Short-Term Traffic Flow Prediction Based on Road Network Topology

Feng Jin*, Baicheng Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Accurate short-term traffic flow prediction plays a crucial role in intelligent transportation system (ITS), because it can assist both traffic authorities and individual travelers make better decisions. Previous researches mostly focus on shallow traffic prediction models, which performances were unsatisfying since short-term traffic flow exhibits the characteristics of high nonlinearity, complexity and chaos. Taking the spatial and temporal correlations into consideration, a new traffic flow prediction method is proposed with the basis on the road network topology and gated recurrent unit (GRU). This method can help researchers without professional traffic knowledge extracting generic traffic flow features effectively and efficiently. Experiments are conducted by using real traffic flow data collected from the Caltrans Performance Measurement System (PEMS) database in San Diego and Oakland from June 15, 2017 to September 27, 2017. The results demonstrate that our method outperforms other traditional approaches in terms of mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) and root mean square error (RMSE).

Original languageEnglish
Pages (from-to)383-388
Number of pages6
JournalJournal of Beijing Institute of Technology (English Edition)
Volume28
Issue number3
DOIs
Publication statusPublished - 1 Sept 2019

Keywords

  • Gated recurrent unit (GRU)
  • Intelligent transportation systems
  • Road network topology
  • Traffic flow prediction

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