Efficient implementation of a wavelet neural network model for short-term traffic flow prediction: Sensitivity analysis

The concept of a smart city has emerged to address significant challenges arising from rapid urbanization, economic growth, and climate change. Innovative technology solutions can be used as a means to promote sustainable and inclusive urban development. Effective strategies such as the deployment of the internet of things (IoT), artificial intelligence (AI), energy management, and smart transportation. In the smart city, intelligent transportation systems (ITS) are playing a vital role in efficient traffic management. This paper explores the use of hybrid artificial intelligence techniques for predicting short-term traffic flow data from M25 motorways in the UK. Since volume traffic flow data are non-stationary, wavelet transform (WT) as a powerful signal analyzer is applied for signal decomposition for the elimination of redundant data from input matrices. The feature selection method based on Gram-Schmidt (GS) is used for the selection of more valuable features. The elimination of redundant data can speed up the learning process and improve the generalisation capability of the prediction models. After a pre-processing stage, a wavelet neural network (WNN) with a simple structure is applied as a powerful prediction tool. Two separate structures are considered for the prediction of weekday and weekend traffic volume data. The experiments explore that the debauchies-4 (db4) wavelet function with 7 decomposition levels leads to the best detection accuracy. Moreover, the range of forecasting, the type of the day, the level of decomposition, and other factors all have an impact on prediction stability. Compared with existing prediction methods, the proposed approach produces lower values of root mean square error (RMSE) and mean absolute percentage error (MAPE) for all step-horizons analyzed. These findings provide valuable implications and insights into the development of an efficient and reliable road condition monitoring system for delivering secure and sustainable transportation services.

• Record URL:
  • https://doi.org/10.1016/j.ijtst.2024.02.004
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S2046043024000145
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/20460430
• Supplemental Notes:
  • © 2024 Tongji University and Tongji University Press. Publishing Services by Elsevier B.V. Abstract reprinted with permission of Elsevier.
• Authors:
  • Mrad, Sonia
  • Mraihi, Rafaa
  • Murthy, Aparna S
• Publication Date: 2025-3

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 21-38
• Serial:
  • International Journal of Transportation Science and Technology
  • Volume: 17
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2046-0430
  • Serial URL: http://www.sciencedirect.com/science/journal/20460430

Subject/Index Terms

• TRT Terms: Intelligent transportation systems; Neural networks; Predictive models; Sensitivity analysis; Traffic forecasting; Wavelets
• Geographic Terms: United Kingdom
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01912705
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 21 2024 11:04AM