Shared mobility demand prediction via A fast spatiotemporal tensor autoregression

Abstract

Shared mobility is critical to urban transportation, yet its complex spatiotemporal dynamics challenge traditional prediction methods. We propose the Tucker Decomposition-based Spatiotemporal Tensor Autoregressive Model (T-STAR), which leverages tensor-structured data modeling and Tucker decomposition to efficiently capture multi-dimensional dependencies. Unlike conventional methods, T-STAR preserves high-dimensional structures by decomposing raw spatiotemporal data into a low-rank core tensor and mode-specific factor matrices, reducing complexity and enhancing interpretability by decoupling spatial, temporal, and modal interactions. Experimental results on three benchmark datasets demonstrate T-STAR's strong performance. On the Beijing Taxi Trajectory Dataset (TaxiBJ), T-STAR achieves Mean Absolute Error (MAE) of 23.53 and Root Mean Square Error (RMSE) of 37.71, improving performance by 18.5 % and 21.2 % over baseline averages. On the New York City Taxi Dataset (NYCtaxi), it records MAE of 18.18 and RMSE of 46.87, reducing errors by 22.7 % and 15.4 %. In the sparse-demand New York City Bike-Sharing Dataset (NYCbike), it maintains robust accuracy with MAE of 7.95 and RMSE of 14.32, outperforming baselines by 14.1 % and 17.9 %, respectively. Most notably, T-STAR achieves these results at high speed: on TaxiBJ, it completes a prediction in just 0.35 seconds–87 % faster than the Adaptive Graph Convolutional Recurrent Network (AGCRN) and 99.8 % faster than the Diffusion Convolutional Recurrent Neural Network (DCRNN). By retaining over 95 % of key spatiotemporal correlations through Tucker compression, T-STAR reduces prediction error by 20–30 % while delivering real-time performance, offering a scalable framework for urban traffic prediction and shared vehicle scheduling. Code and data are both available at yanhongyu0/TSTAR (github.com)