Abstract
Understanding the intensity of traffic congestion and its ensuing effects has a lot of uses that include alleviation in traffic intensity and the economic and social damage caused. The present research is an attempt to analyse data that might be useful in determining the effect on traffic during an accident. It is hypothesised that accurate traffic congestion intensity forecast will empower travellers to choose different routes, departure times and means of transport according to the given conditions. The paper attempts to extend machine learning theory by developing a model to predict traffic congestion intensity caused by road accidents in the US using K-nearest neighbours classification algorithm. A stepwise approach is adopted by taking appropriate data, formatting it, visualising it, creating meaning out of raw data, uncovering new insights and then modelling it. The research concludes that a model may be developed with an accuracy of ~ 68%.
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Mudgil, P., Joshi, I. (2022). Intensity of Traffic Due to Road Accidents in US: A Predictive Model. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_4
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DOI: https://doi.org/10.1007/978-981-16-3071-2_4
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