主讲人：席好宁 助理教授

邀请人：谢驰 教授

时间：2023年11月21日（周二）下午15:30

地点：通达馆103会议室

主讲人简介：

席好宁博士在澳大利亚纽卡斯尔大学（University of Newcastle）商学院担任讲师（助理教授）。在获得长聘教轨职位之前，她曾在悉尼大学商学院的交通物流研究所（ITLS）担任研究员。席好宁在澳大利亚新南威尔士大学（UNSW）土木与环境工程学院获得博士学位（2022）。在博士学习期间，席好宁是澳大利亚联邦科学与工业研究组织（CSIRO）Data61的联合培养生，并获得“UNSW国际研究生奖学金”和“CSIRO Data 61 顶尖博士奖学金”两项奖学金；博士毕业后被授予澳大利亚“全球人才独立”称号。在博士学习之前，席好宁从清华大学获得硕士学位（2019），在美国加州大学伯克利分校和香港科技大学担任研究助理（2018）。席好宁有交叉学科的背景，跨交通工程、交通经济、运筹学、统计分析和数据挖掘等专业; 主要研究方向包括：出行即服务（MaaS）、出行行为研究、公共交通系统、及可持续发展交通系统等。她的主要研究成果发表在European Journal of Operational Research, Transportation Research Part B: Methodological, Transport Reviews, Transport Policy, Computer-Aided Civil and Infrastructure Engineering 等期刊。席好宁主导并参与多个基金项目，她近期的研究得到了澳大利亚新南威尔士州（New South Wales）交通部和昆士兰州（Queensland）交通与道路部门的支持。

主讲内容简介：

Mobility-as-a-Service (MaaS) has recently received significant attention from researchers, industry stakeholders, and the public sector. In the context of Everything-as-a-Service (XaaS), the transportation sector has been evolving towards user-centric business models in which customized services and mode-agnostic mobility resources are priced in a unified framework. Yet, in the vast majority of studies on MaaS systems, mobility resource pricing is based on segmented travel modes, e.g., private vehicle, public transit, and shared mobility services. This thesis attempts to address this research gap by introducing innovative MaaS mechanisms and optimization methods for mobility resource allocation, pricing strategies, and demand management in MaaS systems. This research proposes a unified framework and various tractable optimization methodologies for the innovative MaaS paradigm, exploits the potentialities of MaaS systems to evaluate futuristic transport scenarios, and provides meaningful managerial insights for the regulation of MaaS systems under government-contracting and economic deregulation. There has been a significant increase in cooperation and coordination between the MaaS systems and their stakeholders due to the technological advancements. This will render the proposed models and algorithms essential tools for future MaaS systems.

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