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Machine Learning-Enhanced Traffic Light Optimization System Prioritizing Emergency Vehicle Passage Using SVM and Random Forest Models

Year 2025, Volume: 12 Issue: 1, 175 - 196, 26.03.2025

Yıldıran Yılmaz

https://doi.org/10.54287/gujsa.1581105

Abstract

Traffic congestion in cities includes the complex and dangerous passing of emergency vehicles, which is a time-consuming task. This problem requires the optimisation of traffic lights in favour of emergency vehicles. To accomplish this, this paper discusses an optimized traffic light system using machine learning that prioritizes the passing of emergency vehicles into city areas. It integrates SVM and Random Forest models by dynamically adjusting traffic light signals based on traffic density to accelerate emergency vehicles. The results reveal that the proposed system would lead to improved emergency response times while enhancing overall transportation efficiency with reduced congestion of traffic. Additionally, the study further went on to establish the effectiveness of the proposed model as a solution in traffic flow optimization and management. Results show that the performance of the proposed model is effective for the purpose of traffic light optimization. The SVM+SAFS and RF+SAFS methods figured prominently as high-performance methods with accuracy rates of 94.89% and 95.02%, respectively. Furthermore, in the case of the RF+SAFS method used for traffic light optimization, it was possible to reduce the average waiting time by 20%, increase the capacity of transit by 15%, and decrease fuel consumption by 10%. Overall, combining the outputs in the model led to the following performance, an 18% decrease in total travel time.

Keywords

Machine Learning Random Forest SVM Urban Traffic Emergency Vehicles Traffic Light Optimization Feature Selection

References

  • Abdul Kareem, E. I., & Hoomod, H. K. (2022). Integrated tripartite modules for intelligent traffic light system. International Journal of Electrical and Computer Engineering (IJECE), 12(3), 2971. https://doi.org/10.11591/ijece.v12i3.pp2971-2985
  • Almukhalfi, H., Noor, A., & Noor, T. H. (2024). Traffic management approaches using machine learning and deep learning techniques: A survey. In Engineering Applications of Artificial Intelligence (Vol. 133). https://doi.org/10.1016/j.engappai.2024.108147
  • Barzilai, O., Rika, H., Voloch, N., Hajaj, M. M., Steiner, O. L., & Ahituv, N. (2023). Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane. Transport and Telecommunication Journal, 24(1), 1–12. https://doi.org/10.2478/ttj-2023-0001
  • Boudhrioua, S., & Shatanawi, M. (2019). Implementation of Absolute Priority in a Predictive Traffic Actuation Schemes. Periodica Polytechnica Transportation Engineering, 49(2), 182–188. https://doi.org/10.3311/PPtr.14191
  • Chu, H.-C., Liao, Y.-X., Chang, L., & Lee, Y.-H. (2019). Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning. Applied Sciences, 9(21), 4558. https://doi.org/10.3390/app9214558
  • Das, D., Altekar, N. V., & Head, K. L. (2023). Priority-Based Traffic Signal Coordination System With Multi-Modal Priority and Vehicle Actuation in a Connected Vehicle Environment. Transportation Research Record: Journal of the Transportation Research Board, 2677(5), 666–681. https://doi.org/10.1177/03611981221134627
  • Deepika, & Pandove, G. (2024). Optimizing traffic flow with Q-learning and genetic algorithm for congestion control. Evolutionary Intelligence, 17(5–6), 4179–4197. https://doi.org/10.1007/s12065-024-00978-9
  • Deshpande, S., & Hsieh, S.-J. (2023). Cyber-Physical System for Smart Traffic Light Control. Sensors, 23(11), 5028. https://doi.org/10.3390/s23115028
  • Djahel, S., Smith, N., Wang, S., & Murphy, J. (2015). Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach. 2015 IEEE First International Smart Cities Conference (ISC2), 1–8. https://doi.org/10.1109/ISC2.2015.7366151
  • Gaikwad, V., Holkar, A., Hande, T., Lokhande, P., & Badade, V. (2023). Smart Traffic Light System Using Internet of Things. In Data Science and Intelligent Computing Techniques (pp. 795–808). Soft Computing Research Society. https://doi.org/10.56155/978-81-955020-2-8-68
  • Hu, H.-C., Zhou, J., Barlow, G. J., & Smith, S. F. (2022). Connection-Based Scheduling for Real-Time Intersection Control. https://doi.org/arXiv.2210.08445
  • Lei, Z., & Yigong, S. (2023). Intelligent Traffic System Using Machine Learning Techniques: A Review. International Journal of Research Publication and Reviews, 4(5), 1457–1461. https://doi.org/10.55248/gengpi.234.5.38047
  • Lu, Q., & Kim, K.-D. (2017). A Genetic Algorithm Approach for Expedited Crossing of Emergency Vehicles in Connected and Autonomous Intersection Traffic. Journal of Advanced Transportation, 2017, 1–14. https://doi.org/10.1155/2017/7318917
  • Moumen, I., Abouchabaka, J., & Rafalia, N. (2023a). Adaptive traffic lights based on traffic flow prediction using machine learning models. International Journal of Electrical and Computer Engineering (IJECE), 13(5), 5813. https://doi.org/10.11591/ijece.v13i5.pp5813-5823
  • Moumen, I., Abouchabaka, J., & Rafalia, N. (2023b). Enhancing urban mobility: integration of IoT road traffic data and artificial intelligence in smart city environment. Indonesian Journal of Electrical Engineering and Computer Science, 32(2), 985. https://doi.org/10.11591/ijeecs.v32.i2.pp985-993
  • Naik, D. L., & kiran, R. (2021). A novel sensitivity-based method for feature selection. Journal of Big Data, 8(1), 128. https://doi.org/10.1186/s40537-021-00515-w
  • Nambajemariya, F., & Wang, Y. (2021). Excavation of the Internet of Things in Urban Areas Based on an Intelligent Transportation Management System. Advances in Internet of Things, 11(03), 113–122. https://doi.org/10.4236/ait.2021.113008
  • Nellore, K., & Hancke, G. (2016). Traffic Management for Emergency Vehicle Priority Based on Visual Sensing. Sensors, 16(11), 1892. https://doi.org/10.3390/s16111892
  • Odhiambo Omuya, E., Onyango Okeyo, G., & Waema Kimwele, M. (2021). Feature Selection for Classification using Principal Component Analysis and Information Gain. Expert Systems with Applications, 174, 114765. https://doi.org/10.1016/j.eswa.2021.114765
  • Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, & Yun-Pang. (2018). Microscopic Traffic Simulation using SUMO in The 21st IEEE International Conference on Intelligent Transportation Systems. SUMO Conference Proceedings. https://elib.dlr.de/124092/
  • Sánchez-Maroño, N., & Alonso-Betanzos, A. (2007). Feature Selection Based on Sensitivity Analysis. In Current Topics in Artificial Intelligence (pp. 239–248). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_25
  • Savithramma, R. M., Sumathi, R., & Sudhira, H. S. (2022). SMART Emergency Vehicle Management at Signalized Intersection using Machine Learning. Indian Journal Of Science And Technology, 15(35), 1754–1763. https://doi.org/10.17485/IJST/v15i35.1151
  • Shanaka, H. M. R., Pussella, L. C. P., Rathnayake, R. M. P. N., Ariyarathna, W. A. M. N. C., Viduruwan, P. D. R., & Kulathilake, K. A. S. H. (2018). Case Study on an Adaptive Traffic Controlling Method Using Real-time Traffic Streaming. 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 1–6. https://doi.org/10.1109/ICIAFS.2018.8913329
  • Vihurskyi, B. (2024). Optimizing Urban Traffic Management with Machine Learning Techniques: A Systematic Review. 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), 403–408. https://doi.org/10.1109/InCACCT61598.2024.10551137
  • Wang, M., Pang, A., Kan, Y., Pun, M.-O., Chen, C. S., & Huang, B. (2024). LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments.
  • Yang, Z., Mei, D., Yang, Q., Zhou, H., & Li, X. (2014). Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing. Mathematical Problems in Engineering, 2014, 1–8. https://doi.org/10.1155/2014/926251
  • Zrigui, I., Khoulji, S., Larbi Kerkeb, M., Ennassiri, A., & Bourekkadi, S. (2023). Reducing Carbon Footprint with Real-Time Transport Planning and Big Data Analytics. E3S Web of Conferences, 412, 01082. https://doi.org/10.1051/e3sconf/202341201082

Year 2025, Volume: 12 Issue: 1, 175 - 196, 26.03.2025

Yıldıran Yılmaz

https://doi.org/10.54287/gujsa.1581105

Abstract

References

  • Abdul Kareem, E. I., & Hoomod, H. K. (2022). Integrated tripartite modules for intelligent traffic light system. International Journal of Electrical and Computer Engineering (IJECE), 12(3), 2971. https://doi.org/10.11591/ijece.v12i3.pp2971-2985
  • Almukhalfi, H., Noor, A., & Noor, T. H. (2024). Traffic management approaches using machine learning and deep learning techniques: A survey. In Engineering Applications of Artificial Intelligence (Vol. 133). https://doi.org/10.1016/j.engappai.2024.108147
  • Barzilai, O., Rika, H., Voloch, N., Hajaj, M. M., Steiner, O. L., & Ahituv, N. (2023). Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane. Transport and Telecommunication Journal, 24(1), 1–12. https://doi.org/10.2478/ttj-2023-0001
  • Boudhrioua, S., & Shatanawi, M. (2019). Implementation of Absolute Priority in a Predictive Traffic Actuation Schemes. Periodica Polytechnica Transportation Engineering, 49(2), 182–188. https://doi.org/10.3311/PPtr.14191
  • Chu, H.-C., Liao, Y.-X., Chang, L., & Lee, Y.-H. (2019). Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning. Applied Sciences, 9(21), 4558. https://doi.org/10.3390/app9214558
  • Das, D., Altekar, N. V., & Head, K. L. (2023). Priority-Based Traffic Signal Coordination System With Multi-Modal Priority and Vehicle Actuation in a Connected Vehicle Environment. Transportation Research Record: Journal of the Transportation Research Board, 2677(5), 666–681. https://doi.org/10.1177/03611981221134627
  • Deepika, & Pandove, G. (2024). Optimizing traffic flow with Q-learning and genetic algorithm for congestion control. Evolutionary Intelligence, 17(5–6), 4179–4197. https://doi.org/10.1007/s12065-024-00978-9
  • Deshpande, S., & Hsieh, S.-J. (2023). Cyber-Physical System for Smart Traffic Light Control. Sensors, 23(11), 5028. https://doi.org/10.3390/s23115028
  • Djahel, S., Smith, N., Wang, S., & Murphy, J. (2015). Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach. 2015 IEEE First International Smart Cities Conference (ISC2), 1–8. https://doi.org/10.1109/ISC2.2015.7366151
  • Gaikwad, V., Holkar, A., Hande, T., Lokhande, P., & Badade, V. (2023). Smart Traffic Light System Using Internet of Things. In Data Science and Intelligent Computing Techniques (pp. 795–808). Soft Computing Research Society. https://doi.org/10.56155/978-81-955020-2-8-68
  • Hu, H.-C., Zhou, J., Barlow, G. J., & Smith, S. F. (2022). Connection-Based Scheduling for Real-Time Intersection Control. https://doi.org/arXiv.2210.08445
  • Lei, Z., & Yigong, S. (2023). Intelligent Traffic System Using Machine Learning Techniques: A Review. International Journal of Research Publication and Reviews, 4(5), 1457–1461. https://doi.org/10.55248/gengpi.234.5.38047
  • Lu, Q., & Kim, K.-D. (2017). A Genetic Algorithm Approach for Expedited Crossing of Emergency Vehicles in Connected and Autonomous Intersection Traffic. Journal of Advanced Transportation, 2017, 1–14. https://doi.org/10.1155/2017/7318917
  • Moumen, I., Abouchabaka, J., & Rafalia, N. (2023a). Adaptive traffic lights based on traffic flow prediction using machine learning models. International Journal of Electrical and Computer Engineering (IJECE), 13(5), 5813. https://doi.org/10.11591/ijece.v13i5.pp5813-5823
  • Moumen, I., Abouchabaka, J., & Rafalia, N. (2023b). Enhancing urban mobility: integration of IoT road traffic data and artificial intelligence in smart city environment. Indonesian Journal of Electrical Engineering and Computer Science, 32(2), 985. https://doi.org/10.11591/ijeecs.v32.i2.pp985-993
  • Naik, D. L., & kiran, R. (2021). A novel sensitivity-based method for feature selection. Journal of Big Data, 8(1), 128. https://doi.org/10.1186/s40537-021-00515-w
  • Nambajemariya, F., & Wang, Y. (2021). Excavation of the Internet of Things in Urban Areas Based on an Intelligent Transportation Management System. Advances in Internet of Things, 11(03), 113–122. https://doi.org/10.4236/ait.2021.113008
  • Nellore, K., & Hancke, G. (2016). Traffic Management for Emergency Vehicle Priority Based on Visual Sensing. Sensors, 16(11), 1892. https://doi.org/10.3390/s16111892
  • Odhiambo Omuya, E., Onyango Okeyo, G., & Waema Kimwele, M. (2021). Feature Selection for Classification using Principal Component Analysis and Information Gain. Expert Systems with Applications, 174, 114765. https://doi.org/10.1016/j.eswa.2021.114765
  • Pablo Alvarez Lopez, Michael Behrisch, Laura Bieker-Walz, Jakob Erdmann, & Yun-Pang. (2018). Microscopic Traffic Simulation using SUMO in The 21st IEEE International Conference on Intelligent Transportation Systems. SUMO Conference Proceedings. https://elib.dlr.de/124092/
  • Sánchez-Maroño, N., & Alonso-Betanzos, A. (2007). Feature Selection Based on Sensitivity Analysis. In Current Topics in Artificial Intelligence (pp. 239–248). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_25
  • Savithramma, R. M., Sumathi, R., & Sudhira, H. S. (2022). SMART Emergency Vehicle Management at Signalized Intersection using Machine Learning. Indian Journal Of Science And Technology, 15(35), 1754–1763. https://doi.org/10.17485/IJST/v15i35.1151
  • Shanaka, H. M. R., Pussella, L. C. P., Rathnayake, R. M. P. N., Ariyarathna, W. A. M. N. C., Viduruwan, P. D. R., & Kulathilake, K. A. S. H. (2018). Case Study on an Adaptive Traffic Controlling Method Using Real-time Traffic Streaming. 2018 IEEE International Conference on Information and Automation for Sustainability (ICIAfS), 1–6. https://doi.org/10.1109/ICIAFS.2018.8913329
  • Vihurskyi, B. (2024). Optimizing Urban Traffic Management with Machine Learning Techniques: A Systematic Review. 2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), 403–408. https://doi.org/10.1109/InCACCT61598.2024.10551137
  • Wang, M., Pang, A., Kan, Y., Pun, M.-O., Chen, C. S., & Huang, B. (2024). LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments.
  • Yang, Z., Mei, D., Yang, Q., Zhou, H., & Li, X. (2014). Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing. Mathematical Problems in Engineering, 2014, 1–8. https://doi.org/10.1155/2014/926251
  • Zrigui, I., Khoulji, S., Larbi Kerkeb, M., Ennassiri, A., & Bourekkadi, S. (2023). Reducing Carbon Footprint with Real-Time Transport Planning and Big Data Analytics. E3S Web of Conferences, 412, 01082. https://doi.org/10.1051/e3sconf/202341201082
There are 27 citations in total.

Details

Primary Language English
Subjects Planning and Decision Making
Journal Section Information and Computing Sciences
Authors

Yıldıran Yılmaz 0000-0002-5337-6090

Publication Date March 26, 2025
Submission Date November 8, 2024
Acceptance Date January 6, 2025
Published in Issue Year 2025 Volume: 12 Issue: 1

Cite

APA Yılmaz, Y. (2025). Machine Learning-Enhanced Traffic Light Optimization System Prioritizing Emergency Vehicle Passage Using SVM and Random Forest Models. Gazi University Journal of Science Part A: Engineering and Innovation, 12(1), 175-196. https://doi.org/10.54287/gujsa.1581105
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