A SURVEY OF MACHINE LEARNING MODELS FOR TRAFFIC MOVEMENT PREDICTION

Authors

Keywords:

Gridlock, Vehicular movement, Peak period, Prediction

Abstract

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.

Dimensions

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Published

10-08-2024

How to Cite

A SURVEY OF MACHINE LEARNING MODELS FOR TRAFFIC MOVEMENT PREDICTION. (2024). FUDMA JOURNAL OF SCIENCES, 8(4), 172-178. https://doi.org/10.33003/fjs-2024-0804-2650

Issue

Vol. 8 No. 4 (2024): FUDMA Journal of Sciences - Vol. 8 No. 4

Section

Review Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

A SURVEY OF MACHINE LEARNING MODELS FOR TRAFFIC MOVEMENT PREDICTION. (2024). FUDMA JOURNAL OF SCIENCES, 8(4), 172-178. https://doi.org/10.33003/fjs-2024-0804-2650

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