Grid LSTM based Attention Modelling for Traffic Flow Prediction

Biju, Rahul; Goparaju, Sai Usha; Gangadharan, Deepak; Mandal, Bappaditya

Abstract

Traffic flow prediction is an important task that can directly impact the control of traffic flow positively and improve the overall traffic throughput. Although a large number of studies have been performed to improve traffic flow prediction, there are very few works on purely temporal prediction models, which is important for execution on an edge device that does not have access to spatial flow information. In order to explore the temporal prediction models further, we propose an innovative hybrid long short-term memory (LSTM) model, which we call Grid LSTM based Attention Modelling for Traffic Flow Prediction (GLSTM-A), that helps to encode temporal information better at various levels/scenarios. The proposed architecture incorporates a Grid LSTM to capture historical dependencies and a simple LSTM layer dedicated to the short-term analysis of recent data. Moreover, an innovative attention mechanism is designed to focus on the importance of data features automatically for further enhancing the model's predictive capabilities. Our proposed GLSTM-A outperforms other popular temporal prediction models such as temporal convolutional network (TCN), Bi-LSTM and LSTM, in terms of prediction accuracy and memory efficiency as mentioned in the experimental results. Experimental results and ablation studies on benchmark datasets demonstrate the superior performance of the proposed model over existing state-of-the-art models in various time series prediction tasks.

Citation

Biju, R., Goparaju, S. U., Gangadharan, D., & Mandal, B. (2024, June). Grid LSTM based Attention Modelling for Traffic Flow Prediction. Presented at 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), Singapore

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring)
• Start Date: Jun 24, 2024
• Acceptance Date: Jun 1, 2024
• Online Publication Date: Sep 25, 2024
• Publication Date: Jun 24, 2024
• Deposit Date: Oct 18, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 1-7
• DOI: https://doi.org/10.1109/vtc2024-spring62846.2024.10683344
• Keywords: Traffic flow prediction , Temporal Prediction , Grid LSTM , Attention Modelling , Temporal convolutional networks
• Public URL: https://keele-repository.worktribe.com/output/950565