讲座主题：基于大规模仿真平台和人工智能的需求响应出行服务运营策略

时间：2023年7月22日 9:30~10:30

地点：大连理工大学厚兴楼（三号实验楼）405 

报告人：柯锦涛 博士，助理教授

Title: A large-scale simulation platform and artificial intelligence based operational strategies for on-demand ride services

Abstract:

On-demand ride services or ride-sourcing services, offered by transportation network companies like Uber, Lyft and Didi, have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various mathematical models and optimization algorithms, including reinforcement learning approaches, have been developed in the literature to help ride-sourcing platforms design better operational strategies to achieve higher operational efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will thus be very important for both researchers and industrial practitioners to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models/algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents (including drivers and passengers) on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. In addition, it can be used to test how well the theoretical models, developed in the literature for equilibrium analysis and strategic planning, approximate the simulated outcomes. Evaluated by experiments based on real-world datasets, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations.

在过去十年中，由Uber、Lyft和滴滴等交通网络公司提供的需求响应出行服务或网约车服务经历了快速发展，并稳步重塑了人们的出行方式。包括强化学习方法在内的各种数学模型和优化算法已在文献中得到开发，以帮助网约车平台设计更好的运营策略，实现更高的运营效率。然而，由于成本和可靠性问题（在实际运营中实施不成熟的算法可能会导致系统紊乱），在现实世界的乘车外包平台中验证这些模型和训练/测试这些优化算法通常是不可行的。因此，作为一个有用的测试平台，网约车的仿真平台对于研究人员和工业从业人员通过跟踪和错误进行算法培训/测试或模型验证都是非常重要的。尽管之前的研究已经针对各自的任务建立了多种模拟器，但缺乏一个公平、公开的平台来比较不同研究人员提出的模型/算法。此外，现有的模拟器仍然面临着许多挑战，从它们是否接近真实的网约车系统环境，到它们所能实现的不同任务的完整性。为了应对这些挑战，我们提出了一个新颖的多功能、开源的乘车网约系统仿真平台，它可以模拟真实交通网络中各方（包括司机和乘客）的行为和动作。它为用户提供了多个接口，用于训练和测试各种优化算法，特别是强化学习算法，以完成各种任务，包括按需匹配、闲置车辆重新定位和动态定价。此外，它还可用于测试文献中开发的用于平衡分析和战略规划的理论模型与模拟结果的近似程度。通过基于真实世界数据集的实验评估，该模拟器被证明是与按需乘车服务运营相关的各种任务的高效和有效的测试平台。 

Short Bio:

Dr. Jintao Ke is an Assistant Professor in the Department of Civil Engineering at the University of Hong Kong (HKU). Dr. Ke received his B.S. degree (2016) in civil engineering from Zhejiang University, and his PhD degree (2020) in Civil and Environment Engineering from Hong Kong University of Science and Technology. Prior to joining HKU, he was a research assistant professor in the Hong Kong Polytechnic University. His research interests include shared mobility on demand, transportation big data analytics, multimodal intelligent transportation systems, transportation pricing, short-term travel demand forecasting, etc. The vision of his research is to develop novel models, algorithms, and conduct data-driven quantitative analyses to better manage, operate, and regulate shared mobility and other emerging mobility services. He has published over 30 SCI/SSCI indexed research papers in top-tier journals in the field of transportation research and data mining, such as Transportation Research Part A-E, IEEE Transactions on Intelligence Transportation System, IEEE Transactions on Knowledge and Data Engineering. He was awarded the Honorable Mention of HKSTS Outstanding Dissertation Award in 2020. He serves as an Advisory Board Member of Transportation Research Part C, guest editors of two Special Issues of Transportation Research Part C and Travel Behavior and Society, and referees for a few top transportation journals.

    柯锦涛博士是香港大学土木工程系助理教授。柯博士于2016年获得浙江大学土木工程学士学位，并于2020年获得香港科技大学土木与环境工程博士学位。在加入香港大学之前，他是香港理工大学的研究助理教授。他的研究兴趣包括共享出行、交通大数据分析、多式联运智能交通系统、交通定价、短期出行需求预测等。他的研究兴趣是开发新型模型和算法，并进行数据驱动的定量分析，以更好地管理、运营和规范共享交通和其他新兴交通服务。他在交通研究和数据挖掘领域的顶级期刊上发表了30多篇SCI/SSCI收录的研究论文，如《Transportation Research Part A-E》、《IEEE Transactions on Intelligence Transportation System》、《IEEE Transactions on Knowledge and Data Engineering》等。他于2020年获得香港科技学会优秀论文荣誉奖。担任《Transportation Research Part C》顾问委员会成员，《Transportation Research Part C》和《Travel Behavior and Society》两本专刊的客座编辑，并担任多家顶级交通期刊的审稿人。