Learning Identification Strategies for Traffic Flow Model: A Review Study
Subject Areas : Information Technology in Engineering Design (ITED) Journal
Mahboubeh Zare Feizabadi
1
,
Seyyed Abed Hosseini
2
,
Mahboobeh Houshmand
3
1 - Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
2 - Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
3 - Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Keywords: Traffic flow model prediction, ARIMA model, Hybrid model, Deep learning, Nonlinear macroscopic traffic model.,
Abstract :
Travel traffic and its results, which include air pollution and the chaotic economic situation, are among the factors that limit the development of healthy and sustainable cities. Traffic flow model parameters are important for urban road network management. Learning the identification strategy should be such that the modeling of the traffic network guarantees the simplicity, accuracy, and validation of the model. In this paper, different methods of traffic flow system identification, including traffic flow modeling and its prediction, are reviewed and analyzed in articles. At the end, the advantages and disadvantages of different methods in this field are categ orized.
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