# Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web Source: [Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web]() Collection index: [llms.txt]() Data updated: 2026-02-13T05:00:04.183Z Authors: Xixuan Hao, Guicheng Li, Daiqiang Wu, Xusen Guo, Yumeng Zhu, Zhichao Zou, Peng Zhen, Yao Yao, Yuxuan Liang Published: 2026-02-11T04:02:07.000Z ## Abstract The proliferation of ride-hailing services has fundamentally transformed urban mobility patterns, making accurate ride-hailing forecasting crucial for optimizing passenger experience and urban transportation efficiency. However, ride-hailing forecasting faces significant challenges due to geospatial heterogeneity and high susceptibility to external events. This paper proposes MVGR-Net(Multi-View Geospatial Representation Learning), a novel framework that addresses these challenges through a two-stage approach. In the pretraining stage, we learn comprehensive geospatial representations by integrating Points-of-Interest and temporal mobility patterns to capture regional characteristics from both semantic attribute and temporal mobility pattern views. The forecasting stage leverages these representations through a prompt-empowered framework that fine-tunes Large Language Models while incorporating external events. Extensive experiments on DiDi's real-world datasets demonstrate the state-of-the-art performance. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Ride-hailing Call Prediction on Ride-hailing dataset 2025 (test)](): Ride-hailing Call Prediction; Ride-hailing dataset 2025 (test); WMAPE: 0.089. - [Ride-hailing Call Prediction on Ride-hailing dataset 2024 (test)](): Ride-hailing Call Prediction; Ride-hailing dataset 2024 (test); WMAPE: 7.8. - [Ride-hailing Call Prediction on Ride-hailing dataset 2023 (test)](): Ride-hailing Call Prediction; Ride-hailing dataset 2023 (test); WMAPE: 0.088. - [Ride-hailing TSH Prediction on Ride-hailing dataset 2025 (test)](): Ride-hailing TSH Prediction; Ride-hailing dataset 2025 (test); WMAPE: 0.027. - [Ride-hailing TSH Prediction on Ride-hailing dataset 2024 (test)](): Ride-hailing TSH Prediction; Ride-hailing dataset 2024 (test); WMAPE: 2.8. - [Ride-hailing TSH Prediction on Ride-hailing dataset 2023 (test)](): Ride-hailing TSH Prediction; Ride-hailing dataset 2023 (test); WMAPE: 2.8. ## Original sources and implementation [Original paper]() [PDF]()