Incremental Gate State Output Decomposition Model for Highway Traffic Forecasting Using Toll Collection Data

Authors

  • Liang Yu Jiangxi Traffic Monitoring Command Center, Nanchang, China
  • Ming Li Jiangxi Transportation Institute Co., Ltd, Nanchang, China
  • Kaifeng Liu Hangzhou iGreenTrans Co., Ltd., Hangzhou, Zhejiang, China
  • Xiangping Cheng Jiangxi Academy of Sciences, Nanchang, China

DOI:

https://doi.org/10.5755/j02.eie.38471

Keywords:

Highway traffic prediction, Incremental gate state output decomposition, Self-attention, Toll collection data

Abstract

Traffic flow on long-distance highways, especially at sections with multi-interchanges and ramps, exhibits nonlinear trends affected by long-term and short-term spatiotemporal dependencies, resulting limited fitting capabilities for the major applied spatiotemporal forecasting models in use. This paper tackles this challenge by integrating an incremental gate state output decomposition (IGOD) mechanism into the recurrent neural network (RNN) model framework, accounting for the interdependencies of spatiotemporal traffic data. The proposed method improves the ability of the RNN model to estimate traffic data series by segmenting consecutive time intervals and accumulating incremental changes across these time intervals, allowing for more precise traffic predictions. This study also explores how threshold amplitudes affect prediction effectiveness. We applied it to real traffic data from segment k602+630 to k625+420 on the Changjiu Highway. The results demonstrate that the proposed model consistently exhibits robustness, with variations in threshold magnitude having little impact on its prediction accuracy.

Downloads

Published

2025-02-24

How to Cite

Yu, L., Li, M., Liu, K., & Cheng, X. (2025). Incremental Gate State Output Decomposition Model for Highway Traffic Forecasting Using Toll Collection Data. Elektronika Ir Elektrotechnika, 31(1), 4-11. https://doi.org/10.5755/j02.eie.38471

Issue

Section

AUTOMATION, ROBOTICS

Funding data