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Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays

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  • Binglei Xie

    (School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China)

  • Yu Sun

    (School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China)

  • Xiaolong Huang

    (School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China)

  • Le Yu

    (School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China
    Department of Building and Real Estate and Research Institute of Sustainable Development, The Hong Kong Polytechnic University, Hong Kong 999077, China)

  • Gangyan Xu

    (School of Architecture, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China)

Abstract

As China’s urbanization process continues to accelerate, the demand for intercity residents’ transportation has increased dramatically. Holiday travel has different demand characteristics, causing serious shortage during peak periods. However, current research barely focuses on the passenger flow prediction along with travel characteristics of intercity shuttles. Accurately predicting passenger flow during the holidays helps to improve operational organization efficiency and residents’ satisfaction, and provides a basis for reasonable resource allocation by the management department. This paper analyzes the spatiotemporal characteristics of intercity shuttles passenger flow in the Pearl River Delta. Separate passenger flow prediction models on non-holiday and holiday are established using an improved genetic algorithm optimized back propagation neural network (IGA-BPNN) based on the characteristics of passenger flow, and the prediction models are validated based on panel data. The results of weekly flow show obvious holiday characteristics, and the hourly traffic flow of holidays is much larger than that of weekends and weekdays. There is a significant difference in the hourly flow between different holidays. The IGA-BPNN model used in this paper achieves lower prediction error relative to the benchmark BPNN approach (leads a two thirds reduction in MAPE, and an over 85% reduction in MSPE).

Suggested Citation

  • Binglei Xie & Yu Sun & Xiaolong Huang & Le Yu & Gangyan Xu, 2020. "Travel Characteristics Analysis and Passenger Flow Prediction of Intercity Shuttles in the Pearl River Delta on Holidays," Sustainability, MDPI, vol. 12(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7249-:d:408834
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    References listed on IDEAS

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    1. Fahad Alrukaibi & Rushdi Alsaleh & Tarek Sayed, 2019. "Applying Machine Learning and Statistical Approaches for Travel Time Estimation in Partial Network Coverage," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    2. Chang-Jui Lin & Hsueh-Fang Chen & Tian-Shyug Lee, 2011. "Forecasting Tourism Demand Using Time Series, Artificial Neural Networks and Multivariate Adaptive Regression Splines:Evidence from Taiwan," International Journal of Business Administration, International Journal of Business Administration, Sciedu Press, vol. 2(2), pages 14-24, May.
    3. Claveria, Oscar & Torra, Salvador, 2014. "Forecasting tourism demand to Catalonia: Neural networks vs. time series models," Economic Modelling, Elsevier, vol. 36(C), pages 220-228.
    4. Shu-Chuan Chen & Shih-Yao Kuo & Kuo-Wei Chang & Yi-Ting Wang, 2012. "Improving the forecasting accuracy of air passenger and air cargo demand: the application of back-propagation neural networks," Transportation Planning and Technology, Taylor & Francis Journals, vol. 35(3), pages 373-392, April.
    5. Zhang, Kequan & Qu, Zongxi & Dong, Yunxuan & Lu, Haiyan & Leng, Wennan & Wang, Jianzhou & Zhang, Wenyu, 2019. "Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 814-830.
    6. Chang-Jui Lin & Tian-Shyug Lee, 2013. "Tourism Demand Forecasting: Econometric Model based on Multivariate Adaptive Regression Splines, Artificial Neural Network and Support Vector Regression," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 3(6), pages 1-1.
    7. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, MIR Center for Socio-Economic Research, vol. 4(3), pages 12-28, March.
    8. Dr. Murat çuhadar & Iclal Cogurcu & Ceyda Kukrer, 2014. "Modelling and Forecasting Cruise Tourism Demand to Izmir by Different Artificial Neural Network Architectures," International Journal of Business and Social Research, LAR Center Press, vol. 4(3), pages 12-28, March.
    9. Wu, Weitiao & Li, Peng & Liu, Ronghui & Jin, Wenzhou & Yao, Baozhen & Xie, Yuanqi & Ma, Changxi, 2020. "Predicting peak load of bus routes with supply optimization and scaled Shepard interpolation: A newsvendor model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
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    2. Yang, Hu & Lv, Sirui & Guo, Bao & Dai, Jianjun & Wang, Pu, 2024. "Uncovering spatiotemporal human mobility patterns in urban agglomerations: A mobility field based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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