HT-STNet: a hierarchical Tucker decomposition and spatio-temporal LSTM network for accurate and efficient shared mobility demand forecasting on sparse data

Abstract

This study proposes an innovative framework that combines spatiotemporal long- and short-term memory networks (ST-LSTM) with hierarchical Tucker decomposition (HTD), aiming at efficiently processing and predicting complex spatiotemporal data, such as the demand for shared trips. The framework compresses the original tensor data into low-rank cores and factor matrices through a recursive hierarchical decomposition strategy, which not only significantly reduces the storage and computation overheads, but also improves the data processing efficiency, especially in sparse data scenarios showing superior performance. In addition, ST-LSTM achieves accurate modeling of multi-scale features through a lightweight spatio-temporal gating mechanism, capturing the long-term and short-term dependencies in time series.HT-STNet also proposes a dynamic feature selection and gradient masking mechanism, which effectively solves the problem of localized sparsity in traffic data, and avoids redundant computation of invalid information and zero-valued elements through sparsity-aware decomposition of rank adjustment. The experimental results show that HT-STNet outperforms multiple mainstream baseline models in terms of prediction accuracy, computational efficiency, and sparse data processing capability, especially in multi-scale feature extraction and dimensionality reduction. The method provides an efficient and robust solution for high-dimensional spatio-temporal data modeling, which is especially suitable for complex travel demand prediction tasks and breaks through the bottleneck of traditional models in characterizing complex spatio-temporal relationships.