Role of Parking Management in Urban Traffic Congestion: A Review
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Abstract
Rapid urbanisation poses a serious transportation problem and is particularly pressing in developing nations. Inefficient parking management significantly affects traffic flow, roadway capacity, and the overall performance of transport systems. This review examines the relationship between parking characteristics and urban traffic congestion, drawing on recent academic articles, policy documents, and case-based findings. The main issues identified are over-parking on streets, insufficient organised parking supply, weak compliance with parking rules and policies, and poor integration of parking with land-use and public transportation planning. Taken together, these variables reduce the effective carriageway width, generate more traffic snarls, introduce regional bottlenecks and hold-ups, and worsen traffic congestion. The review focuses on current parking management strategies, including demand-responsive parking pricing, shared parking modes, smart parking technologies, regulatory enforcement, and connections to sustainable mobility planning. Special attention is focused on medium and small cities, characterized by insufficient infrastructure, mixed circulation, institutional bottlenecks and limitations in parking management. Results suggest that parking management forms an essential part of congestion mitigation strategies within urban transport planning.
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Ćelić, J., Mandžuka, B., Tomas, V., & Tadić, F. (2024). Driver‑centric urban route planning: Smart search for parking. Sustainability, 16(2), 856. DOI: https://doi.org/10.3390/su16020856
Sakib, N., Bakibillah, A. S. M., Susilawati, S., Kamal, M. A. S., & Yamada, K. (2024). Eco‑friendly smart car parking management system with enhanced sustainability. Sustainability, 16(10). DOI: https://doi.org/10.3390/su16104145
Li, J., Dong, Y., Wang, Q., & Liu, C. (2024). Proactive pricing strategies for on‑street parking management using physics‑informed neural networks. International Journal of Strategic Property Management, 28(5), 320–333.
DOI: https://doi.org/10.3846/ijspm.2024.22233
Enríquez, F. J., Mejía‑Muñoz, J. M., Bravo, G., & Cruz‑Mejía, O. (2024). Smart parking using fog computing and machine learning‑based occupancy prediction. Applied System Innovation, 7(3), 52. DOI: https://doi.org/10.3390/asi7030052
Jin, B., Zhao, Y., & Ni, J. (2022). Prediction of short‑term parking space availability using an improved LSTM algorithm. Applied Sciences, 12(21). DOI: https://doi.org/10.3390/app122111046
Alymani, M., Almoqhem, L., Alabdulwahab, D., Alghamdi, A., & Raza, K. (2025). Enabling smart parking for smart cities using IoT and machine learning. PeerJ Computer Science. DOI: https://doi.org/10.7717/peerj-cs.2544
Ala’anzy, M. A., Abilakim, A., Zhanuzak, R., & Li, L. (2025). Real‑time smart parking system based on IoT and fog computing. Scientific Reports. DOI: https://doi.org/10.1038/s41598-025-15507-6
Chauhan, P., Goel, S., & Winter, S. (2025). Impact of on‑street parking space placement on through traffic. AGILE GIScience Series, 6, 17.