A Review Of Traffic Congestion Prediction Using Artificial Intelligence Monitoring and understanding traffic congestion is difficult because of its complex nature. the paper analyzed the efficiency of three supervised machine learning algorithms on traffic. The main goals of this research are to: (1) examine how different factors, including weather, temporal patterns, and special events, affect the dynamics of traffic congestion; and (2) create machine learning based models that can precisely predict the levels of congestion under various scenarios.
Traffic Prediction Using Machine Learning This paper comprehensively reviews existing ml and deep learning methods for traffic congestion prediction, highlighting their strengths, limitations, and real world applications. The prediction of traffic congestion can serve a crucial role in making future decisions. although many studies have been conducted regarding congestion, most of these could not cover all the important factors (e.g., weather conditions). To carry out a comprehensive literature review on road trafic flow prediction models (statistical, machine learning, and process based) with applications to recurrent and non recurrent road trafic flow. This paper suggests a machine learning model to predict rush hour traffic congestion using a newly defined traffic congestion index (m tci), incorporating traffic density as a crucial factor for congestion prediction.
Figure 1 From The Traffic Congestion Prediction Using Machine Learning To carry out a comprehensive literature review on road trafic flow prediction models (statistical, machine learning, and process based) with applications to recurrent and non recurrent road trafic flow. This paper suggests a machine learning model to predict rush hour traffic congestion using a newly defined traffic congestion index (m tci), incorporating traffic density as a crucial factor for congestion prediction. We proposed a prediction model for the traffic congestion that can predict congestion based on day, time and several weather data (e.g., temperature, humidity). This systematic literature review thoroughly explores the development of traffic congestion forecasting methodologies from 2014 to 2024 by analyzing 100 peer reviewed publications according to the preferred reporting items for systematic reviews and meta analyses (prisma) guidelines. The idea of using machine learning to predict traffic jams embodies the convergence of data science and transportation engineering, offering a proactive solution to traffic issues. Li et al. [29] proposed a deep prediction model named lstm sprvm based on deep learning algorithms, machine learning algorithms, and spark parallelization technology for the prediction of traffic congestion features in the future.
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