About this Research Topic

Manuscript Submission Deadline 27 August 2022

The recent rapid development of pervasive computing technologies has provided an unprecedented amount of data in urban areas. Utilizing such large amounts of spatio-temporal urban data and novel data-driven methods has become a new direction to solve many urban problems. In particular, decision-making problems in the urban space, such as site selection, vehicle routing, dispatching, scheduling, traffic signal control, etc., play a vital role in improving the efficiency and service level of modern urban systems. Modeling through the real-world data offers a way to more realistically characterize the real-world systems, thus allows for more deployable applications. However, developing real-world data-driven decision-making solutions for urban problems also faces a series of technical challenges, such as data sparsity, limited data coverage, complex spatio-temporal pattern modeling, anomaly detection, counterfactual reasoning, etc. New data modeling techniques, as well as data-driven decision-making algorithms, are needed to develop real-world deployable urban applications.

Data-driven methods have seen rapid development over the past few years and greatly change the landscape of the current urban system modeling paradigm. Given the emerging big data sources in urban space as well as the complexity of the real-world built environment, developing new spatio-temporal data modeling techniques, robust data-driven decision making algorithms, and new problem modeling paradigms, become essential in building successful real-world urban applications. There are still many technical challenges that remain in this field, which require novel modeling frameworks and robust data-driven optimization algorithms. This Research Topic seeks to advance insights and methodologies to help better model, design, and ultimately solve complex real-world urban decision-making problems.

This article collection welcomes papers on the broad areas regarding data-driven decision-making studies in urban applications, including but not restricted to:
- Practical data-driven applications that solve urban decision-making problems;
- Studies that combine data-driven and traditional approaches to solve urban decision-making problems;
- Novel data-driven decision-making modeling or optimization methodologies, such as new offline reinforcement learning, offline imitation learning, and offline planning algorithms and their applications:
- Studies that analyze or model real-world human decision-making strategies in specific scenarios using big data;
- Decision-making components to address real-world issues.

Keywords: Decision-making Modeling, Urban Computing, Data Mining, Data-driven Method, Optimization


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 recent rapid development of pervasive computing technologies has provided an unprecedented amount of data in urban areas. Utilizing such large amounts of spatio-temporal urban data and novel data-driven methods has become a new direction to solve many urban problems. In particular, decision-making problems in the urban space, such as site selection, vehicle routing, dispatching, scheduling, traffic signal control, etc., play a vital role in improving the efficiency and service level of modern urban systems. Modeling through the real-world data offers a way to more realistically characterize the real-world systems, thus allows for more deployable applications. However, developing real-world data-driven decision-making solutions for urban problems also faces a series of technical challenges, such as data sparsity, limited data coverage, complex spatio-temporal pattern modeling, anomaly detection, counterfactual reasoning, etc. New data modeling techniques, as well as data-driven decision-making algorithms, are needed to develop real-world deployable urban applications.

Data-driven methods have seen rapid development over the past few years and greatly change the landscape of the current urban system modeling paradigm. Given the emerging big data sources in urban space as well as the complexity of the real-world built environment, developing new spatio-temporal data modeling techniques, robust data-driven decision making algorithms, and new problem modeling paradigms, become essential in building successful real-world urban applications. There are still many technical challenges that remain in this field, which require novel modeling frameworks and robust data-driven optimization algorithms. This Research Topic seeks to advance insights and methodologies to help better model, design, and ultimately solve complex real-world urban decision-making problems.

This article collection welcomes papers on the broad areas regarding data-driven decision-making studies in urban applications, including but not restricted to:
- Practical data-driven applications that solve urban decision-making problems;
- Studies that combine data-driven and traditional approaches to solve urban decision-making problems;
- Novel data-driven decision-making modeling or optimization methodologies, such as new offline reinforcement learning, offline imitation learning, and offline planning algorithms and their applications:
- Studies that analyze or model real-world human decision-making strategies in specific scenarios using big data;
- Decision-making components to address real-world issues.

Keywords: Decision-making Modeling, Urban Computing, Data Mining, Data-driven Method, Optimization


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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