A Transformer-Based Method for Short-Term Urban Ride-Hailing Demand Forecasting with Multi-Factor Features

Abstract

For urban ride-hailing services, accurate shortterm demand forecasting is essential for improving vehicle dispatching efficiency, reducing passenger waiting times, and supporting sustainable and intelligent urban mobility management. Short-term ride-hailing demand is shaped by complex couplings among historical demand, temporal regularity, and weather, which makes accurate prediction challenging. Unlike most existing ride-hailing forecasters that rely on graph-intensive or task-specific architectures, this paper proposes a concise Transformer-based method whose unique contribution to ridehailing demand prediction lies in two aspects: (i) a compact multifactor feature set tailored to short-term ride-hailing fluctuation, integrating historical demand, fine-grained temporal attributes, and weather variables; and (ii) explicit modelling of the nonlinear and non-monotonic effects of extreme weather and rainfall intensity on demand within a single Transformer encoder. Experiments on the City of Chicago Transportation Network Providers (TNP) dataset show that the proposed method achieves an MAE of 14.8, an RMSE of 20.1, and a MAPE of 12.1%, outperforming LSTM, Bi-LSTM, and GRU on all three metrics, with relative reductions of 20.9% in MAE, 19.3% in RMSE, and 23.4% in MAPE versus LSTM. These results demonstrate that a concise Transformer with carefully designed multi-factor features can serve as an effective, reproducible baseline for short-term ridehailing demand forecasting, supporting smarter vehicle allocation, lower idle mileage, and reduced energy consumption and carbon emissions in intelligent transportation systems.