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23 November 2022 Research on the short-time passenger flow forecast for urban rail transit based on fractal interpolation
Zeyu Du, Jinjin Tang
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Proceedings Volume 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022); 123023M (2022) https://doi.org/10.1117/12.2645470
Event: Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 2022, Guangzhou, China
Abstract
With the continuous expansion of the operation scale of urban rail transit, the urban rail transit shows an increase in the ratio of transfer passenger flow, the superposition of passenger flow between lines is apparent, and the changes in passenger demand are complex, making it difficult for operation managers to grasp the variation of passenger flow accurately. Under the current passenger flow management and operation conditions, accurate short-time passenger flow forecast data could help improve the management and operation level of urban rail transit and passenger travel experience. This paper first analyzes the demand for short-time passenger flow forecast of urban rail transit and the short-time passenger flow forecast process. Secondly, because of the nonlinear characteristics of passenger flow in different periods of a single day, and based on analyzing the historical passenger flow changes, the paper proposes a short-time passenger flow forecast method based on fractal interpolation theory. The passenger flow curve calculated by this method is more in line with the actual situation. Finally, the accurate passenger flow data of several stations in a city in western China is used for verification, and the forecast accuracy is about 96.63%, which proves the feasibility and accuracy of the method.
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Zeyu Du and Jinjin Tang "Research on the short-time passenger flow forecast for urban rail transit based on fractal interpolation", Proc. SPIE 12302, Seventh International Conference on Electromechanical Control Technology and Transportation (ICECTT 2022), 123023M (23 November 2022); https://doi.org/10.1117/12.2645470
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KEYWORDS
Fractal analysis
Iterated function systems
Silicon
Visualization
Statistical analysis
Autoregressive models
Neural networks
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