Abstract
The advent of the big data era has stimulated the collection, processing, and analysis of traffic data. This article proposes a deep learning-based models for vehicle speed prediction and improves it using three methods: BPNN (Backpropagation Neural Network), BPTT (BP Through Time), and LSTM (Long Short-Term Memory). The three methods are trained using different data volumes, including one week, one month, and two months. The effects of different data volumes on the different methods are compared with BPNN as the baseline. Additionally, it is worth noting that the experimental code involved in this paper does not utilize any existing libraries. Finally, the prediction results of the three methods are evaluated using RMSE (Root Mean Square Error) and MAE (Mean Absolute Error). The results show that when using one month of data volume, the difference between the three methods is more obvious, and BPTT outperforms BPNN and LSTM. Compared with BPNN, BPTT reduces RMSE by 2.68% and MAE by 0.86%.
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Xiao, Y., Fan, X., Lu, Y., Xue, J. (2024). Vehicle Speed Prediction Based on Deep Learning. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_5
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DOI: https://doi.org/10.1007/978-981-97-2757-5_5
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