A SURVEY OF MACHINE LEARNING MODELS FOR TRAFFIC MOVEMENT PREDICTION - Ihama, Eyotor I.; Amenaghawon, V. A. (FUDMA JOURNAL OF SCIENCES, 2024)
In most developed cities globally, traffic congestion has become a major challenge to commuters and road users. In most of the urbanized nations, there are traffic gridlock at certain periods of the day (peak periods). Road users spend alot of time at these gridlocks, wasting a lot of working hours. This gridlock has also resulted to air pollution and accident. Many researchers have develoed different vehicular movement prediction models for better traffic prediction. In this paper, we surveyed different traffic prediction model for congestion management.
Keywords: Gridlock; Vehicular movement; Peak period; Prediction