Abstract
People move from place to place for various purposes using different modes of transportation. This creates traffic on the roads. As population increases, number of vehicles on the road increases. This leads to a serious problem called traffic congestion. Predicting traffic congestion is a challenging task. Data Mining analyzes huge data to produce meaningful information to the end users. Classification is a function in data mining which classifies the given data into various classes. Traffic congestion on roads can be classified as free, low, medium, high, very high, and extreme. Congestion on roads is based on the attributes such as speed of the vehicle, density of vehicles on the road, occupation of the road by the vehicles, and the average waiting time of the vehicles. This paper discusses how traffic congestion is predicted using data mining classifiers with big data analytics and compares different classifiers and their accuracy.
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Patricia Annie Jebamalar, J., Paul, S., Ponmary Pushpa Latha, D. (2019). Classifying Road Traffic Data Using Data Mining Classification Algorithms: A Comparative Study. In: Peter, J., Alavi, A., Javadi, B. (eds) Advances in Big Data and Cloud Computing. Advances in Intelligent Systems and Computing, vol 750. Springer, Singapore. https://doi.org/10.1007/978-981-13-1882-5_18
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DOI: https://doi.org/10.1007/978-981-13-1882-5_18
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