Data-Driven Multi-Horizon Taxi Demand Forecasting Using Transformer-Based Temporal Modeling

Abstract

: Accurate short-term taxi demand forecasting is essential for efficient urban transportation, as it directly influences fleet allocation, passenger waiting times, and quality of service. This work studies multi-horizon taxi demand prediction as the task of learning a mapping from past observations to multiple future demand values under temporal dependence and uncertainty. A data-driven framework is developed using data from the New York City Taxi and Limousine Commission, where raw trip records are transformed into structured temporal representations through lag-based features, cyclical encoding, and statistical summaries. The framework evaluates sequence modeling architectures for multi-step prediction, such as recurrent models, in particular Long Short-Term Memory (LSTM) networks, and attention-based models, i.e., Transformer architectures. Evaluation across multiple forecasting horizons reveals that attention-based models consistently achieve lower prediction error, indicating their capability to capture the long-term temporal dependencies needed to predict multiple horizons. Conformal prediction and Monte Carlo Dropout are used to produce calibrated prediction intervals and point forecasts that represent demand variability under uncertainty. The findings indicate that successful multi-horizon forecasting requires global temporal interaction and explicit quantification of uncertainty. The proposed framework provides a systematic and scalable approach for real-world deployment in intelligent transportation systems.