flow update policy is studied and investigated intensively. The block size is determined using a sensitive test, and five indices-grouping rules are compared. Besides, a greedy update order of block indexes is introduced to compare with the cyclic scheme. Moreover, a new algorithm is developed based on the alternating direction method of multipliers (ADMM). In order to take use of the ADMM, the network links should be grouped into several blocks, where the links in the same block are disconnected. This link grouping problem falls into the category of edge-coloring problem in graph theory, and it follows the Vizing theorem. Numerical examples show that the proposed algorithms perform well in convergence and efficiency and can significantly reduce the computing time.