1st SIGIR Workshop on
Intelligent Transportation Informatics
Held in conjunction with SIGIR'18
July 12, 2018 - Ann Arbor, Michigan, United States

Introduction

Modern transportation is no longer powered by fuel or electricity alone. It is powered by information as well. The transportation system has come to an era where the abundant information and the advanced machine learning, data mining, and decision-making algorithms can join forces to solve the issues we face in transportation and make it more intelligent and efficient.

We propose the Intelligent Transportation Informatics (InTI), which refers to the acquisition, integration, and analysis of heterogeneous information generated in transportation, to improve the efficiency of the transportation system. The vision of InTI calls for interdisciplinary collaborations between computer science and other fields, such as transportation, economy, and operational research.

Schedule

Room D (Floor 3), Michigan League

1:30 PM - 5:00 PM

July 12, 2018

1:30 PM - 2:20 PM Keynote Talk : Data and Decision Sciences for Mobility Services by Pascal Van Hentenryck

2:20 PM - 3:00 PM Paper Presentation

  • Modeling Transportation Uncertainty in Matching Help Seekers and Suppliers during Disasters. Hemant Purohit, Nikhita Vedula, Krishnaprasad Thirunarayan and Srinivasan Parthasarathy.
  • POI Semantic Model with a Deep Convolutional Structure. Ji Zhao, Meiyu Yu, Huan Chen, Boning Li, Lingyu Zhang, Qi Song, Li Ma, Hua Chai and Jieping Ye.

3:00 PM - 3:30 PM Coffee Break

3:30 PM - 4:20 PM Keynote Talk : Promises and Issues of Transportation Big Data by Xuegang (Jeff) Ban

4:20 PM - 5:00 PM Poster Session

  • Spatio-temporal Ensemble Method for Car-Hailing Demand Prediction. Yang Liu, Zhiyuan Liu and Cheng Lyu.
  • DiDi Ride Cancellation Smart Duty Judge System. Cheng Zhang, Xiaolin Deng, Zhangxun Liu, Yifang Wang, Lichen Shi and Bing Han.
  • An Information Theoretic Metric for Performance Prediction of Machine Vision Systems. Ismael Xique, William Buller and Benjamin Hart.
  • Data-Driven Modeling of Ride-Hailing Trajectories. Hao Yuan, Qi Luo and Robert Hampshire.
  • Bus is Coming: Predicting Bus Arrival Time Based on Real-time GPS Data. Muhan Yuan, Kaifeng Chen, Huoran Li, Xuanzhe Liu and Qiaozhu Mei.

keynotes

 
Pascal Van Hentenryck

Pascal Van Hentenryck

University of Michigan    

 
University of Washington

Xuegang (Jeff) Ban

University of Washington

 

Title : Data and Decision Sciences for Mobility Services

Presenter : Pascal Van Hentenryck

Abstract : The availability of massive data sets, combined with progress in communication technologies, data and decision sciences, and connected and automated vehicles has the potential to transform mobility for entire population segments. This talk reviews this opportunity, from its potential societal impact, to the development of new mobility services, and the science and technology powering them. In particular, the talk presents recent developments in on-demand multimodal transit systems and community-based trip sharing on real case studies, as well as the optimization and privacy mechanisms underlying them.

Bio : Pascal Van Hentenryck is the Seth Bonder Collegiate Professor at the University of Michigan. He is professor of Industrial and Operations Engineering, Professor of Computer Science and Engineering, and Core Faculty in the Michigan Institute for Data Science. Prior to this appointment, he led the optimization research group (about 70 people) at National ICT Australia (NICTA) (until its merger with CSIRO) and was a professor of Computer Science at Brown University for about 20 years, which he joined after his PhD in Belgium. Van Hentenryck is also an Honorary Professor at the Australian National University.Van Hentenryck is a Fellow of AAAI (the Association for the Advancement of Artificial Intelligence) and INFORMS (the Institute for Operations Research and Management Science). Van Hentenryck is program co-chair of the AAAI’19 conference.

 

Title: Promises and Issues of Transportation Big Data

Presenter: Xuegang (Jeff) Ban

Abstract : Big data and related data analytics methods have received much attention recently in transportation for various planning and operational applications. This talk summarizes the promises of big data and illustrates the issues of some commonly used big data sources in transportation. We then briefly discuss the implications of such issues and suggest a possible pathway that may help address those issues.

Bio : Dr. Xuegang (Jeff) Ban is an Associate Professor of the Department of Civil and Environmental Engineering at the University of Washington. He received his B.S. and M.S. in Automobile Engineering from Tsinghua University, and his M.S. in Computer Sciences and Ph.D. in Civil and Environmental Engineering (Transportation) from the University of Wisconsin at Madison. His research focuses on Transportation Network System Modeling and Simulation, Urban Traffic System Modeling and Control, and Intelligent Transportation Systems (ITS). He has published more than 120 papers in refereed journals, as book chapters, or in conference proceedings. He is a member of the TRB’s Network Modeling Committee and Vehicle-Highway Automation Committee. He is an Associate Editor of Transportation Research Part C, IEEE Transactions on Intelligent Transportation Systems, and Journal of Intelligent Transportation Systems. He is the recipient of the CAREER Award from the National Science Foundation, and the New Faculty Award from the Council of University Transportation Centers (CUTC) and the American Road & Transportation Builders Association (ARTBA).

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 https://easychair.org/conferences/?conf=inti2018 .

For inquires about the workshop and submissions, please email to inti2018workshop@umich.edu

Important Dates

May 14, 2018 (extended): Workshop papers due. (Anywhere on Earth)

May 25, 2018: Workshop paper notifications.

June 8, 2018: Camera-ready deadline for workshop papers.

July 12, 2018: Workshop Day.

Organizers

Yan Liu

Yan Liu

University of Southern California

Didi Chuxing

 
Zhenhui Jessie Li

Zhenhui Jessie Li

Pennsylvania State University

 
Wei Ai

Wei Ai

University of Michigan

 
Lingyu Zhang

Lingyu Zhang

Didi Chuxing

 
Kurt Liang

Kurt Liang

Didi Chuxing

 
Chenxi Wang

Chenxi Wang

Didi Chuxing