Yunus Dogan1, Goksu Tuysuzoglu1, Elife Ozturk Kiyak2, Bita Ghasemkhani3, Kokten Ulas Birant1,4, Semih Utku1, Derya Birant1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1677-1715, 2025, DOI:10.32604/cmes.2025.069255
- 31 August 2025
Abstract Accurate traffic flow prediction (TFP) is vital for efficient and sustainable transportation management and the development of intelligent traffic systems. However, missing data in real-world traffic datasets poses a significant challenge to maintaining prediction precision. This study introduces REPTF-TMDI, a novel method that combines a Reduced Error Pruning Tree Forest (REPTree Forest) with a newly proposed Time-based Missing Data Imputation (TMDI) approach. The REPTree Forest, an ensemble learning approach, is tailored for time-related traffic data to enhance predictive accuracy and support the evolution of sustainable urban mobility solutions. Meanwhile, the TMDI approach exploits temporal patterns… More >
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