Traffic is the pulse of the city. Transportation systems can involve humans, vehicles, shipments, information technology, and the physical infrastructure, all interacting in complex ways. Intelligent transportation enables the city to function in a more efficient and effective way. 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 reports, bike-sharing information, Points-Of-Interest, traffic sensors, public transportation data, and many more. This abundance of data poses a grand challenge to the CIKM research community: How to utilize such data toward city intelligence, across various transportation tasks?

The 3rd workshop of "Data-driven Intelligent Transportation" welcomes articles and presentations in the areas of transportation systems, data mining, and artificial intelligence, conveying new advances and developments in theory, modeling, simulation, testing, and case studies, as well as large-scale deployment.

This workshop would like to bring together the researchers to share the exciting data-driven techniques to solve transportation problems.

Topics of interest include but not limited to:

- Data mining techniques for transportation: traffic forecasting, travel time estimation, traffic estimation, semantic mobility data understanding, data visualization, and interactive design
- Intelligent control and planning in transportation: traffic signal control, autonomous driving, vehicle dispatching, route planning, public transportation management, including air, road, and rail traffic management
- Open datasets, benchmarks and demonstrations in transportation
- Security, privacy, and safety issues in transportation systems
- Support systems for drivers, pedestrians, bike riders, policymakers and other parties
- Public policy, and regulatory and societal issues in transportation systems
- Intelligent transportation systems integration into existing road infrastructure


======= Submission Guidelines =====

This workshop follows the submission requirement by CIKM. All the accepted papers will be treated as invited papers to the "Special Issue: Data-driven Intelligent Transportation" published by Electronic Research Archive and waived the fees.

Rules:
- 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 ACM sigconf template manuscript style, following the submission guidelines available at: https://www.acm.org/publications/proceedings-template.
- Papers should be submitted in PDF format, electronically, using the CMT submission system: https://cmt3.research.microsoft.com/DIT2022
- All accepted papers will be included in the "Special Issue: Data-driven Intelligent Transportation" published by Electronic Research Archive. Therefore, papers must not have been accepted for publication elsewhere or be under review for another workshop, conferences or journals.