Recent Advancements in Traffic Flow Prediction: Review, Challenges and Future Research Directions

Abstract

Traffic congestion remains a prevalent issue in urban areas, contributing to environmental pollution, increased fuel consumption, and delays in emergency services. Addressing this challenge is paramount, with traffic flow prediction emerging as a pivotal technology within Intelligent Transportation Systems (ITS) to mitigate congestion and conserve time and energy. Extensive efforts have been directed towards developing predictive models, categorized into parametric, non-parametric, and hybrid approaches. While existing literature has extensively explored parametric and non-parametric models, this survey paper focuses on recent advancements in traffic flow prediction models, examining their merits and drawbacks. A comprehensive taxonomy of hybrid models utilized in traffic prediction is also provided. Additionally, the role of contextual features in enhancing traffic flow prediction is explored. Further, popular datasets employed for training traffic flow prediction models are discussed, accompanied by a comparative analysis of leading models’ performance using these datasets. The survey explores various performance metrics employed to evaluate prediction model efficacy. Furthermore, it identifies research gaps and challenges and outlines future research directions, aiming to contribute to the ongoing discourse on traffic flow prediction and inform future endeavors in this domain.