Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads

• URL: http://arxiv.org/abs/2301.02731v1
• Date: Fri, 6 Jan 2023 22:23:57 GMT
• Title: Attention-LSTM for Multivariate Traffic State Prediction on Rural Roads
• Authors: Elahe Sherafat and Bilal Farooq and Amir Hossein Karbasi and Seyedehsan Seyedabrishami
• Abstract summary: An Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic volume and speed. This study compares the results of the A-LSTM model with the Long Short-Term Memory (LSTM) model.
• Score: 5.259027520298188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate traffic volume and speed prediction have a wide range of applications in transportation. It can result in useful and timely information for both travellers and transportation decision-makers. In this study, an Attention based Long Sort-Term Memory model (A-LSTM) is proposed to simultaneously predict traffic volume and speed in a critical rural road segmentation which connects Tehran to Chalus, the most tourist destination city in Iran. Moreover, this study compares the results of the A-LSTM model with the Long Short-Term Memory (LSTM) model. Both models show acceptable performance in predicting speed and flow. However, the A-LSTM model outperforms the LSTM in 5 and 15-minute intervals. In contrast, there is no meaningful difference between the two models for the 30-minute time interval. By comparing the performance of the models based on different time horizons, the 15-minute horizon model outperforms the others by reaching the lowest Mean Square Error (MSE) loss of 0.0032, followed by the 30 and 5-minutes horizons with 0.004 and 0.0051, respectively. In addition, this study compares the results of the models based on two transformations of temporal categorical input variables, one-hot or cyclic, for the 15-minute time interval. The results demonstrate that both LSTM and A-LSTM with cyclic feature encoding outperform those with one-hot feature encoding.

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