Workshop on Artificial Intelligence in Transportation
(AI in Transportation)
Held in conjunction with CIKM 2019
November, 2019 - Beijing, China

Introduction

Data-enabled smart transportation has attracted a surge of interest from machine learning and data mining communities nowadays due to the bloom of online ride-hailing industry and rapid development of intelligent driving. Large-scale high quality route data and trading data (spatiotemporal data) have been generated every day, which makes AI a natural choice for the decision making in intelligent transportation systems. While a large of amount of work has been dedicated to traditional transportation problems, they are far from satisfactory for the rising need.

We propose a half-day workshop at CIKM 2019 for the professionals, researchers, and practitioners who are interested in mining and understanding big and heterogeneous data generated in transportation, and AI applications to improve the transportation system.

Schedule

Room 303B, China National Convention Center

2:00 PM - 5:00 PM

November 7th, 2019

2:00 PM - 2:10 PM -- Opening & Welcome

2:10 PM - 2:40 PM -- Keynote Talk: Towards Real-world Decision-Making via Simulator-based Reinforcement Learning
Dr. Yang Yu, Professor, LAMDA Group, School of Artificial Intelligence, Nanjing University

2:40 PM - 3:10 PM -- Keynote Talk: Team Competition Promotes Productivity in Ride-sharing Economy
Dr. Qiaozhu Mei, Professor, School of Information, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor

3:10 PM - 3:40 PM -- Keynote Talk: Multi-agent Reinforcement Learning for Complex Optimization
Dr. Bo An, Associate Professor, School of Computer Science and Engineering, Nanyang Technological University

3:40 PM - 4:00 PM -- Coffee Break

4:00 PM - 4:45 PM -- Contributed Talks

  • CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario, Huichu Zhang, Qidong Su, Siyuan Feng, Chang Liu, Yichen Zhu, Weinan Zhang, Yong Yu, Haiming Jin
  • R2E: A Real-time Routing and Charging Recommendation System for Electric Taxi Drivers, Enshu Wang, Haiming Jin, Feng Shen, Lu Su, Wen Dong, Chunming Qiao, Fan Zhang
  • Exploiting Mobile Kinetic Data for Transportation Apps, Dongyao Chen, Kang G. Shin

4:45 PM - 5:00 PM -- Concluding Remarks

Keynotes

Yang Yu

Yang Yu

Professor
Nanjing University

Towards Real-world Decision-Making via Simulator-based Reinforcement Learning

Reinforcement learning achieved significant successes include being part of the AlphaGo system and playing Atari games. However, it is also criticized for applicability only in virtual worlds due to the requirement of huge amount of interaction data. In recent years, we have explored a path to land reinforcement learning in real-world decision making tasks, through learning effective simulators of the real-world scenarios. In this talk, we will report our progress in this direction.


Bio
Yang Yu is a Professor of School of Artificial Intelligence in Nanjing University, China. His research interest is mainly in reinforcement learning. He was recommended as "AI’s 10 to Watch" by IEEE Intelligent Systems in 2018, invited to have an Early Career Spotlight talk in IJCAI’18 on reinforcement learning, and received the Early Career Award of PAKDD in 2018.

Qiaozhu Mei

Qiaozhu Mei

Professor
University of Michigan, Ann Arbor

Team Competition Promotes Productivity in Ride-sharing Economy

The ride-sharing economy provides drivers with the benefits of autonomy and flexibility, but it does so at the expense of work identity and co-worker bonds.  These sacrifices make drivers less productive and more likely to leave.  Team competitions, practices rooted in social identity theory and contest theories, have been recognized as a potential cure for the pain. In this talk, we reveal the effect of team formation and competitions on the productivity of drivers at a leading ride-sharing platform, DiDi Chuxing.  AI-empowered recommender systems are designed to team the drivers, who then compete with other teams in short-term, financially incentivized contests as well as in long-term, leaderboard-driven competitions.  Through competing as teams, drivers are able to build team identity and social bonds with teammates, create a sense of achievement by winning a competition, and eventually increase their satisfaction and performance at work. 


Bio
Qiaozhu Mei is a professor in the School of Information and the Department of EECS at the University of Michigan. His research focuses on large-scale data mining, machine learning, information retrieval, and natural language processing, with broad applications to social media, Web, and health informatics. Qiaozhu is an ACM distinguished member (2017) and a recipient of the NSF Career Award (2011). His work has received multiple best paper awards at ICML, KDD, WWW, WSDM, and other major conferences in computing. He has served as the General Co-Chair of SIGIR 2018 and is on the editorial boards of multiple journals such as JMLR, TOIS, and TWEB. He is the founding director of the new master degree of applied data science at the University of Michigan. 

Bo An

Bo An

Associate Professor
Nanyang Technological University

Multi-agent Reinforcement Learning for Complex Optimization

For some complex domains with strategic interaction, reinforcement learning have been successfully used to learn efficient policies. This talk will discuss key techniques behind these success and their applications in domains including games, e-commerce, and urban planning.


Bio
Bo An is the President’s Council Chair Associate Professor in Computer Science and Engineering, Nanyang Technological University. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His current research interests include artificial intelligence, multiagent systems, computational game theory, reinforcement learning, and optimization. His research results have been successfully applied to many domains including infrastructure security and e-commerce. He has published over 100 referred papers at AAMAS, IJCAI, AAAI, ICAPS, KDD, WWW, JAAMAS, NeurIPS, AIJ and ACM/IEEE Transactions. Dr. An was the recipient of the 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award, an Operational Excellence Award from the Commander, First Coast Guard District of the United States, the 2012 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice, and 2018 Nanyang Research Award (Young Investigator). His publications won the Best Innovative Application Paper Award at AAMAS’12 and the Innovative Application Award at IAAI’16. He was invited to give Early Career Spotlight talk at IJCAI’17. He led the team HogRider which won the 2017 Microsoft Collaborative AI Challenge. He was named to IEEE Intelligent Systems' "AI's 10 to Watch" list for 2018. He was invited to be an Advisory Committee member of IJCAI’18. He is PC Co-Chair of AAMAS’20. He is a member of the editorial board of JAIR and the Associate Editor of JAAMAS, IEEE Intelligent Systems, and ACM TIST. He was elected to the board of directors of IFAAMAS and senior member of AAAI.

Accepted Papers

Call For Paper

In this workshop, we invite professionals, researchers and technologists of all relevant fields to present the state-of-the-art development and applications, share their envisions about the future of intelligent transportation informatics.

The topics of interests include, but not limited to, the following aspects:

  • - Spatial-temporal analysis of transportation data
  • - Fusing social media data for intelligent transportation
  • - Human and traffic trajectory mining
  • - Agent-based transportation analysis
  • - Human mobility data mining
  • - Driver/passenger behavior modeling and analysis
  • - Destination suggestion and prediction
  • - Ride sharing and bike sharing
  • - Effective dispatching and resource allocation
  • - Automated POI discovery and classification
  • - Filtering and Recommending POI
  • - ETA and route planning
  • - Transportation in smart city
  • - Intelligent traffic light control
  • - Transportation and economy
  • - Demand and supply prediction
  • - Dynamic pricing tools for demand and supply balancing
  • - Energy consumption and sustainable transportation
  • - Transportation safety modeling and intervention
  • - Emergency and crisis response
  • - Visualizing transportation information

We encourage short papers (4 pages), poster papers (2 pages) and demo proposals (2 pages). Submissions of workshop papers must be in English, in PDF format, and should not exceed the appropriate length requirements in the current ACM two-column conference format. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop. We will follow a single-blind process and submissions will be evaluated by the program committee based on the quality of the work and its fit to the workshop themes. All papers are to be submitted via EasyChair a link here .

For inquires about the workshop and submissions, please email to an email here

Important Dates

June 1, 2019: all invited keynotes and research talks are confirmed.

September 5, 2019: Workshop paper due. (Anywhere on Earth)

September 20, 2019: Workshop paper notifications.

November 7, 2019: Workshop Day.

Notice: The date of Workshop paper due has been postponed to September 5, 2019.

Organizers

 
Weinan Zhang

Weinan Zhang

Shanghai Jiao Tong University

 
Haiming Jin

Haiming Jin

Shanghai Jiao Tong University

 
Lingyu Zhang

Lingyu Zhang

Didi Chuxing

 
 
Hongtu Zhu

Hongtu Zhu

Didi Chuxing

University of North Carolina at Chapel Hill

 
Zhenhui Jessie Li

Zhenhui Jessie Li

Pennsylvania State University

 
Jieping Ye

Jieping Ye

Didi Chuxing

University of Michigan