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465-Smart Mobility Management in Urban Railway Systems
  发表时间:2021-05-25    阅读次数:
第465期同路人学术论坛

Smart Mobility Management in Urban Railway Systems

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主讲人:马振良 助理教授/博导
邀请人:沈煜 副教授
时间:2021年5月31日(周一)13:30-15:30
地点:交通运输工程学院103会议室
主讲人简介:
         Zhenliang (Mike) Ma is an Assistant Professor at Monash University and Director of Master of Transportation Program at Monash-Suzhou. He is a TRB committer member in AP090 Transit Data and AEP060 Travel Demand Management.
         Dr. Ma completed his Ph.D. at the University of Queensland, Australia, and was a Research Associate at Northeastern University and MIT Transit Lab. In this role, he co-managed and led the research partnership program with Mass Transit Railway Corporation Limited Hong Kong.
         Dr Ma has an education background in information technology, computer science, and transportation. His main research focuses are inference, prediction, and optimization, via the integration of novel data sources into mathematical learning models. Applications include traffic control, public transport, shared mobility, and mobility management.

主讲内容简介:
          Mobility management, aiming for better utilization of available capacity through influencing customers’ behavior, is a promising alternative to deal with the challenging issue of urban congestion. The talk will synthesize the mobility management strategies and technologies in traffic and public transport. It presents the main steps of a general public transport demand management (PTDM) framework and discusses novel methodologies using smart fare card data for problem identification, design, evaluation, and monitoring. Case study findings are reported for a pre-peak incentive program in Hong Kong, including users’ behavioral response over time, the effectiveness of design structures, and the implication of users’ behavior on optimal promotion design. Better targeting users, places, times are critical for effective PTDM programs. The individualized incentives, enabled by smartphones and data analytics, can be cost-efficiently in improving PTDM effectiveness.
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