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Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs

Author

Listed:
  • Yujie Guo

    (Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA)

  • Ying Chen

    (Department of Civil and Environmental Engineering, Northwestern University, Evanston, IL 60208, USA)

  • Yu Zhang

    (Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, USA)

Abstract

Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models.

Suggested Citation

  • Yujie Guo & Ying Chen & Yu Zhang, 2024. "Enhancing Demand Prediction: A Multi-Task Learning Approach for Taxis and TNCs," Sustainability, MDPI, vol. 16(5), pages 1-14, March.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:2065-:d:1349897
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    References listed on IDEAS

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    1. Yang, Zhuo & Franz, Mark L. & Zhu, Shanjiang & Mahmoudi, Jina & Nasri, Arefeh & Zhang, Lei, 2018. "Analysis of Washington, DC taxi demand using GPS and land-use data," Journal of Transport Geography, Elsevier, vol. 66(C), pages 35-44.
    2. Judd Cramer & Alan B. Krueger, 2016. "Disruptive Change in the Taxi Business: The Case of Uber," American Economic Review, American Economic Association, vol. 106(5), pages 177-182, May.
    3. Dong, Xianlei & Zhang, Min & Zhang, Shuang & Shen, Xinyi & Hu, Beibei, 2019. "The analysis of urban taxi operation efficiency based on GPS trajectory big data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 528(C).
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