Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161883
Title: Estimating the impact of high-fidelity rainfall data on traffic conditions and traffic prediction
Authors: Prokhorchuk, Anatolii
Mitrovic, Nikola
Muhammad Usman
Stevanovic, Aleksandar
Muhammad Tayyab Asif
Dauwels, Justin
Jaillet, Patrick
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Prokhorchuk, A., Mitrovic, N., Muhammad Usman, Stevanovic, A., Muhammad Tayyab Asif, Dauwels, J. & Jaillet, P. (2021). Estimating the impact of high-fidelity rainfall data on traffic conditions and traffic prediction. Transportation Research Record, 2675(11), 1285-1300. https://dx.doi.org/10.1177/03611981211026309
Journal: Transportation Research Record
Abstract: 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.
URI: https://hdl.handle.net/10356/161883
ISSN: 0361-1981
DOI: 10.1177/03611981211026309
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 National Academy of Sciences. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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