Workshop on Data-Driven Intelligent Transportation (in Conjunction with ICDM'18)

November 17, 2018
Singapore
http://dm.ist.psu.edu/dit2018/

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Traffic is the pulse of the city. Intelligent transportation enables the 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.
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

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).

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Organizers:

Zhenhui (Jessie) Li, Penn State University
Yan Liu, University of Southern California