1st Workshop on Data-driven Intelligent Transportation (DIT 2018)
Held in conjunction with ICDM 2018
Location: Aquarius 4 of the Convention Center at Resort World Sentosa (RWS) at Sentosa Island Singapore
Traffic is the pulse of the city. Intelligent transportation enables city to function in a more efficient and effective way. At the same time, city data are growing at an unprecedented speed. A wide range of city data become increasingly available, such as taxi trips, surveillance camera data, human mobility data from mobile phones or location-based services, events from social media, car accident report, bike sharing information, Points-Of-Interest, traffic sensors, public transportation data, and many more.
How to utilize such large-scale city data towards a more intelligent transportation system? This workshop calls for interesting papers with techniques to utilize city data and data mining techniques to improve our transportation system.
- Semantic mobility data understanding
- Large-scale city data analysis and modeling
- Large-scale traffic data visualization and interactive design
- Sustainable transportation system
- City data sensing and collecting
- City data fusion and mining
- Anomaly detection and forecasting
In particular, this workshop would like to call for research papers sharing the experiences from the real data and real-world practice. We do not require technical innovations (using existing data mining techniques is totally acceptable).
- Long paper (up to 8 pages) and short paper (up to 4 pages). The page limit includes the bibliography and any possible appendices.
- All papers must be formatted according to the IEEE Computer Society proceedings manuscript style, following IEEE ICDM 2018 submission guidelines available at: http://icdm2018.org/calls/call-for-papers.
- All accepted papers will be included in the IEEE ICDM 2018 Workshops Proceedings volume published by IEEE Computer Society Press, and will also be included in the IEEE Xplore Digital Library. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.
Paper Submission Deadline: August 28, 2018, 11:59 PM Pacific Time.
Paper Notification: September 9, 2018
Camera Ready Version: September 15, 2018
Workshop: November 17, 2018
Chao Zhang University of Illinois at Urbana-Champaign
Fang Jin Texas Tech University
Fei Wu Pennsylvania State University
Hongjian Wang Pennsylvania State University
Jingyuan Wang Beihang University
Qi Yu California Institute of Technology
Sanjay Purushotham University of Southern California
Xun Zhou The University of Iowa
Yanjie Fu Missouri University of Science and Technology
Yongxin Tong Beihang University
Zhe Jiang The University of Alabama
Zijun Yao IBM Research
Map Making Meets Data Science
Speaker: Sanjay Chawla
With the impending arrival of self-driving cars, the race to build accurate maps is on. Many start-ups exclusively dedicated to building accurate maps have mushroomed and major car companies and technology giants have assembled large map teams. At Qatar Computing Research Institute (QCRI), we are working with local stakeholders and collaborating with MIT to algorithmically build accurate maps using data from multiple sources.
In this talk, Dr. Sanjay Chawla will focus on two problems: (i) map-construction: given GPS trajectories and satellite images, construct a road network graph which is “close” to the ground truth map; (ii) map-fusion: given a base map and GPS data from vehicles that ply on the road network, construct a new map which is consistent with both the base map and GPS data.
Map making turns out to be an excellent pedagogic use case to demonstrate the power of (and possibly even define) “Data Science.” This is joint work with collaborators at QCRI and MIT.
Bio: Dr. Sanjay Chawla is the Research Director of the Data Analytics Group at Qatar Computing Research Institute (QCRI). Before that he was a Professor in the School of Information Technologies at the University of Sydney, Australia. His research works spans data mining, machine learning and spatial data analysis. He is a co-author on the text “Spatial Databases: A Tour”
Data Science Behind Grab
Speaker: Wenqing Chen
Grab is an everyday super-app in Southeast Asia, providing the services that matter most to consumers – safe and affordable transport, food and package delivery, mobile payments and financial services. We have facilitated 2.5 billion rides to date, covering 235 cities in 8 countries. Grab has deep insights on how cities across Southeast Asia move today.
In this talk, Dr. Chen Wenqing will explain how Grab uses Data Science to impact the ride hailing and delivery experience for both passengers and drivers. She will also highlight how Data Science improves the network efficiency, so to bring smarter transport and smoother traffic to cities across Southeast Asia.
Bio: Dr. Wenqing Chen is a Data Science Lead at Grab, managing the development of data-centric solutions for Transport and Delivery services in the region. She is mainly focused on solving challenging problems using optimization algorithms, machine learning and simulation techniques. Prior to Grab, she has held various roles with leading IT companies, including IBM, where she was Leading consultants developing and implementing Operations Research solutions; ILOG, where she was involved in one of the fastest optimization softwares, CPLEX. She received Ph.D. from Operations Research, National University of Singapore in 2007. She also holds dual degrees in Economics and Computer Science from Shanghai Jiaotong University. She published several papers in top journal “Operations Research” and her research interest lies in Robust Optimization, Approximate Dynamic Programming and Transportation Planning.
Reinforcement Learning for Intelligent Transportation
Speaker: Zhenhui (Jessie) Li
Large-scale mobility data can be collected from mobile phones, car navigation systems, road surveillance cameras, and loop sensors. Turning such mobility data into knowledge can provide insights about our city and empower the city to be more intelligent.
This talk presents how to utilize mobility data and advanced learning methods for traffic signal control. First, we examine the existing traffic signal control system and discuss why today we have the opportunity for a potential breakthrough in traffic signal control. Second, the talk presents our recent research results in traffic signal control via deep reinforcement learning. We demonstrate how the classical transportation methods can be integrated to guide our reinforcement learning approach. Finally, we would like to discuss the open challenges in this research topic and share the experience in the field experiments of controlling traffic signals in the city of Hangzhou.
Bio: Dr. Zhenhui (Jessie) Li is a tenured associate professor of Information Sciences and Technology at the Pennsylvania State University. She is Haile family early career endowed professor. Prior to joining Penn State, she received her PhD degree in Computer Science from University of Illinois Urbana-Champaign in 2012, where she was a member of data mining research group. Her research has been focused on mining spatial-temporal data with applications in transportation, ecology, environment, social science, and urban computing. She is a passionate interdisciplinary researcher and has been actively collaborating with cross-domain researchers. She has served as organizing committee or senior program committee of many conferences including KDD, ICDM, SDM, CIKM, and SIGSPATIAL. She has received NSF CAREER award, junior faculty excellence in research, and George J. McMurtry junior faculty excellence in teaching and learning award. She is currently taking sabbatical leave (2018-2019) at Hangzhou China to conduct city brain research. She is looking for interns to join her research team in Hangzhou. To learn more, please visit her homepage: https://faculty.ist.psu.edu/jessieli
ICDM Paper Blitz Sessions
We invite accpected papers related to our workshop from ICDM 2018 to give us talks on their papers. Speakers will be updated soon.
Human-Centric Urban Transit Evaluation and Planning
Sanjay Chawla Research Director of the Data Analytics Group at Qatar Computing Research Institute
Paper Presentation: Reinforcement Learning in Transportation
Optimal Routing for Autonomous Taxis using Distributed Reinforcement Learning 09:30 AM -09:45AM
Salar Rahili (California Institute of Technology, United States), Benjamin Riviere (California Institute of Technology, United States), Suzanne Oliver (California Institute of Technology, United States), Soon-Jo Chung (California Institute of Technology, United States)
Deep Reinforcement Learning for Traffic Light Optimization 09:45 AM - 10:00 AM
Mustafa Coskun (QCRI, Qatar),Abdelkader Baggag (QCRI, Qatar),Sanjay Chawla (QCRI, Qatar)
Zhenhui (Jessie) Li Associate professor at the Pennsylvania State University
Paper Presentation: Practice for a Better City
LiSense: Monitoring City Street Lighting during Night using Smartphone Sensors 02:10 PM -- 02:25 PM
Munshi Yusuf Alam (National Institute of Technology, Durgapur, India),MD Shahrukh Imam (National Institute of Technology, Durgapur, India), Harshit Anurag(National Institute of Technology, Durgapur, India), Sujoy Saha (National Institute of Technology, Durgapur, India), Subrata Nandi (National Institute of Technology, Durgapur, India),Mousumi Saha (National Institute of Technology, Durgapur, India)
Anomaly Detection in Car-Booking Graphs 02:25 PM -- 02:35 PM
Oleksandr Shchur (Technical University of Munich, Germany), Aleksandar Bojchevski (Technical University of Munich, Germany), Mohamed Farghal (Technical University of Munich, Germany), Stephan Günnemann (Technical University of Munich, Germany), Yusuf Saber (Careem, United Arab Emirates)
Online Vehicle Dispatch: from Assignment to Scheduling 02:35 PM -- 02:45 PM
A Smartphone-based Probe Data Platform for Road Management and Safety in Developing Countries 02:45 PM -- 02:55 PM
Kotaro Kataoka (Indian Institute of Technology Hyderabad, India), Saurabh Gangwar (Indian Institute of Technology Hyderabad, India), Karthik Yadav Mudda (Indian Institute of Technology Hyderabad, India), Souraj Mandal (Indian Institute of Technology Hyderabad, India)
Stitching Aerial Images for Vehicle Positioning and Tracking 02:55 PM -- 03:10 PM
Gary Goh (Singapore University of Technology and Design, Singapore), Jing Yu Koh (Singapore University of Technology and Design, Singapore), Yue Zhang (Singapore University of Technology and Design, Singapore)
Predicting MRT trips in Singapore by Creating a Mobility Behavior Model based on GSM Data 03:45 PM -- 04:00 PM