Data-enabled smart transportation has attracted a surge of interest from machine learning and data mining researchers nowadays due to the bloom of online ride-hailing industry and rapid development of autonomous driving. Large-scale high quality route data and trading data (spatiotemporal data) have been generated every day, which makes AI an urgent need and preferred solution for the decision making in intelligent transportation systems. While a large of amount of work have been dedicated to traditional transportation problems, they are far from satisfactory for the rising need. In this tutorial, we systematically introduce the decision making (core planning) process in modern mobile transportation platform and the key emerging challenges in this process. We survey existing work on these challenges, focusing on the recent progresses in AI and data-driven solutions. Furthermore, we release a large-scale transportation benchmark and a related research platform for researchers who are interested in transportation AI. We aim to provide a general overview of the key problems, common formulations, existing methodologies and future directions. This tutorial will inspire the audience and facilitate transportation AI research.
Part I: Challenges and opportunities in transportation AI
Part II: AI applications in transportation
Part III: Data and tools for transportation AI
Dr. Zheng Wang is a researcher in Didi AI Labs and the architecture of the intelligent map service and the architecture of the precision prediction system for Didi Chuxing. He received his Ph.D. degree from Tsinghua University and worked as a research faculty in the University of Michigan at Ann Arbor before joining DiDi. He has received several awards, including best research paper award runner-up in KDD and best paper award in IEEE International Conference in Social Computing (SocialCom). He served as the PC member of leading conferences, such as ICML, NIPS, ICLR, AISTATS, SDM and IJCAI. He is now leading the R&D team, working on designing and developing novel machine learning systems and services for DiDi map and DiDi capacity prediction platform. He designed the novel machine learning and deep learning solutions of DiDi ETA and route planning services, which serve over 40 billions of requests per day.
Dr. Yan Liu is chief scientist in time series in Didi AI Labs. She is also an associate professor in Computer Science Department at University of Southern California from 2010. She was a Research Staff Member at IBM Research in 2006-2010. She received her Ph.D. degree from Carnegie Mellon University in 2006. Her research interest is data mining and machine learning with applications to social media, biology and climate science. She has received several awards, including NSF CAREER Award, Okawa Foundation Research Award, ACM Dissertation Award Honorable Mention, Best Paper Award in SIAM Data Mining Conference, Yahoo! Faculty Award and the winner of several data mining competitions, such as KDD Cup and INFORMS data mining competition. She has published over 10 referred articles on temporal causal models for time series data in top conferences, such as KDD, ICML, ICDM, SDM and AAAI, and given invited talks on the topic in many institutions and industrial research labs.
Dr. Jieping Ye is head of Didi AI Labs, a VP of Didi Chuxing and a Didi Fellow. He is also an associate professor of University of Michigan, Ann Arbor. His research interests include big data, machine learning, and data mining with applications in transportation and biomedicine. He has served as a Senior Program Committee/Area Chair/Program Committee Vice Chair of many conferences including NIPS, ICML, KDD, IJCAI, ICDM, and SDM. He serves as an Associate Editor of Data Mining and Knowledge Discovery, IEEE Transactions on Knowledge and Data Engineering, and IEEE Transactions on Pattern Analysis and Machine Intelligence. He won the NSF CAREER Award in 2010. His papers have been selected for the outstanding student paper at ICML in 2004, the KDD best research paper runner up in 2013, and the KDD best student paper award in 2014.