Integration of Speed Control and Parking Management in SUMO for Urban Traffic Optimization
Abstract
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.
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Alekszejenkó, L., & Dobrowiecki, T. (2023). SUMO simulations for federated learning in communicating autonomous vehicles: A survey on efficiency and security. SUMO User Conference Proceedings, 4(1), 115–121. https://doi.org/10.52825/scp.v4i.221
Amiour, Y., Waygood, E. O. D., & Van den Berg, P. E. W. (2022). Objective and perceived traffic safety for children: A systematic literature review of traffic and built environment characteristics related to safe travel. International Journal of Environmental Research and Public Health, 19(5), 2641. https://doi.org/10.3390/ijerph19052641
Azfar, T., & Ke, R. (2024). Traffic co-simulation framework empowered by infrastructure camera sensing and reinforcement learning. arXiv preprint, arXiv:2412.03925. https://doi.org/10.48550/arXiv.2412.03925
Bagheri, M., & Sarvi, M. (2023). Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic circle in SUMO. Transportation Research Record, 2677(1), 1–12. https://doi.org/10.1177/03611981231167420
Beil, C., Kendir, M., Ruhdorfer, R., & Kolbe, T. H. (2022). Dynamic and web-based 4D visualization of streetspace activities derived from traffic simulations and semantic 3D city models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4/W2-2022, 29–36. https://doi.org/10.5194/isprs-annals-X-4-W2-2022-29-2022
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, arXiv:1905.03889. https://doi.org/10.48550/arXiv.1905.03889
Farrag, S., El-Hansali, M. Y., Yasar, A., & Shakshuki, E. M. (2020). Simulation-based evaluation of using variable speed limit in traffic incidents. Procedia Computer Science, 175, 340–348. https://doi.org/10.1016/j.procs.2020.07.049
Guastella, D. A., Montero-Porras, E., Morales-Hernández, A., & Bontempi, G. (2023). Traffic modeling with SUMO: A tutorial. arXiv preprint, arXiv:2304.05982. https://doi.org/10.48550/arXiv.2304.05982
Guo, J., Cheng, L., & Wang, S. (2022). CoTV: Cooperative control for traffic light signals and connected autonomous vehicles using deep reinforcement learning. arXiv preprint, arXiv:2201.13143. https://doi.org/10.48550/arXiv.2201.13143
Hakeem, A., Gehani, N., Ding, X., Curtmola, R., & Borcea, C. (2016). On-the-fly curbside parking assignment. Proceedings of the 8th International Conference on Mobile Computing, Applications and Services (MobiCASE 2016), 1–10. https://doi.org/10.4108/eai.30-11-2016.2267101
Héder, M. (2017). From NASA to EU: The evolution of the TRL scale in public sector innovation. The Innovation Journal: The Public Sector Innovation Journal, 22(2), 1–23. https://web.archive.org/web/20171011071816/https://www.innovation.cc/discussion-papers/22_2_3_heder_nasa-to-eu-trl-scale.pdf
Hussain, A., Wang, T., & Jiahua, C. (2020). Optimizing traffic lights with multi-agent deep reinforcement learning and V2X communication. arXiv preprint, arXiv:2002.09853. https://doi.org/10.48550/arXiv.2002.09853
Kheterpal, N., Vinitsky, E., Behrisch, M., Bieker-Walz, L., Wu, C., & Bayen, A. M. (2018). Flow: Deep reinforcement learning for control in SUMO. EPiC Series in Engineering, 2, 134–151. https://flow-project.github.io/papers/Flow_Deep_Reinforcement_Learning_for_Control_in_SUMO.pdf
Mousavi, S. S., Schukat, M., & Howley, E. (2017). Traffic light control using deep policy-gradient and value-function based reinforcement learning. arXiv preprint, arXiv:1704.08883. https://doi.org/10.48550/arXiv.1704.08883
Rahman, M. R., Akhter, S., & Sumit, S. H. (2020). A SUMO based simulation framework for intelligent traffic management system using deep-neuro-fuzzy model. Journal of Traffic and Logistics Engineering, 8(1), 1–5. https://www.jtle.net/uploadfile/2020/0529/20200529050106994.pdf
Reza, S., Ferreira, M. C., Machado, J. J. M., & Tavares, J. M. R. S. (2023). A citywide TD-learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO. Expert Systems, 42(1), e13301. https://doi.org/10.1111/exsy.13301
Sabour, S., Rao, S., & Ghaderi, M. (2021). DeepFlow: Abnormal traffic flow detection using Siamese networks. arXiv preprint, arXiv:2108.12016. https://doi.org/10.48550/arXiv.2108.12016
Samarakoon, G. S., & Sivakumar, T. (2022). Microscopic simulation of parking violations in curbside with-flow bus priority lanes using SUMO Traffic Control Interface (TraCI). SUMO User Conference Proceedings, 2023(1), 45–52. http://dl.lib.uom.lk/handle/123/19416
Skorobogatchenko, D., Zhokhov, V., Astafurova, O., & Fantrov, P. (2023). Neural network-based optimization of traffic light regulation of a transport hub with data fetched during simulation in SUMO package. In A. G. Kravets, M. V. Shcherbakov, & P. P. Groumpos (Eds.), Creativity in Intelligent Technologies and Data Science (pp. 337–350). Springer. https://doi.org/10.1007/978-3-031-44615-3_23
Sun, K., & Yu, H. (2024). Reinforcement learning for freeway lane-change regulation via connected vehicles. arXiv preprint, arXiv:2412.04341. https://doi.org/10.48550/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 simulation and optimization. arXiv. https://arxiv.org/abs/2506.03548
Ye, Q., Feng, Y., Han, J., Stettler, M., & Angeloudis, P. (2021). A reinforcement learning-based adaptive control model for future street planning: An algorithm and a case study. arXiv preprint, arXiv:2112.05434. https://doi.org/10.48550/arXiv.2112.05434
Yin, S., Cui, Z., & Wang, Y. (2022). Reinforcement learning for curbside space management with infrastructure autonomy and mixed vehicle connectivity. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 3276–3282). IEEE. https://doi.org/10.1109/ITSC55140.2022.9922149
Amiour, Y., Waygood, E. O. D., & Van den Berg, P. E. W. (2022). Objective and perceived traffic safety for children: A systematic literature review of traffic and built environment characteristics related to safe travel. International Journal of Environmental Research and Public Health, 19(5), 2641. https://doi.org/10.3390/ijerph19052641
Azfar, T., & Ke, R. (2024). Traffic co-simulation framework empowered by infrastructure camera sensing and reinforcement learning. arXiv preprint, arXiv:2412.03925. https://doi.org/10.48550/arXiv.2412.03925
Bagheri, M., & Sarvi, M. (2023). Implementing artificial neural network-based gap acceptance models in the simulation model of a traffic circle in SUMO. Transportation Research Record, 2677(1), 1–12. https://doi.org/10.1177/03611981231167420
Beil, C., Kendir, M., Ruhdorfer, R., & Kolbe, T. H. (2022). Dynamic and web-based 4D visualization of streetspace activities derived from traffic simulations and semantic 3D city models. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-4/W2-2022, 29–36. https://doi.org/10.5194/isprs-annals-X-4-W2-2022-29-2022
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, arXiv:1905.03889. https://doi.org/10.48550/arXiv.1905.03889
Farrag, S., El-Hansali, M. Y., Yasar, A., & Shakshuki, E. M. (2020). Simulation-based evaluation of using variable speed limit in traffic incidents. Procedia Computer Science, 175, 340–348. https://doi.org/10.1016/j.procs.2020.07.049
Guastella, D. A., Montero-Porras, E., Morales-Hernández, A., & Bontempi, G. (2023). Traffic modeling with SUMO: A tutorial. arXiv preprint, arXiv:2304.05982. https://doi.org/10.48550/arXiv.2304.05982
Guo, J., Cheng, L., & Wang, S. (2022). CoTV: Cooperative control for traffic light signals and connected autonomous vehicles using deep reinforcement learning. arXiv preprint, arXiv:2201.13143. https://doi.org/10.48550/arXiv.2201.13143
Hakeem, A., Gehani, N., Ding, X., Curtmola, R., & Borcea, C. (2016). On-the-fly curbside parking assignment. Proceedings of the 8th International Conference on Mobile Computing, Applications and Services (MobiCASE 2016), 1–10. https://doi.org/10.4108/eai.30-11-2016.2267101
Héder, M. (2017). From NASA to EU: The evolution of the TRL scale in public sector innovation. The Innovation Journal: The Public Sector Innovation Journal, 22(2), 1–23. https://web.archive.org/web/20171011071816/https://www.innovation.cc/discussion-papers/22_2_3_heder_nasa-to-eu-trl-scale.pdf
Hussain, A., Wang, T., & Jiahua, C. (2020). Optimizing traffic lights with multi-agent deep reinforcement learning and V2X communication. arXiv preprint, arXiv:2002.09853. https://doi.org/10.48550/arXiv.2002.09853
Kheterpal, N., Vinitsky, E., Behrisch, M., Bieker-Walz, L., Wu, C., & Bayen, A. M. (2018). Flow: Deep reinforcement learning for control in SUMO. EPiC Series in Engineering, 2, 134–151. https://flow-project.github.io/papers/Flow_Deep_Reinforcement_Learning_for_Control_in_SUMO.pdf
Mousavi, S. S., Schukat, M., & Howley, E. (2017). Traffic light control using deep policy-gradient and value-function based reinforcement learning. arXiv preprint, arXiv:1704.08883. https://doi.org/10.48550/arXiv.1704.08883
Rahman, M. R., Akhter, S., & Sumit, S. H. (2020). A SUMO based simulation framework for intelligent traffic management system using deep-neuro-fuzzy model. Journal of Traffic and Logistics Engineering, 8(1), 1–5. https://www.jtle.net/uploadfile/2020/0529/20200529050106994.pdf
Reza, S., Ferreira, M. C., Machado, J. J. M., & Tavares, J. M. R. S. (2023). A citywide TD-learning based intelligent traffic signal control for autonomous vehicles: Performance evaluation using SUMO. Expert Systems, 42(1), e13301. https://doi.org/10.1111/exsy.13301
Sabour, S., Rao, S., & Ghaderi, M. (2021). DeepFlow: Abnormal traffic flow detection using Siamese networks. arXiv preprint, arXiv:2108.12016. https://doi.org/10.48550/arXiv.2108.12016
Samarakoon, G. S., & Sivakumar, T. (2022). Microscopic simulation of parking violations in curbside with-flow bus priority lanes using SUMO Traffic Control Interface (TraCI). SUMO User Conference Proceedings, 2023(1), 45–52. http://dl.lib.uom.lk/handle/123/19416
Skorobogatchenko, D., Zhokhov, V., Astafurova, O., & Fantrov, P. (2023). Neural network-based optimization of traffic light regulation of a transport hub with data fetched during simulation in SUMO package. In A. G. Kravets, M. V. Shcherbakov, & P. P. Groumpos (Eds.), Creativity in Intelligent Technologies and Data Science (pp. 337–350). Springer. https://doi.org/10.1007/978-3-031-44615-3_23
Sun, K., & Yu, H. (2024). Reinforcement learning for freeway lane-change regulation via connected vehicles. arXiv preprint, arXiv:2412.04341. https://doi.org/10.48550/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 simulation and optimization. arXiv. https://arxiv.org/abs/2506.03548
Ye, Q., Feng, Y., Han, J., Stettler, M., & Angeloudis, P. (2021). A reinforcement learning-based adaptive control model for future street planning: An algorithm and a case study. arXiv preprint, arXiv:2112.05434. https://doi.org/10.48550/arXiv.2112.05434
Yin, S., Cui, Z., & Wang, Y. (2022). Reinforcement learning for curbside space management with infrastructure autonomy and mixed vehicle connectivity. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (pp. 3276–3282). IEEE. https://doi.org/10.1109/ITSC55140.2022.9922149
Iza, C., Iza, J., Posadas-Yagüe, J.-L., & Poza-Lujan, J.-L. (2025). Integration of Speed Control and Parking Management in SUMO for Urban Traffic Optimization. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e33585. https://doi.org/10.14201/adcaij.33585
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