STEI-PCN: an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring

TL;DR

The paper presents elsarticle.cls, a LaTeX document class engineered for Elsevier submissions, built on article.cls to minimize package conflicts and ensure compatibility with common tooling such as natbib and hyperref. It contrasts elsarticle.cls with the older elsart.cls, outlining architectural differences, default formatting options, and flexible citation and layout capabilities for preprint and final formats. Installation guidance is provided, including download sources, file generation from dtx/ins, local TEXMF placement, and the necessity to refresh the file database, alongside usage instructions for loading the class with various submission models. This work streamlines the workflow for authors preparing manuscripts for Elsevier journals by delivering a robust, widely compatible document class with configurable formatting options.

Abstract

Traffic data exhibits complex temporal, spatial, and spatial-temporal correlations. Most of models use either independent modules to separately extract temporal and spatial correlations or joint modules to synchronously extract them, without considering the spatial-temporal correlations. Moreover, models that consider joint spatial-temporal correlations (temporal, spatial, and spatial-temporal correlations) often encounter significant challenges in accuracy and computational efficiency which prevent such models from demonstrating the expected advantages of a joint spatial-temporal correlations architecture. To address these issues, this paper proposes an efficient pure convolutional network for traffic prediction via spatial-temporal encoding and inferring (STEI-PCN). The model introduces and designs a dynamic adjacency matrix inferring module based on absolute spatial and temporal coordinates, as well as relative spatial and temporal distance encoding, using a graph convolutional network combined with gating mechanism to capture local synchronous joint spatial-temporal correlations. Additionally, three layers of temporal dilated causal convolutional network are used to capture long-range temporal correlations. Finally, through multi-view collaborative prediction module, the model integrates the gated-activated original, local synchronous joint spatial-temporal, and long-range temporal features to achieve comprehensive prediction. This study conducts extensive experiments on flow datasets (PeMS03/04/07/08) and speed dataset (PeMS-Bay), covering multiple prediction horizons. The results show that STEI-PCN demonstrates competitive computational efficiency in both training and inference speeds, and achieves superior or slightly inferior to state-of-the-art (SOTA) models on most evaluation metrics.