Integration of Speed Control and Parking Management in SUMO for Urban Traffic Optimization

• Iza, Cristian ^[1] ; Iza, Joana ^[2] ; Posadas-Yagüe, Juan-Luis ^[1] ; Poza-Lujan, José-Luis ^[1]
  1. [1] Universidad Politécnica de Valencia

     Universidad Politécnica de Valencia

     Valencia, España

     
  2. [2] Escuela Superior Politécnica de Chimborazo
• Localización: ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, ISSN-e 2255-2863, Vol. 14, Nº. 1, 2025
• Idioma: inglés
• DOI: 10.14201/adcaij.33585
• Enlaces
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• Resumen

  • This study presents an intelligent system for dynamic urban traffic management, focused on the automatic adjustment of speed limits and the activation of lanes as parking areas to optimize urban traffic. Using realistic scenarios simulated in the SUMO (Simulation of Urban MObility) platform and controlled by a neural network implemented in Python, the system effectively responds to variable conditions such as vehicle density, traffic flow, and other factors. To evaluate the achieved optimization, the results are compared using various traffic performance indicators, including congestion levels, average speed, travel time, among others. Through adaptive real-time behavior, the system achieves greater traffic fluidity, reduced congestion, and better use of available infrastructure. The designed control system significantly improves performance compared to uncontrolled scenarios: on average, it reduces travel time by 16 %, CO₂ emissions by 13 %, waiting time by 12 %, and accident probability by 15 %, while traffic flow increases by 11 %. The results show that this approach can effectively complement traditional traffic light control and adapt to diverse urban contexts, positioning itself as a promising solution to improve mobility in modern cities.

• Referencias bibliográficas
  • Alekszejenkó, L., & Dobrowiecki, T. (2023). SUMO simulations for federated learning in communicating autonomous vehicles: A survey on...
  • Amiour, Y., Waygood, E. O. D., & Van den Berg, P. E. W. (2022). Objective and perceived traffic safety for children: A systematic literature...
  • Azfar, T., & Ke, R. (2024). Traffic co-simulation framework empowered by infrastructure camera sensing and reinforcement learning. arXiv...
  • Bagheri, M., & Sarvi, M. (2023). Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic...
  • Beil, C., Kendir, M., Ruhdorfer, R., & Kolbe, T. H. (2022). Dynamic and web-based 4D visualization of streetspace activities derived from...
  • Chen, L. (2024). Development of a parking system based on learning automata. Multimedia Tools and Applications, 83(13), 61165–61180. https://doi.org/10.1007/s11042-023-18031-9
  • Ehlers, S. F. G. (2019). Traffic queue length and pressure estimation for road networks with geometric deep learning algorithms. arXiv preprint,...
  • Farrag, S., El-Hansali, M. Y., Yasar, A., & Shakshuki, E. M. (2020). Simulation-based evaluation of using variable speed limit in traffic...
  • Guastella, D. A., Montero-Porras, E., Morales-Hernández, A., & Bontempi, G. (2023). Traffic modeling with SUMO: A tutorial. arXiv preprint,...
  • Guo, J., Cheng, L., & Wang, S. (2022). CoTV: Cooperative control for traffic light signals and connected autonomous vehicles using deep...
  • Hakeem, A., Gehani, N., Ding, X., Curtmola, R., & Borcea, C. (2016). On-the-fly curbside parking assignment. Proceedings of the 8th International...
  • Héder, M. (2017). From NASA to EU: The evolution of the TRL scale in public sector innovation. The Innovation Journal: The Public Sector Innovation...
  • Hussain, A., Wang, T., & Jiahua, C. (2020). Optimizing traffic lights with multi-agent deep reinforcement learning and V2X communication....
  • Kheterpal, N., Vinitsky, E., Behrisch, M., Bieker-Walz, L., Wu, C., & Bayen, A. M. (2018). Flow: Deep reinforcement learning for control...
  • Mousavi, S. S., Schukat, M., & Howley, E. (2017). Traffic light control using deep policy-gradient and value-function based reinforcement...
  • Rahman, M. R., Akhter, S., & Sumit, S. H. (2020). A SUMO based simulation framework for intelligent traffic management system using deep-neuro-fuzzy...
  • Reza, S., Ferreira, M. C., Machado, J. J. M., & Tavares, J. M. R. S. (2023). A citywide TD-learning based intelligent traffic signal control...
  • Sabour, S., Rao, S., & Ghaderi, M. (2021). DeepFlow: Abnormal traffic flow detection using Siamese networks. arXiv preprint, arXiv:2108.12016....
  • Samarakoon, G. S., & Sivakumar, T. (2022). Microscopic simulation of parking violations in curbside with-flow bus priority lanes using...
  • Skorobogatchenko, D., Zhokhov, V., Astafurova, O., & Fantrov, P. (2023). Neural network-based optimization of traffic light regulation...
  • Sun, K., & Yu, H. (2024). Reinforcement learning for freeway lane-change regulation via connected vehicles. arXiv preprint, arXiv:2412.04341....
  • Ye, C., Xiong, G., Shang, J., Dai, X., Gong, X., & Lv, Y. (2025). SUMO-MCP: Leveraging the Model Context Protocol for autonomous traffic...
  • Ye, Q., Feng, Y., Han, J., Stettler, M., & Angeloudis, P. (2021). A reinforcement learning-based adaptive control model for future street...
  • Yin, S., Cui, Z., & Wang, Y. (2022). Reinforcement learning for curbside space management with infrastructure autonomy and mixed vehicle...