Title Urban traffic congestion prediction: a multi-step approach utilizing sensor data and weather information /
Authors Tsalikidis, Nikolaos ; Mystakidis, Aristeidis ; Koukaras, Paraskevas ; Ivaškevičius, Marius ; Morkūnaitė, Lina ; Ioannidis, Dimosthenis ; Fokaides, Paris A ; Tjortjis, Christos ; Tzovaras, Dimitrios
DOI 10.3390/smartcities7010010
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Is Part of Smart cities.. Basel : MDPI. 2024, vol. 7, iss. 1, p. 233-253.. ISSN 2624-6511
Keywords [eng] traffic congestion prediction ; time series forecasting ; road traffic ; Machine Learning ; Deep Learning, smart cities ; weather information
Abstract [eng] The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.
Published Basel : MDPI
Type Journal article
Language English
Publication date 2024