Short-time traffic flow prediction based on GRA-SLSTM model

Zhizhu Wu; Mingxia Huang; Tao Yang

doi:10.1117/12.3024202

14 February 2024 Short-time traffic flow prediction based on GRA-SLSTM model

Zhizhu Wu, Mingxia Huang, Tao Yang

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130181A (2024) https://doi.org/10.1117/12.3024202
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

This paper proposes a new traffic flow prediction method, which combines the grey relational analysis method with the long and short term memory network, and uses Python language to build the model. Taking California expressway network PEMS data set as the research object, the speed and occupancy of target detection points, as well as the traffic flow of surrounding detection points as influencing factors, are input into the traffic flow prediction model, so as to take into account the influence of multidimensional spatiotemporal factors on the prediction model. Specifically, grey correlation analysis GRA is used as the correlation analysis method of each detection point.^[1]-[4] By eliminating the traffic data with little or no correlation to the traffic flow of the target detection point, the data dimension of the input of the prediction model is reduced, and the efficiency of model training and prediction is improved. ^[5]-[7] Then, constructs a traffic flow prediction model based on stacked LSTM using several LSTM networks to capture the spatiotemporal characteristics of road traffic conditions that are strongly coupled in multidimensional traffic data types and massive data, thereby improving the effectiveness and accuracy of traffic state prediction.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhizhu Wu, Mingxia Huang, and Tao Yang "Short-time traffic flow prediction based on GRA-SLSTM model", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130181A (14 February 2024); https://doi.org/10.1117/12.3024202

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KEYWORDS

Data modeling

Education and training

Performance modeling

Target detection

Neural networks

Neurons

Mathematical modeling

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