Estimating the Impact of High-Fidelity Rainfall Data on Traffic Conditions and Traffic Prediction

Accurate prediction of network-level traffic parameters during inclement weather conditions can greatly help in many transportation applications. Rainfall tends to have a quantifiable impact on driving behavior and traffic network performance. This impact is often studied for low-resolution rainfall data on small road networks, whereas this study investigates it in the context of a large traffic network and high-resolution rainfall radar images. First, the impact of rainfall intensity on traffic performance throughout the day and for different road categories is analyzed. Next, it is investigated whether including rainfall information can improve the predictive accuracy of the state-of-the-art traffic forecasting methods. Numerical results show that the impact of rainfall on traffic varies for different rainfall intensities as well as for different times of the day and days of the week. The results also show that incorporating rainfall data into prediction models improves their overall performance. The average reduction in mean absolute percentage error (MAPE) for models with rainfall data is 4.5%. Experiments with downsampled rainfall data were also performed, and it was concluded that incorporating higher resolution weather data does indeed lead to an increase in performance of traffic prediction models.

• Record URL:
  https://doi.org/10.1177/03611981211026309
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • Anatolii Prokhorchuk https://orcid.org/0000-0001-6347-1107 © National Academy of Sciences: Transportation Research Board 2021.
• Authors:
  • Prokhorchuk, Anatolii
  • 0000-0001-6347-1107
  • Mitrovic, Nikola
  • Muhammad, Usman
  • Stevanovic, Aleksandar
  • 0000-0003-1091-3340
  • Asif, Muhammad Tayyab
  • Dauwels, Justin
  • Jaillet, Patrick
• Publication Date: 2021-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1285-1300
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2675
  • Issue Number: 11
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Radar images; Rainfall; Traffic forecasting
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01778699
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: Aug 5 2021 9:19AM