Abstract
As the development of sharing economics, the sharing bicycles rapidly grow domestically and overseas. However, there are some problems together with the shared bicycles, such as traffic congestion, bicycles are hard to park, etc. This paper will discuss the changes of people’s traffic behaviors without sharing bikes, by combining wavelet analysis with Back Propagation (BP) neural network and established a traffic behavior prediction model based on wavelet analysis method. First, the model selected the BP as network and the Morlet wavelet as the hidden layer excitation function, then take the data of New York City in October 2018 as an example to apply in the prediction model to solve this problem. The simulation results show that the prediction model has accurate precise the urban traffic change trend, with a fast convergence speed, and has high practical application value. Lastly, if there are no shared bicycles, a small number of people will choose to walk or take a taxi, most people are more willing to take the bus to finish the “last mile”.
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Acknowledgement
This work is supported by the Start-up Foundation for Doctors of East China University of Technology (No. DHBK2012201), the Foundation of Jiangxi Educational Committee (GJJ170484), the Science and Technology Foundation of Jiangxi Province (No. 20181BAB202019) and the National Natural Science Foundation of China (Nos. 51567001, 11565002).
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Liu, G., Yi, W., Lin, Z., Chen, Y. (2019). Model Analysis and Prediction of People’s Travel Behaviors Under no Shared Bicycles. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_36
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DOI: https://doi.org/10.1007/978-3-030-26763-6_36
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