With increasing economic and social activities, travel demand has increased significantly over the past several decades, overloading many already congested roadways. The widening gap between travel demand and infrastructure supply has worsened the levels of congestion worldwide, resulting in many urban mobility, safety, and environmental issues, such as severe congestion, lengthened travel time, increased risk of traffic accidents, excessive fuel consumption, increased air pollution, and significant public health issues. Driven by technological innovation in traffic sensing, sensor data visualization and interpretation, smart city, network communication, big data analytics, connected vehicle systems, and traffic network resilience, urban mobility has been improved rapidly in the past decade. More and more transportation-related data and computational resources become available.

These new resources enable applications of data-driven approaches to model and analyze urban mobility. This special issue aims to serve as a major platform to facilitate the discussion and exchange of research ideas and technology development, encourage multidimension knowledge sharing, and enhance research activities in data-driven urban mobility modeling and analysis.

This special issue solicits novel contributions on all aspects of theoretical and applied studies in data-driven urban mobility modeling and analysis. This special issue is open to the entire international research community.

Potential topics include but are not limited to the following:

• Big data enabled urban transit system operation and optimization
• Shared-mobility system modeling
• Mobile sensing in urban transportation system
• Urban mobility enhancement in the context of connected/automated vehicles
• Smart pedestrian/bicycle facility design for traffic mode shifting
• Intelligent and adaptive signal control in urban networks
• Urban expressway and freeway operations and management
• Multimode connection optimization around metropolitan urban centers