Predictive Modeling of Traffic Density for Adaptive Urban Road Lighting Applications

Öz

Accurate prediction of traffic density is a key requirement for adaptive road lighting systems that aim to improve energy efficiency without compromising road safety. Conventional lighting infrastructure operates on fixed schedules and maintains constant illumination throughout the night, resulting in unnecessary energy consumption during low-traffic hours. This study proposes a machine learning-based predictive modeling approach that uses artificial neural networks and extreme gradient boosting to forecast hourly traffic volumes and to inform dynamic lighting control. Traffic data recorded at two-minute intervals on Cendere Avenue in Istanbul between 2015 and 2017 were cleaned, aggregated to hourly totals, and used to train both models, with the dataset randomly divided into training and test subsets containing 70% and 30% of the observations, respectively. Candidate hyperparameter configurations were compared using the coefficient of determination and the mean absolute error. The best neural network, a four-layer structure with twenty-five neurons in each layer, reached a coefficient of determination of 0.766 and a mean absolute error of 261.3 vehicles per hour on the test set, whereas the best boosting model, with a maximum tree depth of five, one hundred trees, and a learning rate of 0.2, reached 0.833 and 221.3, respectively. Gradient boosting therefore provides higher accuracy than the neural network, making it the more suitable option for real-time deployment. The resulting forecasts provide a basis for lighting control strategies that reduce illumination in advance of periods of low expected demand. The findings indicate that traffic-aware lighting can lower operating costs and support sustainable urban development. Future work will incorporate environmental variables, such as weather and visibility, along with real-time data acquisition from sensor networks.

Anahtar Kelimeler

• Adaptive road lighting
• Traffic prediction
• Machine learning
• Energy efficiency
• Gradient boosting
• Smart cities

Etik Beyan

Ethics committee approval was not required for this study because no study was conducted on animals or humans.

Teşekkür

This study was derived from the research project "Development of a Smart Road Lighting Automation Algorithm within the Scope of Energy Efficiency" (Project No: MAB-2020-42809), supported by the Scientific Research Projects Coordination Unit of Istanbul Technical University. The author thanks Istanbul IT and Smart City Technologies Inc. (ISBAK) for providing the traffic data used in this study.

Kaynakça

1. Abi-Char, P. E., & Ashtaiwi, A. (2020). Towards intelligent and dynamic road speed adaptation model in smart cities. In 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) (pp. 1–6). IEEE. https://doi.org/10.1109/WiMob50308.2020.9253378
2. Agramelal, F., Sadik, M., El Hannani, A., & Moubarak, Y. (2022). A traffic-aware street lighting system based on fuzzy logic controller. In 2022 IEEE 18th International Colloquium on Signal Processing and Applications (CSPA) (pp. 132–137). IEEE. https://doi.org/10.1109/CSPA55076.2022.9782045
3. Asif, M., Shams, S., Hussain, S., Bhatti, J. A., Rashid, M., & Zeeshan-ul-Haque, M. (2022). Adaptive control of streetlights using deep learning for the optimization of energy consumption during late hours. Energies, 15(17), Article 6337. https://doi.org/10.3390/en15176337
4. Casavola, A., Franzè, G., Gagliardi, G., & Tedesco, F. (2022). A multi-agent trust and reputation mechanisms for the management of smart urban lighting systems. IFAC-PapersOnLine, 55(6), 545–550. https://doi.org/10.1016/j.ifacol.2022.07.185
5. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939785
6. Energy Market Regulatory Authority. (2026). Elektrik faturalarına esas tarife tabloları [Tariff tables applicable to electricity bills]. Energy Market Regulatory Authority. https://www.epdk.gov.tr/Detay/Icerik/3-1327/elektrik-faturalarina-esas-tarife-tablolari
7. Gagliardi, G., Lupia, M., Cario, G., Tedesco, F., Cicchello Gaccio, F., Lo Scudo, F., & Casavola, A. (2020). Advanced adaptive street lighting systems for smart cities. Smart Cities, 3(4), 1495–1512. https://doi.org/10.3390/smartcities3040071
8. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

9. International Energy Agency. (2006). Light’s labour’s lost: Policies for energy-efficient lighting. OECD/IEA. https://www.iea.org/reports/lights-labours-lost
10. International Energy Agency. (2018). World energy outlook 2018. International Energy Agency. https://www.iea.org/reports/world-energy-outlook-2018
11. Jiang, Y., Shuai, Y., He, X., Wen, X., & Lou, L. (2021). An energy-efficient street lighting approach based on traffic parameters measured by wireless sensing technology. IEEE Sensors Journal, 21(17), 19134–19143. https://doi.org/10.1109/JSEN.2021.3089208
12. Mohammad, A., Modabbir, Ashraf, I., & Kamal, M. M. (2023). Economic and environmental impact of energy efficient design of smart lighting system. Journal of the Institution of Engineers (India): Series B, 104(3), 679–692. https://doi.org/10.1007/s40031-023-00885-0
13. R Foundation for Statistical Computing. (2025). The R project for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/
14. Rahman, M. A., Asyhari, A. T., Obaidat, M. S., Kurniawan, I. F., Mukta, M. Y., & Vijayakumar, P. (2021). IoT-enabled light intensity-controlled seamless highway lighting system. IEEE Systems Journal, 15(1), 46–55. https://doi.org/10.1109/JSYST.2020.2975592
15. Turkish Standards Institution. (2015). Road lighting – Part 1: Selection of lighting classes (TSE CEN/TR 13201-1). Turkish Standards Institution.