Affiliation:
1. School of Automation, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China
Abstract
Accurate traffic flow forecasting is very important for urban planning and traffic management. However, this is a huge challenge due to the complex spatial–temporal relationships. Although the existing methods have researched spatial–temporal relationships, they neglect the long periodic aspects of traffic flow data, and thus cannot attain a satisfactory result. In this paper, we propose a novel model Attention-Based Spatial–Temporal Convolution Gated Recurrent Unit (ASTCG) to solve the traffic flow forecasting problem. ASTCG has two core components: the multi-input module and the STA-ConvGru module. Based on the cyclical nature of traffic flow data, the data input to the multi-input module are divided into three parts, near-neighbor data, daily-periodic data, and weekly-periodic data, thus enabling the model to better capture the time dependence. The STA-ConvGru module, formed by CNN, GRU, and attention mechanism, can capture both temporal and spatial dependencies of traffic flow. We evaluate our proposed model using real-world datasets and experiments show that the ASTCG model outperforms the state-of-the-art model.
Funder
Natural Science Foundation of Hubei Province
Subject
General Physics and Astronomy
Reference48 articles.
1. Data-Driven Intelligent Transportation Systems: A Survey;Zhang;IEEE Trans. Intell. Transp. Syst.,2011
2. Synthesis of short-term traffic flow forecasting research progress;Yuan;Urban Transp. China,2012
3. Performance evaluation of short-term time-series traffic prediction model;Ishak;J. Transp. Eng.,2002
4. Performance Analysis of Corroded Grounding Devices with an Accurate Corrosion Model;Dan;CSEE J. Power Energy Syst.,2023
5. A Summary of Traffic Flow Forecasting Methods;Liu;J. Highw. Transp. Res. Dev.,2004