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Short-term demand forecasting for online car-hailing using ConvLSTM networks

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  • Lu, Xijin
  • Ma, Changxi
  • Qiao, Yihuan

Abstract

This paper used the previous data of online car-hailing orders in Haikou provided by Didi Chuxing GAIA Initiative to predict the short-term demand for online car-hailing service. This paper contains two main steps. The first step is about converting online car-hailing demand for images that contains spatiotemporal feature of online car-hailing orders. This paper draws a picture every 72 min from 2017∕5∕8 to 2017∕8∕8, with a total of 1,840 binary vector images. The second step is to employ the deep learning method of a Conv-LSTM neutral network to the image for online car-hailing demand prediction. Conv-LSTM has excellent image prediction properties, so it is ideal for predicting such binary vector figures with spatiotemporal information. After learning the first 1460 images, the last 380 images were simulated, predicted and tested. Finally, the simulation results of the last 20 images were taken as the effect of the model. Our result shows that the Conv-LSTM neutral network can train the model with a reasonable output and is suitable for short-term forecasting network ride-hailing demand forecast with spatiotemporal feature information. By comparing five different training session times, it can be seen that when the number of training session reaches 30, the model reaches an optimum. Reasonable prediction results can provide data support for vehicle dispatching and distribution, solve problems such as energy waste and traffic congestion caused by asymmetric supply and demand, and maximize the interests of passengers, drivers and ride-hailing platforms.

Suggested Citation

  • Lu, Xijin & Ma, Changxi & Qiao, Yihuan, 2021. "Short-term demand forecasting for online car-hailing using ConvLSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    • Handle: RePEc:eee:phsmap:v:570:y:2021:i:c:s0378437121001102
    DOI: 10.1016/j.physa.2021.125838
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    References listed on IDEAS

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    Cited by:

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    2. Zhenbao Wang & Xin Gong & Yuchen Zhang & Shuyue Liu & Ning Chen, 2023. "Multi-Scale Geographically Weighted Elasticity Regression Model to Explore the Elastic Effects of the Built Environment on Ride-Hailing Ridership," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    3. Liu, Baoju & Tang, Zhongan & Deng, Min & Shi, Yan & He, Xiao & Huang, Bo, 2024. "Estimation of travel flux between urban blocks by combining spatio-temporal and purpose correlation," Journal of Transport Geography, Elsevier, vol. 116(C).
    4. Qianru Qi & Rongjun Cheng & Hongxia Ge, 2022. "Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    5. Huang, Zhiwen & Li, Tong & Huang, Kexin & Ke, Hanbing & Lin, Mei & Wang, Qiuwang, 2022. "Predictions of flow and temperature fields in a T-junction based on dynamic mode decomposition and deep learning," Energy, Elsevier, vol. 261(PA).
    6. Ma, Changxi & Zhao, Mingxi, 2023. "Spatio-temporal multi-graph convolutional network based on wavelet analysis for vehicle speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).

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