Abstract
Traffic flow prediction has become a hot spot in the intelligent transportation system study and is attracting more and more researchers. Short-term traffic flow prediction is an important issue of Traffic flow prediction. In this paper, ARIMA (Auto-Regressive Integrated Moving Average) model is used to predict short-term traffic flow of subway. We focus on selecting the most appropriate parameters - p and q of ARIMA model through Stationarity Test, Model Recognition, Parameter Estimation, Model Diagnosis and Prediction except a single AIC (Akaike Information Criterion) estimation or single SACF (Sample Auto-Correlation Function) plots. And then, we predict passenger flow of five subway stations of Guangzhou Metro using presented method and SVM (Support Vector Machine). The experiments’ results show that ARIMA model performs better than SVM, AIC or SACF&SPACF (Sample partial Auto-Correlation Function) in short-term traffic flow prediction.
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Acknowledgments
This paper is supported by “National 863 project (No. 2015AA050204)” and “State Grid Corporation project (No. 520626170011)”. We would like to thank Guangzhou Metro Operation Department.
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Yan, D., Zhou, J., Zhao, Y., Wu, B. (2018). Short-Term Subway Passenger Flow Prediction Based on ARIMA. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-13-0893-2_49
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DOI: https://doi.org/10.1007/978-981-13-0893-2_49
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