Special Issue: Data-driven Intelligent Transportation
Prof. Hua Wei
Department of Informatics, New Jersey Institute of Technology, USA
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. How to utilize such data toward city intelligence, across various transportation tasks?
The special issue of "Data-driven Intelligent Transportation" for ERA 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, case studies, as well as large-scale deployment.
The topics of interest include but are not limited to the following:
• 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
Instructions for authors
Please submit your manuscript to online submission system