Wavelet‐attention‐based traffic prediction for smart cities. Issue 1 (25th November 2021)

• Record Type: Journal Article
• Authors: Nasser, Aram; Simon, Vilmos
• Is Part Of: IET smart cities. Volume 4:Issue 1(2022)
• Page Start: 3
• Page End: 16
• Publication Date: 2021-11-25
• Subjects: signal processing -- smart cities -- traffic prediction -- wavelet transform; Smart cities -- Periodicals; City planning -- Technological innovations -- Periodicals; Cities and towns -- Growth -- Periodicals; Sustainable urban development -- Periodicals; Sustainable development; City planning -- Technological innovations; Cities and towns -- Growth; Periodicals; 307.76
• DOI: 10.1049/smc2.12018
• Languages: English
• ISSNs: 2631-7680

Abstract

Abstract: Traffic congestion is a problem facing today's world, especially in smart cities where the economy is booming. Solving this issue by upgrading the traffic infrastructure of the city might be very cost‐inefficient as well as time‐consuming. With the help of recent technologies, traffic can be predicted to give the authorities the time to react before congestion evolves. As traffic is affected by several external factors, such as weather and anomalies (accidents, not expected road closures etc.), understanding the relationship between traffic and these factors can improve the prediction even further. In this study, a new method, the weather‐based traffic analysis (hereafter WBTA), is utilised to investigate the temporal correlations between the traffic flow and the exogenous weather factors at different frequencies and time intervals. In addition, a novel method, the wavelet‐attention‐based calculation (hereafter WABC) is introduced to help to understand the importance of each external factor, compared with the others. Five weather factors (temperature, wind speed, rain, visibility, and humidity) are analysed, weighted, and merged with each other as one auxiliary input to improve traffic prediction accuracy. Based on that, the wavelet‐attention‐based prediction model is introduced, where the mean squared error is reduced by 32.3% and 24.52% for one future time step prediction, and 14.9% and 18.22% for five, compared with using the traffic time series alone, and with external factors without weights, respectively.