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Title	An Overview Of Model-Driven And Data-Driven Forecasting Methods For Smart Transportation
ID_Doc	8929
Authors	Mrad S.; Mraihi R.
Year	2023
Published	Studies in Big Data, 132
DOI	http://dx.doi.org/10.1007/978-3-031-38325-0_8
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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Author Keywords	Data-driven approach; Intelligent transportation systems; Model-driven approach; Smart transportation; Traffic flow forecasting

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