Abstract

Traffic congestion remains a critical challenge in rapidly expanding urban areas, causing increased travel delays, excessive fuel consumption, environmental degradation, and economic inefficiencies. Traditional traffic management approaches are often insufficient, as they lack the ability to respond dynamically to continuously changing traffic conditions. This study proposes a novel data-driven framework for the prediction and management of traffic congestion using Intelligent Transportation Systems (ITS). The methodology integrates real-time and historical traffic data with advanced machine learning techniques to accurately forecast congestion levels and support effective traffic control strategies. Key parameters considered in the analysis include traffic density, average vehicle speed, roadway capacity, weather conditions, and time-dependent variations. A hybrid predictive model is developed by combining regression techniques with ensemble learning methods to effectively capture both linear trends and complex non-linear traffic patterns. The effectiveness of the model is assessed through widely accepted error metrics along with accuracy-based evaluation methods, ensuring dependable and consistent performance.The results indicate that the proposed framework significantly improves prediction accuracy and enhances the ability of traffic management authorities to make informed, real-time decisions. Furthermore, the integration of data analytics with ITS technologies demonstrates strong potential for reducing congestion, improving traffic flow efficiency, and promoting sustainable urban mobility. This study supports the development of advanced traffic management approaches by offering practical insights that can be applied to next-generation smart city transportation systems.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Li Y, Zhang J, Wang X. Application of machine learning in traffic flow estimation. Transp Res Part C. 2021;120:102810.
2. Smith BL, Williams BM. Traffic forecasting models: a comparative study. Transp Res Part C. 2002;10(4):303–321.
3. Chen C, Wang Y. Predictive modeling for congestion analysis using intelligent systems. IEEE Trans Intell Transp Syst. 2019;20(9):3215–3224.
4. Zheng Z, Wang J. Data-driven approaches for urban traffic management. Sustainability. 2020;12(3):1025.
5. Vlahogianni EI, Karlaftis MG. Short-term traffic prediction techniques: a review. Transport Reviews. 2014;34(4):421–447
6. Zhang J, Zheng Y, Qi D. Deep spatio-temporal residual networks for citywide crowd flows prediction. Proc AAAI Conf Artif Intell. 2017;31(1):1655–1661.
7. Polson NG, Sokolov VO. Deep learning for short-term traffic flow prediction. Transp Res Part C. 2017;79:1–17.
8. Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Proc Int Joint Conf Artif Intell. 2018;3634–3640.
9. Lv Y, Duan Y, Kang W, Li Z, Wang F. Traffic flow prediction with big data: A deep learning approach. IEEE Trans Intell Transp Syst. 2015;16(2):865–873.
10. Koesdwiady A, Soua R, Karray F. Improving traffic flow prediction with weather information in connected cars: A deep learning approach. IEEE Trans Veh Technol. 2016;65(12):9508–9517.

Abstract

Traffic congestion remains a critical challenge in rapidly expanding urban areas, causing increased travel delays, excessive fuel consumption, environmental degradation, and economic inefficiencies. Traditional traffic management approaches are often insufficient, as they lack the ability to respond dynamically to continuously changing traffic conditions. This study proposes a novel data-driven framework for the prediction and management of traffic congestion using Intelligent Transportation Systems (ITS). The methodology integrates real-time and historical traffic data with advanced machine learning techniques to accurately forecast congestion levels and support effective traffic control strategies. Key parameters considered in the analysis include traffic density, average vehicle speed, roadway capacity, weather conditions, and time-dependent variations. A hybrid predictive model is developed by combining regression techniques with ensemble learning methods to effectively capture both linear trends and complex non-linear traffic patterns. The effectiveness of the model is assessed through widely accepted error metrics along with accuracy-based evaluation methods, ensuring dependable and consistent performance.The results indicate that the proposed framework significantly improves prediction accuracy and enhances the ability of traffic management authorities to make informed, real-time decisions. Furthermore, the integration of data analytics with ITS technologies demonstrates strong potential for reducing congestion, improving traffic flow efficiency, and promoting sustainable urban mobility. This study supports the development of advanced traffic management approaches by offering practical insights that can be applied to next-generation smart city transportation systems.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Li Y, Zhang J, Wang X. Application of machine learning in traffic flow estimation. Transp Res Part C. 2021;120:102810.
2. Smith BL, Williams BM. Traffic forecasting models: a comparative study. Transp Res Part C. 2002;10(4):303–321.
3. Chen C, Wang Y. Predictive modeling for congestion analysis using intelligent systems. IEEE Trans Intell Transp Syst. 2019;20(9):3215–3224.
4. Zheng Z, Wang J. Data-driven approaches for urban traffic management. Sustainability. 2020;12(3):1025.
5. Vlahogianni EI, Karlaftis MG. Short-term traffic prediction techniques: a review. Transport Reviews. 2014;34(4):421–447
6. Zhang J, Zheng Y, Qi D. Deep spatio-temporal residual networks for citywide crowd flows prediction. Proc AAAI Conf Artif Intell. 2017;31(1):1655–1661.
7. Polson NG, Sokolov VO. Deep learning for short-term traffic flow prediction. Transp Res Part C. 2017;79:1–17.
8. Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Proc Int Joint Conf Artif Intell. 2018;3634–3640.
9. Lv Y, Duan Y, Kang W, Li Z, Wang F. Traffic flow prediction with big data: A deep learning approach. IEEE Trans Intell Transp Syst. 2015;16(2):865–873.
10. Koesdwiady A, Soua R, Karray F. Improving traffic flow prediction with weather information in connected cars: A deep learning approach. IEEE Trans Veh Technol. 2016;65(12):9508–9517.