Abstract
Real-time road condition prediction based on GPS big data plays a very important role in traffic management. Based on a large number of taxi GPS data and accurately predict the passengers point of problem, using historical taxi GPS data to predict the fact that the speed and comparing with the real speed, so as to realize the analysis of urban traffic in real time, and discusses its implementation in K - means algorithm, in data cleaning, data clustering, visual display. The research work for urban traffic points, data mining and forecasting analysis for the passenger transportation and distribution of public resources, is to reduce the taxi empty loading rate and improve the traffic efficiency.
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Wang, J., Zhang, L., Wang, L., Yan, K., Diao, Y., Zhao, Z. (2021). The Prediction Analysis of Passengers Boarding and Alighting Points Based on the Big Data of Urban Traffic. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_3
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DOI: https://doi.org/10.1007/978-3-030-62743-0_3
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