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An Overview of Model-Driven and Data-Driven Forecasting Methods for Smart Transportation

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Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications

Part of the book series: Studies in Big Data ((SBD,volume 132))

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Abstract

Rapid economic development has brought with it an increase in traffic demand and, as a result, serious traffic problems (e.g., congestion, air pollution, and road accidents). Intelligent transport systems (ITS) can significantly improve the efficiency and sustainability of traffic networks by reducing all these problems. Traffic forecasting is an essential component of ITS applications. By providing timely and accurate real-time traffic information for traffic drivers, which can be used for better decision-making and quick actions, think about the future development of real smart transportation in smart cities. Traffic flow forecasting is still an open challenge. Many methods that deal with this problem have been tried out in the empirical literature, but there is still a lot of room for improvement. A comprehensive overview of the development of traffic flow forecasting models is provided in the form of model-driven and data-driven approaches. Through this literature review, which describes the historical evolution of forecasting models, we can identify key trends in forecasting: (1) adjustments and extensions of model-driven to deal with real phenomena; (2) advances in data-driven techniques; (3) the explosion of big data; (4) the convergence of many methods toward machine learning (ML); and (5) the development of hybrid models coupling the advantages of different models.

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  1. Ecole Supérieure de Commerce de Tunis, Université de Manouba, Manouba, Tunisia

    Sonia Mrad & Rafaa Mraihi

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  1. Sonia Mrad
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  2. Rafaa Mraihi
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  1. División Multidisciplinaria de Ciudad Universitaria, Universidad Autónoma de Ciudad Juárez, Chihuahua, Mexico

    Gilberto Rivera

  2. Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Ciudad Madero, Tamaulipas, Mexico

    Laura Cruz-Reyes

  3. School of Engineering, Universidad de Cádiz, Cadiz, Spain

    Bernabé Dorronsoro

  4. Universidad Tecnológica de La Habana “José Antonio Echeverría”, La Habana, Cuba

    Alejandro Rosete

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Mrad, S., Mraihi, R. (2023). An Overview of Model-Driven and Data-Driven Forecasting Methods for Smart Transportation. In: Rivera, G., Cruz-Reyes, L., Dorronsoro, B., Rosete, A. (eds) Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications. Studies in Big Data, vol 132. Springer, Cham. https://doi.org/10.1007/978-3-031-38325-0_8

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