Short term road network Macroscopic Fundamental Diagram parameters and traffic state prediction based on LSTM

Authors

DOI:

https://doi.org/10.61089/aot2024.qxx9rh86

Keywords:

multi-objective optimization, macroscopic fundamental diagram, LSTM, traffic prediction

Abstract

Macroscopic Fundamental Diagram (MFD) is widely used in traffic state evaluation due to its description of the macro level of urban road network. This study focuses on the discrimination and short-term prediction of macro traffic states in urban road networks, using MFD combined with FCM clustering for state partitioning to characterize different macro states of the road network. To predict the MFD state, this paper builds two LSTMs to perform short-term predictions on two important parameters in MFD: road network weighted flow qw and road network weighted density kw. The parameters of the first three statistical intervals and the predicted time period are used as inputs to output the MFD parameters for the predicted time period. To ensure that the two LSTM structures and hyper-parameters settings can achieve the best prediction performance for MFD, the parameter optimization process of both should be included in the same search framework for hyper-parameters search. Therefore, this paper uses GA algorithm combined with multi-objective particle swarm optimization algorithm as the solving algorithm, with the accuracy of solving two MFD parameters and the accuracy of MFD point positioning as the hyper-parameters solving objectives. The study was validated using actual road network data from Hong Kong, and the results showed that the method proposed in this paper has an MRE prediction error of less than 7.8% for the two parameters of MFD, and can predict the future temporal trend of the two parameters, demonstrating the feasibility of MFD related predictions. The model's prediction of the overall shape and change trajectory trend of MFD is consistent with reality, and some test sets in MFD state prediction show high accuracy, the overall accuracy is 81.45%. To verify the effectiveness of the multi-objective search algorithm, typical LSTM models and RNN models were used for comparison. The experiment proved that the model used in this study performed better in error control and state prediction. This study explores and practices a short-term prediction method for road network MFD parameters, MFD status, and their changing trends. Provided path reference for urban road network prediction, traffic control status, and MFD related research.

Author Biographies

  • Xuanhua Lin, College of Intelligent Transportation Engineering, Guangdong Communication Polytechnic, Guangzhou, China

    Teaching in the Intelligent Transportation Technology major at Guangdong Communication Polytechnical,  teacher, master's degree, primary title.

    Participated in multiple projects funded by the National Natural Science Foundation of China and the Provincial Natural Science Foundation of China, and led research projects in provincial universities.

  • Chaojian TAN, College of Intelligent Transportation Engineering, Guangdong Communication Polytechnic, Guangzhou, China

    Teaching in the Intelligent Transportation Technology major at Guangdong Communication Polytechnical, practical training teacher, intermediate professional title

  • Xiaohui LIN, College of Intelligent Transportation Engineering, Guangdong Communication Polytechnic, Guangzhou, China

    Teaching at the Intelligent Transportation Technology major of Guangdong Communication Polytechnic, Professor, Director of the Academic Affairs Office of the university, senior professional title, doctoral degree.Hosted two projects funded by the Natural Science Foundation of China and published more than 10 indexed articles in EI and SCI. Has multiple invention patents. Academic achievements have been included in the provincial scientific and technological achievements.

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Published

2024-09-30

Data Availability Statement

The raw data used in this article are all from the Hong Kong government's open dataset (https://data.gov.hk/sc/), which can be downloaded from its website or through API connections. Further processing is required to obtain MFD data.

Issue

Section

Original articles

How to Cite

Xuanhua Lin, Chaojian TAN, & Xiaohui LIN. (2024). Short term road network Macroscopic Fundamental Diagram parameters and traffic state prediction based on LSTM. Archives of Transport, 71(3), 107-126. https://doi.org/10.61089/aot2024.qxx9rh86

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