Estimating the impact of high-fidelity rainfall data on traffic conditions and traffic prediction

Anatolii Prokhorchuk*, Nikola Mitrovic, Usman Muhammad, Aleksandar Stevanovic, Muhammad Tayyab Asif, Justin Dauwels, Patrick Jaillet

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

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.

Original languageEnglish
Title of host publicationTransportation Research Record
PublisherSAGE Publications
Pages1285-1300
Number of pages16
Volume2675
Edition11
DOIs
Publication statusPublished - 2021

Publication series

NameTransportation Research Record
Number11
Volume2675
ISSN (Print)0361-1981
ISSN (Electronic)2169-4052

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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