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Title	A Weather Oriented Pre-Tuning Methodology For Long-Term Traffic Speed Estimation
ID_Doc	5825
Authors	Bilgin E.; Turkmen H.I.; Guvensan M.A.
Year	2023
Published	Proceedings - 2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2023
DOI	http://dx.doi.org/10.1109/WoWMoM57956.2023.00079
Abstract	Long-term traffic speed estimation become a challenging problem since forecasting the distant future of city traffic requires considering environmental factors such as weather, big events, road maintenance, and accidents. One of the predictable factors is weather condition which has a substantial impact on traffic speed, especially in metropolitan cities. It is very important to exploit the weather parameters correctly to predict traffic speed up to 1 week ahead. In this study, we propose to pre-tune the speed data based on weather parameters before feeding it into deep learning algorithms. Two different pre-tuners, Effect Rate (ER) and Polynomial Regression (PR), are introduced where the first method calculates the effect of weather conditions linearly, while the second method proposes to transform the traffic characteristic regarding weather conditions with the help of polynomial regression. Test results showed that the proposed pre-tuners could decrease the traffic prediction error rate up to 20% depending upon the weather condition. © 2023 IEEE.
Author Keywords	polynomial regression; pre-processing; smart city; traffic speed estimation; transportation; weather conditions

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