Research Topic

Integration of Real-Time Information in Transport Planning and Operations

About this Research Topic

The availability and accessibility of advanced communication and information technologies have made it possible to improve the efficiency and reliability of transportation systems both for passengers and goods. As a result, more informed decisions can be made for such real-time operations. Various intelligent transport systems applications have affected the performance of public transport, last mile delivery of products, ridership and demand patterns. Such information helps the operators to take wiser decisions in real-time to use the available resources. It also helps the users to make better decisions based on their preferences. Currently, data availability offers various opportunities for extraction of information but still we require efficient models and algorithms to exploit this information by incorporating it in operational, tactical or strategical planning.

The advent of data in such large scale opens new research avenues and opportunities for optimizing transport and logistic systems. The availability of real-time information requires efficient mathematical models and algorithms to account for users' behavior to handle the dynamic nature of data. All these challenges call for the development of appropriate methods capable of inferring required information for model estimation and planning.

This special issue aims to bring together novel research on integrating real time data in transport planning (mobility and logistics), and encourages submissions of original research and review articles on (but not limited to) the following topics:

•New measurements and methods to adjust users’ behavior in transport systems in real time.

•New Conceptual and methodological frameworks to incorporate demand forecasting in operational planning.

•Applications of methods and models incorporating real-time data in operational decision making for transport systems.

•New methods and algorithms to suggest efficient recovery plans in case of disruptions/disturbances in transport systems in real-time

All submissions will be peer-reviewed according to the standards of the journal.

Please note: There is currently a 50% publishing discount in place for the journal - this is already reflected in the fees page of our website. This is in effect until May 2021, and any submissions before the 14th May 20201 will receive this discount automatically.


Keywords: on-demand mobility, city logistics, operations research, simulation, heuristics and algorithms, machine learning


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.

The availability and accessibility of advanced communication and information technologies have made it possible to improve the efficiency and reliability of transportation systems both for passengers and goods. As a result, more informed decisions can be made for such real-time operations. Various intelligent transport systems applications have affected the performance of public transport, last mile delivery of products, ridership and demand patterns. Such information helps the operators to take wiser decisions in real-time to use the available resources. It also helps the users to make better decisions based on their preferences. Currently, data availability offers various opportunities for extraction of information but still we require efficient models and algorithms to exploit this information by incorporating it in operational, tactical or strategical planning.

The advent of data in such large scale opens new research avenues and opportunities for optimizing transport and logistic systems. The availability of real-time information requires efficient mathematical models and algorithms to account for users' behavior to handle the dynamic nature of data. All these challenges call for the development of appropriate methods capable of inferring required information for model estimation and planning.

This special issue aims to bring together novel research on integrating real time data in transport planning (mobility and logistics), and encourages submissions of original research and review articles on (but not limited to) the following topics:

•New measurements and methods to adjust users’ behavior in transport systems in real time.

•New Conceptual and methodological frameworks to incorporate demand forecasting in operational planning.

•Applications of methods and models incorporating real-time data in operational decision making for transport systems.

•New methods and algorithms to suggest efficient recovery plans in case of disruptions/disturbances in transport systems in real-time

All submissions will be peer-reviewed according to the standards of the journal.

Please note: There is currently a 50% publishing discount in place for the journal - this is already reflected in the fees page of our website. This is in effect until May 2021, and any submissions before the 14th May 20201 will receive this discount automatically.


Keywords: on-demand mobility, city logistics, operations research, simulation, heuristics and algorithms, machine learning


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

01 March 2021 Abstract
30 July 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

01 March 2021 Abstract
30 July 2021 Manuscript

Participating Journals

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

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