Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities

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J. Jenifer
R. Jemima Priyadarsini


The development of smart cities has occurred over the past ten years. One primary goal of “smart city” initiatives is to lessen vehicle congestion. Several innovative technologies, including vehicular communications, navigation, and traffic control, have been created by Vehicle Networking System to address this problem. The traffic data gathered by smart devices aids in the forecasting of traffic in smart cities. This project created an Intelligent Traffic Congestion Management System (ITCMS) that uses machine learning techniques and traffic data from Kaggle to decrease the amount of time spent stuck in traffic. This study aims to assess feature selection methods and machine learning models for traffic forecasting in smart cities. The feature dimension is reduced using feature selection techniques, such information gain, correlation attribute, and principal component analysis. The recommended model successfully predicted traffic flow, assisting in the alleviation of congestion. The principal component analysis with random forest model outperforms the other machine learning models and has a 95% accuracy rate.

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Jenifer, J. ., & Priyadarsini, R. J. . (2023). Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities. International Journal on Recent and Innovation Trends in Computing and Communication, 11(5s), 269–275. https://doi.org/10.17762/ijritcc.v11i5s.6653


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