A hybrid approach to urban traffic flow optimization: Integration of Karnaugh Maps and reinforcement learning

Abstract

This study presents a hybrid approach combining Karnaugh Maps (K-maps) and Reinforcement Learning (RL) to optimize traffic signal control in urban environments. K-maps are traditionally used for simplifying Boolean expressions in digital logic, and here they are leveraged to enhance decision-making efficiency in RL-based traffic systems. The proposed method aims to improve traffic flow, reduce congestion, and lower fuel consumption. Microsimulation experiments were conducted using software calibrated with historical traffic data from a mid-sized city. Sensor inputs-such as vehicle count, traffic density, and signal phase durations-were validated against real-world data provided by local traffic authorities. Results demonstrate that the hybrid K-map and RL system outperforms conventional methods in adaptability and performance. However, limitations remain due to the exclusion of unpredictable driver behavior and weather conditions, which may affect real-world applicability.

Keywords

• Traffic signal optimization
• Karnaugh maps
• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• Traffic flow optimization
• Urban transportation

Kentsel trafik akışı optimizasyonunda hibrit bir yaklaşım: Karnaugh Haritaları ve pekiştirmeli öğrenme entegrasyonu

Abstract

Bu çalışma, kentsel trafik sinyal kontrolünü optimize etmek amacıyla Karnaugh Haritaları (K-haritaları) ile Pekiştirmeli Öğrenme (Reinforcement Learning-RL) yöntemini birleştiren hibrit bir yaklaşım sunmaktadır. Sayısal mantıkta Boolean ifadelerini sadeleştirmek için kullanılan K-haritaları, burada RL tabanlı trafik sistemlerinde karar verme verimliliğini artırmak amacıyla kullanılmaktadır. Önerilen yöntem, trafik akışını iyileştirmeyi, tıkanıklığı azaltmayı ve yakıt tüketimini düşürmeyi hedeflemektedir. Yapılan mikro-simülasyon deneyleri, orta büyüklükte bir kente ait tarihsel trafik verileriyle kalibre edilmiş bir yazılım üzerinde gerçekleştirilmiştir. Araç sayısı, trafik yoğunluğu ve sinyal süreleri gibi sensör verileri, yerel trafik otoritelerinden elde edilen saha verileriyle doğrulanmıştır. Sonuçlar, hibrit K-haritası ve RL sisteminin geleneksel yöntemlere kıyasla daha uyarlanabilir ve performanslı olduğunu göstermektedir. Bununla birlikte, simülasyonda sürücü davranışlarındaki öngörülemezlikler ve hava koşullarına bağlı değişkenler dikkate alınmamıştır; bu durum, gerçek saha uygulamaları açısından bir sınırlılık oluşturmaktadır.

Keywords

• Trafik sinyali optimizasyonu
• Karnaugh haritaları
• Yapay Zekâ
• Makine Öğrenmesi
• Pekiştirmeli Öğrenme
• Trafik akışı optimizasyonu
• Kentsel ulaşım

References

1. [1] World Bank. “World Bank Open Data”. https://data.worldbank.org (25.04.2025).
2. [2] S&P Global Mobility. “S&P Global Mobility Forecasts 89.6M Auto Sales Worldwide in 2025”. https://press.spglobal.com/2024-12-20-S-P-Global-Mobility-forecasts-89-6M-auto-sales-worldwide-in-2025 (25.04.2025).
3. [3] Anadolu Agency. “Türkiye Posts Record 1.2 Million Auto Sales in 2023”. https://www.aa.com.tr/en/turkiye/turkiye-posts-record-12-million-auto-sales-in-2023/3100674 (25.04.2025).
4. [4] Kumar P, Pal N, Sharma H. “Optimization and techno-economic analysis of a solar photo-voltaic/biomass/diesel/battery hybrid off-grid power generation system for rural remote electrification in eastern India”. Energy, 247, 123560, 2022.
5. [5] Sharma AK, Sharma PK, Chintala V, Khatri N, Patel A. “Environment-friendly biodiesel/diesel blends for improving the exhaust emission and engine performance to reduce the pollutants emitted from transportation fleets”. International Journal of Environmental Research and Public Health, 17(11), 3896, 2020.
6. [6] Mueller EA. “Aspects of the history of traffic signals”. IEEE Transactions on Vehicular Technology, 19(1), 6-17, 1970.
7. [7] Kulkarni AR, Kumar N, Rao KR. “100 years of the ubiquitous traffic lights: An all-round review”. IETE Technical Review, 41(2), 212-225, 2024.
8. [8] Leduc G. “Road traffic data: Collection methods and applications”. European Commission Joint Research Centre, Working Papers on Energy, Transport and Climate Change, 1, 2008.

9. [9] Chase MJ, Hensen RJ. “Traffic control systems-past, present, and future”. Journal of Transportation Engineering, 116(6), 703-713, 1990.
10. [10] Cecchi M. “Integration of computerized systems for centralized traffic control”. Proceedings of the IVth World Congress of the International Road Safety Organization (Prevention Routiere Internationale), Tokyo, Japan, 5-7 June 1990.
11. [11] Lomax T, Turner S, Eisele B, Schrank D, Geng L, Shollar B. “Refining the Real-Timed Urban Mobility Report”. University Transportation Center for Mobility, Texas Transportation Institute, Texas, USA, Technical Report, UTCM 11-06-73, 2012.
12. [12] Papageorgiou M, Kosmatopoulos E, Papamichail I. “Effects of variable speed limits on motorway traffic flow”. Transportation Research Record: Journal of the Transportation Research Board, 2047(1), 37-48, 2008.
13. [13] Walraven E, Spaan MTJ, Bakker B. “Traffic flow optimization: A reinforcement learning approach”. Engineering Applications of Artificial Intelligence, 52, 203-212, 2016.
14. [14] Riaz MT, Aaqib SM, Ahmad S, Amin S, Ali H, Husnain S, Riaz S. “The intelligent transportation systems with advanced technology of sensor and network”. 2021 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan, 26-27 October 2021.
15. [15] Makys M, Kozak S. “Effective method for design of traffic lights control”. IFAC Proceedings Volumes, 44(1), 14934-14939, 2011.
16. [16] Ceylan H, Başkan Ö, Ceylan H, Haldenbilen S. “Mathematical solutions of vehicular delay components at signalized intersections based on approximate calculation method”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 13(2), 279-288, 2007.
17. [17] Helbing D. “Traffic and related self-driven many-particle systems”. Reviews of Modern Physics, 73(4), 1067-1141, 2001.
18. [18] Dey KC, Rayamajhi A, Chowdhury M, Bhavsar P, Martin J. “Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in a heterogeneous wireless network–performance evaluation”. Transportation Research Part C: Emerging Technologies, 68, 168-184, 2016.
19. [19] Daganzo CF. “The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory”. Transportation Research Part B: Methodological, 28(4), 269-287, 1994.
20. [20] Wang M, Wu L, Li J, Wu D, Ma C. “Urban traffic signal control with reinforcement learning from demonstration data”. 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 18-23 July 2022.
21. [21] Passmann C, Brenzel C, Meschenmoser R. “Wireless vehicle to vehicle warning system”. SAE Technical Paper, 2000-01-1307, 2000.
22. [22] Fares A, Gomaa W. “Freeway ramp-metering control based on reinforcement learning”. 11th IEEE International Conference on Control & Automation (ICCA), Taichung, Taiwan, 18-20 June 2014.
23. [23] Rezaee K, Abdulhai B, Abdelgawad H. “Application of reinforcement learning with continuous state space to ramp metering in real-world conditions”. 2012 15th International IEEE Conference on Intelligent Transportation Systems, Anchorage, AK, USA, 16-19 September 2012.
24. [24] Davarynejad M, Hegyi A, Vrancken J, van den Berg J. “Motorway ramp-metering control with queuing consideration using Q-learning”. 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), Washington, DC, USA, 5-7 October 2011.
25. [25] Spall JC, Chin DC. “Traffic-responsive signal timing for system-wide traffic control”. Transportation Research Part C: Emerging Technologies, 5(3), 153-163, 1997.
26. [26] Wiering MA. “Multi-agent reinforcement learning for traffic light control”. Machine Learning: Proceedings of the Seventeenth International Conference (ICML'2000), Stanford, CA, USA, 29 June-2 July 2000.
27. [27] Vrancken J, van Schuppen JH, dos Santos Soares M, Ottenhof F. “A hierarchical network model for road traffic control”. 2009 International Conference on Networking, Sensing and Control, Okayama, Japan, 26-29 March 2009.
28. [28] Kamkar I, Akbarzadeh-T MR, Yaghoobi M. “Intelligent water drops: A new optimization algorithm for solving the vehicle routing problem”. 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Türkiye, 10-13 October 2010.
29. [29] Capali B, Ceylan H. “A multi-objective meta-heuristic approach for the transit network design and frequency setting problem”. Transportation Planning and Technology, 43(8), 851-867, 2020.
30. [30] Karnaugh M. “The map method for synthesis of combinational logic circuits”. Transactions of the American Institute of Electrical Engineers, Part I: Communication and Electronics, 72(5), 593-599, 1953.