The impact of weather data on traffic flow prediction models

Hatem Fahd Al-Selwi, Azlan Bin Abd Aziz, Fazly Salleh Abas, Nur Asyiqin Amir Hamzah, Azwan Bin Mahmud


Traffic flow prediction is an integral part of the intelligent transportation system (ITS) that helps in making well-informed decisions. Traffic flow prediction helps in alleviating traffic congestion as well as in some connected vehicles applications such as resources allocation. However, most of the existing models do not consider external factors such as weather data. Traffic flow in road networks is affected by weather conditions which affects the periodicity of traffic. These effects introduce some irregularity to the traffic pattern, making traffic flow prediction a challenging issue. In this paper, we present a detailed investigation on the impact of weather data on different traffic flow prediction models. The investigation presented in this paper demonstrates how adding weather data could improve the models’ prediction accuracy and efficiency.


Artificial intelligence; Deep learning; Intelligent transportation system; Smart city; Smart traffic management; Traffic prediction;

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