SAE for the prediction of road traffic status from taxicab operating data and bus smart card data

Huang Zhengfeng, Zheng Pengjun, Xu Wenjun () and Ren Gang
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Huang Zhengfeng: Faculty of Maritime and Transportation, Ningbo University, P. R. China†National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, P. R. China‡Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, P. R. China
Zheng Pengjun: Faculty of Maritime and Transportation, Ningbo University, P. R. China†National Traffic Management Engineering & Technology Research Center, Ningbo University Sub-Center, P. R. China‡Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, P. R. China§Transportation Research Group, University of Southampton, UK
Xu Wenjun: Faculty of Maritime and Transportation, Ningbo University, P. R. China
Ren Gang: #x2021;Jiangsu Province Collaborative Innovation Center for Modern Urban Traffic Technologies, P. R. China¶School of Transportation, Southeast University, P. R. China

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 10, 1-10

Abstract: Road traffic status is significant for trip decision and traffic management, and thus should be predicted accurately. A contribution is that we consider multi-modal data for traffic status prediction than only using single source data. With the substantial data from Ningbo Passenger Transport Management Sector (NPTMS), we wished to determine whether it was possible to develop Stacked Autoencoders (SAEs) for accurately predicting road traffic status from taxicab operating data and bus smart card data. We show that SAE performed better than linear regression model and Back Propagation (BP) neural network for determining the relationship between road traffic status and those factors. In a 26-month data experiment using SAE, we show that it is possible to develop highly accurate predictions (91% test accuracy) of road traffic status from daily taxicab operating data and bus smart card data.

Keywords: Stacked autoencoders; traffic status; multi-modal trip data (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1142/S0129183117501212

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