Traffic flow forecasting in smart cities with deep learning

Yihan Wang

doi:10.1117/12.3054688

20 December 2024 Traffic flow forecasting in smart cities with deep learning

Yihan Wang

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 134214W (2024) https://doi.org/10.1117/12.3054688
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

Traffic flow prediction is a method of processing traffic data. Accurate traffic prediction information can provide a strong basis for traffic management decisions and can also allow drivers to choose more fluent routes for travel, thus avoiding or alleviating traffic congestion. Traditional traffic flow prediction data usually consists of vehicle speed and travel trajectories. Researchers obtain data by arranging traffic sensors at intervals along highways, and these methods have achieved good results when applied to suburban areas and highways. However, urban roads are densely populated and have complex traffic conditions, which are not suitable for the large-scale deployment of sensors to obtain the required traffic data, and therefore existing methods cannot be used for prediction. In response to this problem, we have attempted to use five models: SMA (Simple Moving Average), ARIMA (Autoregressive Integrated Moving Average), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and TCN (Temporal Convolutional Network), to predict the traffic flow at four intersections, and have conducted many experiments based on real traffic datasets. The results show that the TCN prediction model achieved the best forecast results, with RMSE, MAP, and MAPE values of 3.9, 2.3, and 16.1, respectively, indicating the predictive accuracy that can be achieved.

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

Citation Download Citation

Yihan Wang "Traffic flow forecasting in smart cities with deep learning", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 134214W (20 December 2024); https://doi.org/10.1117/12.3054688

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