With the ubiquitous application of location-aware devices, trajectory data can be collected from vehicles distributed within the urban road network. Using vehicle GPS traces to analyze urban mobility patterns is gradually becoming an emerging topic in the fields of urban planning, transportation science, and geography. Urban travel mobility often shows a high level of regularity, and it serves as a critical factor to assess the functionality and rationality of a city road network in terms of spatial structure and connectivity. Uncovering the underlying pattern of urban travel mobility can further provide effective guidance for urban planning and management.

Given its rich spatiotemporal information, large-scale vehicle trajectory data has been widely used to explore urban mobility patterns in recent research. However, we still face some prominent challenges in trajectories data quality control and pre-processing. To improve data quality and enhance analyzing accuracy, the field is calling for new methods and frameworks for large-scale trajectory data, such as missing data imputation, trajectory map-matching algorithm, sequential pattern and motif pattern mining, trajectory clustering, anomaly detection, next location prediction, uncertainty analysis, and bias/error data identification and repair, are also necessary.

This Special Issue aims to invite research that discovers recent methodologies, data analytics, and applications of state-of-the-art urban travel mobility analysis based on trajectories in the context of urban and transportation planning and management. The special issue emphasizes the application of novel methodologies and approaches using trajectories to advance our understanding about the regularity rooted in urban mobility. We invite research articles fitting the general theme of “urban mobility analytics using large-scale vehicle trajectory data”, as well as review articles discussing the current state of the art. We also encourage submissions from a broad range of research fields related to urban issues.

Potential topics include but are not limited to the following:

• Data quality analysis and control for trajectory data to deal with its uncertainty and bias/error for application
• Clustering method for understanding travel mobility considering spatiotemporal features of trajectories
• Relation between trajectory-derived mobility pattern and urban city structure or planning
• Uncovering travel patterns of residents from spatial and temporal dimensions
• Impacts of built environment on travel behavior with trajectory data
• Routing choice behavior analysis considering traffic, land-use, and road factors
• Traffic state estimation and prediction in the urban road network
• OD (Origin and Destination) distribution estimation, modeling and prediction