November 17, 2018 in SINGAPORE

1st Workshop on Data-driven Intelligent Transportation (DIT 2018)

Held in conjunction with ICDM 2018

Traffic is the pulse of the city. Intelligent transportation makes city flow smoothly so that city can function more efficiently. At the same time, modern technologies enable us to collect city data 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, car accidents, shared bikes, POI, traffic from loop sensors, public transportation data, and many more.

How to utilize such large-scale city data towards a more intelligent transportation system? While intelligent transportation is not a new topic, especially in the field of transportation research, existing transportation is not effectively utilizing large-scale city data and new computing technologies. For example, SCATS (Sydney Coordinated Adaptive Traffic System) is now widely used in major cities of China for traffic signal control. SCATS uses a set of manually pre-defined rules to control traffic signals. Such pre-defined rules are not learnt from real data and are rarely being updated to accommodate the changes of traffic. With all the new traffic data sources and also more powerful computing technologies, it is promising to design a new data-driven traffic signal control system. There are many specific data mining questions in order to implement this novel data-driven traffic signal control system (e.g., traffic forecasting, traffic pattern mining, agent-based signal coordination). Furthermore, traffic signal control is just one example of intelligent transportation system. There are also many more interesting questions in intelligent transportation, such as route planning, shared transportation, autonomous driving, data sensing, and etc.

This workshop calls for interesting papers with techniques to use city data to improve our transportation system. Topics of interest include but not limited to:

  •     - Traffic forecasting
  •     - Route planning
  •     - Travel time estimation
  •     - Traffic signal control
  •     - Shared transportation
  •     - Autonomous driving vehicles
  •     - City-wide traffic estimation
  •     - 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


We invite two kinds of submissions:

  • Full research papers – up to 9 pages (8 pages at most for the main boday and the last page can only hold references)
  • Vision papers and short technical papers - up to 5 pages (4 pages at most for the main boday and the last page can only hold references)



Zhenhui (Jessie) Li Pennsylvania State University


Yan Liu University of Southern California