ENHANCING URBAN TRAFFIC FLOW USING INTELLIGENT TRANSPORTATION SYSTEMS: A MACHINE LEARNING APPROACH
Keywords:
Intelligent Transportation Systems, machine learning, urban traffic management, congestion reduction, signal controlAbstract
Urban centers worldwide face growing traffic congestion, resulting in increased travel times, fuel consumption, and carbon emissions. Intelligent Transportation Systems (ITS) combined with machine learning offer promising solutions for optimizing urban traffic management. This paper investigates the implementation of machine learning models in ITS to predict traffic patterns, control signal timings, and manage dynamic traffic flows. Results show that machine learning significantly improves traffic efficiency, reduces congestion, and supports sustainable urban mobility.
References
Chen, C., Zhang, J., & He, Z. (2016). Short-term traffic flow prediction with deep learning: A review. Transportation Research Part C: Emerging Technologies, 71, 284–302.
Abdulhai, B., Pringle, R., & Karakoulas, G.J. (2003). Reinforcement learning for true adaptive traffic signal control. Journal of Transportation Engineering, 129(3), 278–285.
Vlahogianni, E.I., Karlaftis, M.G., & Golias, J.C. (2014). Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C: Emerging Technologies, 43, 3–19.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
