Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm

Abstract

Exploring traffic flow characteristics and predicting its variation patterns are the basis of Intelligent Transportation Systems. The intermittent characteristics and intense fluctuation on short-term scales make it a significant challenge on urban roads. A hybrid model, Genetic Algorithm with Attention-based Long Short-Term Memory (GA-LSTM), combining with spatial–temporal correlation analysis, is proposed in this study to predict traffic volumes on urban roads. The spatial correlation is captured by combining the volume transition matrix estimated from vehicle trajectories and network weight matrix quantified from different detectors. The temporal dependency is explored by the attention mechanism, and we introduce the Genetic Algorithm to optimize it. In the experiment, traffic flow data collected from License Plate Recognition (LPR), is utilized to validate the effectiveness of model. The comparison is conducted with several traditional models to show the superiority of the proposed model with higher accuracy and better stability.

KEYWORDS:

• Traffic flow prediction
• urban road network
• spatial–temporal correlation
• Genetic Algorithm
• attention–LSTM
• License Plate Recognition data

Acknowledgments

The research is funded by the National Key R&D Program of China (No. 2020YFB1600400), Natural Science Foundation of Hunan Province (No. 2020JJ4752), Innovation-Driven Project of Central South University (No.2020CX041), Foundation of Central South University (No.502045002), Postdoctoral Science Foundation of China (No.2018M630914 and 2019T120716).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71701215].