Research Topic

Urban Travel Mobility Analysis and Prediction using Traffic Flow Data from Multi-Source Detection Sensors

About this Research Topic

Urban traffic commuting is important for daily life in weekdays, and many people will go to work under different transportation modes. It is common to encounter traffic jams in peaking hours, which indicates that the capacity of urban road network cannot satisfy the demand of travel mobility. Thus, it is important to enhance urban traffic management efficiency via the analysis and prediction of traffic flow using large-scale deployed sensors, for example, loop detectors, radar, unmanned aerial vehicle monitoring data, and trajectories, etc. It is noted that single type of traffic sensing data maybe inaccurate due to unexpected events. To that aim, it is essential to employ multi-source detecting traffic data to reasonably analyze and accurately predict the variation of urban travel mobility.

It is a great challenge to develop novel frameworks for the purpose of urban travel mobility analysis and prediction, such as missing traffic flow data imputation, trajectory mapping and route inferring, traffic pattern identification and mining, traffic demand prediction, anomaly traffic flow data correction, etc. This Research Topic aims to solicit research that implements cutting-edge urban travel mobility analysis via multi-source traffic flow sensing data. The Research Topic emphasizes the application of novel methodologies and approaches using various traffic flow data to help us better understand the urban mobility.

We invite full paper submissions fitting the general theme of “urban travel mobility analysis and prediction using traffic flow data from multi-source detection sensors”. We also encourage submissions from a broad range of research fields related to urban travel mobility issues. Exemplary topics of interest include, but are not limited to:
• Traffic flow modeling, analyzing and predicting via multiple traffic data (e.g., inductive loop detector, video surveillance data, floating car)
• High-resolution traffic data collection via state-of-art artificial intelligence techniques
• Trajectory-based urban mobility pattern exploration and analysis
• Urban traffic safety analysis via various multiple traffic sensing data
• OD (Origin and Destination) distribution estimation, modeling and prediction on urban travel mobility
• Urban traffic mobility oriented future transportation analysis


Keywords: Mobility, Traffic flow, Prediction, Multi-Source data detection, Traffic management


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Urban traffic commuting is important for daily life in weekdays, and many people will go to work under different transportation modes. It is common to encounter traffic jams in peaking hours, which indicates that the capacity of urban road network cannot satisfy the demand of travel mobility. Thus, it is important to enhance urban traffic management efficiency via the analysis and prediction of traffic flow using large-scale deployed sensors, for example, loop detectors, radar, unmanned aerial vehicle monitoring data, and trajectories, etc. It is noted that single type of traffic sensing data maybe inaccurate due to unexpected events. To that aim, it is essential to employ multi-source detecting traffic data to reasonably analyze and accurately predict the variation of urban travel mobility.

It is a great challenge to develop novel frameworks for the purpose of urban travel mobility analysis and prediction, such as missing traffic flow data imputation, trajectory mapping and route inferring, traffic pattern identification and mining, traffic demand prediction, anomaly traffic flow data correction, etc. This Research Topic aims to solicit research that implements cutting-edge urban travel mobility analysis via multi-source traffic flow sensing data. The Research Topic emphasizes the application of novel methodologies and approaches using various traffic flow data to help us better understand the urban mobility.

We invite full paper submissions fitting the general theme of “urban travel mobility analysis and prediction using traffic flow data from multi-source detection sensors”. We also encourage submissions from a broad range of research fields related to urban travel mobility issues. Exemplary topics of interest include, but are not limited to:
• Traffic flow modeling, analyzing and predicting via multiple traffic data (e.g., inductive loop detector, video surveillance data, floating car)
• High-resolution traffic data collection via state-of-art artificial intelligence techniques
• Trajectory-based urban mobility pattern exploration and analysis
• Urban traffic safety analysis via various multiple traffic sensing data
• OD (Origin and Destination) distribution estimation, modeling and prediction on urban travel mobility
• Urban traffic mobility oriented future transportation analysis


Keywords: Mobility, Traffic flow, Prediction, Multi-Source data detection, Traffic management


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

31 October 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

31 October 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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