Data-driven solutions can be used to discover quantitative insights and to assist informed decision-making in transportation management. Other than the methodological breakthroughs—for example, artificial intelligence—their successes rely largely on the data availability.

Thanks to advances in communications, sensing, and data processing technologies, massive amounts of transportation-related data are being made available through new sources, such as connected and autonomous vehicles, Wi-Fi and Bluetooth devices, smartphone applications, traffic cameras, LiDAR, and social media. The emerging data pave the way for timely, closely, and intelligently probing and managing our transportation systems. New research rises from leveraging large-scale emerging data for developing various data-driven solutions for transportation management.

This Special Issue aims to solicit recent research progress and practices in smart transportation management empowered by emerging data and advances in quantitative approaches. We encourage both original research and review articles that focus on the use of real-world, large-scale, multidimensional emerging data to improve existing theories and practices of transportation management with actionable solutions.

Potential topics include but are not limited to the following:

• Data fusion for traffic management (e.g., fusing data from loop detectors and probe vehicles for congestion detection)
• Data-driven multimodal transportation management (e.g., integration of public transit and bicycle-sharing systems)
• Smart demand management (e.g., dynamic tolling and congestion pricing)
• Proactive safety management (e.g., real-time risk detection and measures for crash prevention)
• Innovative traffic incident management (e.g., incident detection, impact analysis, and secondary incident prevention)
• Data-centric emergency management (e.g., evacuation modeling)
• Intelligent work zone management (e.g., advanced warning)
• Management of shared-mobility systems (e.g., car sharing, carpooling, and fleet management)