A HYBRID APPROACH TO TRAFFIC BREAKDOWN DETECTION USING DENSITY PERCENTILE ANALYSIS AND FUZZY LOGIC CONTROL
DOI:
https://doi.org/10.11113/mjce.v38.25562Keywords:
fuzzy logic control, percentile density analysis, traffic breakdownAbstract
This study produces a novel hybrid model to predict traffic flow breakdowns. The procedure started with the creation of a Percentile Density Approach (PDA) which identified traffic flow breakdown using a percentile -based analysis of density. In this method, traffic flow breakdown was found between traffic free-flow and congested states. Afterwards, a fuzzy logic approach was developed to advance the process into a model form whilst incorporating other factors. As a result, this model integrates the major parameters in the PDA, such as free-flow and congested density as well as weather conditions, driver behavior, and seasonal factors. Thus, the fuzzy hybrid logic system developed, uses membership functions built around both the PDA and these additional parameters enhanced by well-defined sets of crisps decision rules. This resulted in a model which describes traffic flow breakdown incorporating fundamental traffic variables with real-world uncertainties
References
Li, Z., Wang, Z., and Liu, Y., 2025, “A Multi-Regime Car-Following Model Capturing Traffic Breakdown,” Electron., 14(2): 1–17. DOI: https://doi.org/10.3390/electronics14020304.
Kerner, B. S., 2023, “Model of Driver Overacceleration Causing Breakdown in Vehicular Traffic,” Physical Review E 108(6): 064305. DOI: https://doi.org/10.1103/PhysRevE.108.064305.
Jovanovi´c, B., Ševrovi´, M. Š., and Luburi´, G., 2024, “Comparative Analysis of Deterministic Fundamental Diagrams Representative of Continuous and Interrupted Traffic Flow on Selected Regional Road in Croatia,” Applied Sciences, 14(533): 2–25. DOI: https://doi.org/10.3390/app14020533.
Shangguan, Y., Tian, X., Jin, S., Gao, K., Hu, X., Yi, W., and Guo, Y., 2023, “On the Fundamental Diagram for Freeway Traffic : Exploring the Lower Bound of the Fitting Error and Correcting the Generalized Linear Regression Models.” DOI: https://doi.org/10.3390/app14020533.
Jian, C., Lin, C., Hu, X., and Lu, J., 2025, “Selective Scale-Aware Network for Traffic Density Estimation and Congestion Detection in ITS,” Sensors, 25(3): 766. DOI: https://doi.org/10.3390/s25030766.
Pan, Y., Cheng, Q., Li, A., Zhang, J., Guo, J., and Chen, Y., 2025, “Analysis of Congestion Key Parameters, Dynamic Discharge Process, and Capacity Estimation at Urban Freeway Bottlenecks: A Case Study in Beijing, China,” Transportation Letters, 17(6): 984–1003. DOI: https://doi.org/10.1080/19427867.2024.2404349.
Hao, M.J., and Zheng, Y.X., 2025, “Highway Travel-Time Forecasting with Greenshields Model-Based Cascaded Fuzzy Logic Systems,” Applied Sciences, 15(14): 7729. DOI: https://doi.org/10.3390/app15147729.
Rabiu, U. S., Hassan, S. A., Mashros, N., Puan, O. C., and Azam, M., 2025, “Traffic Flow Mechanisms and Congestion Characteristics on the Keffi/Abuja Motorway,” Proceedings of the 3rd International Conference on Highway and Transportation Engineering (ICHITRA) Disaster Resiliency for Future Development in Transportation Engineering, M.S. Mohd, Khairul Idham, H. Yaacob, G.P. Ong, and S.N.N. Kamarudin, eds., Springer Nature Singapore Pte Ltd, Singapore, 189–203. DOI: https://doi.org/10.1007/978-981-96-5965-4_14.
Riabushenko, O., Sierpiński, G., Bogomolov, V., Nahliuk, I., and Leontiev, D., 2024, “Study of Distribution of Free Flow Speeds on Urban Road Sections Depending on Their Functional Purpose and One-Way Traffic—Evidence from Kharkiv (Ukraine),” Applied Sciences 14(23): 11302. DOI: https://doi.org/10.3390/app142311302.
Aboud, G. M., Khaled, T. T., Taher, E. S., Hashim, I. N., and Al-Humeidawi, B. H., 2023, “Evaluation of Speed, Flow, and Density Performance under Different Severity of Speed Bumps,” IOP Conference Series: Earth and Environmental Science, 1232(1): 012059. DOI: https://doi.org/10.1088/1755-1315/1232/1/012059.
Romanowska, A., and Jamroz, K., 2021, “Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment,” Applied Sciences, 11(21): 9914. DOI: https://doi.org/10.3390/app11219914.
Cipriani, E., Giannantoni, L., and Mannini, L., 2023, “Integrated Variable Speed Limits and User Information Strategy,” Sustainability, 15(14): 10954. DOI: https://doi.org/10.3390/su151410954.
Nanyondo, J., and Kasumba, H., 2024, “Analysis of Heterogeneous Vehicular Traffic: Using Proportional Densities,” Physica A: Statistical Mechanics and its Applications. 633: 129387. DOI: https://doi.org/10.1016/j.physa.2023.129387.
Schuhmann, F., Nguyen, N. A., Schweizer, J., Huang, W.-C., and Lienkamp, M., 2024, “Creating and Validating Hybrid Large-Scale, Multi-Modal Traffic Simulations for Efficient Transport Planning,” Smart Cities, 8(1): 2. DOI: https://doi.org/10.3390/smartcities8010002.
Jafari, S., 2024, “Research on Fuzzy Logic and Mathematics with Applications,” Symmetry (Basel)., 16(12): 1684. DOI: https://doi.org/10.3390/sym16121684.
Hamed, A., Hireche, S., Bekri, A., and Cheriet, A., 2025, “Designing Fuzzy Membership Functions Using Genetic Algorithm with a New Encoding Method, Indonesian Journal of Electrical Engineering and Computer Science 37(2): 781. DOI: https://doi.org/10.11591/ijeecs.v37.i2.pp781-788.
Alkaissi, Z.A. 2024, “Traffic Congestion Evaluation of Urban Streets Based on Fuzzy Inference System and GIS Application,” Ain Shams Engineering Journal 15(6): 102725. DOI: https://doi.org/10.1016/j.asej.2024.102725.
Taylor, N. B., 2025, “Form of the Equilibrium Speed-Flow-Density Relationship,” Transportation Research Procedia 82: 3076–3095. DOI: https://doi.org/10.1016/j.trpro.2024.12.236.
Zhang, X., Gao, Y., and Zhou, C., 2025, “Enhancing Vehicle Trajectory Quality: A Two‐Step Data Reconstruction Method Using Wavelet Transform and Normal Acceleration Value,” Engineering Reports. 7(1): 1–16. DOI: https://doi.org/10.1002/eng2.13090.
de Grange, L., Marechal, M., and González, F., 2019, “A Traffic Assignment Model Based on Link Densities, Journal of Advanced Transportation, 2019: 1–20. DOI: https://doi.org/10.1155/2019/5282879.
Shahhoseini, Z., Sarvi, M., Saberi, M., and Haghani, M., 2017, “Pedestrian Crowd Dynamics Observed at Merging Sections: Impact of Designs on Movement Efficiency,” Transportation Research Record 2622(1): 48–57. DOI: https://doi.org/10.3141/2622-05.
Haghani, M., 2021, “The Knowledge Domain of Crowd Dynamics: Anatomy of the Field, Pioneering Studies, Temporal Trends, Influential Entities and Outside-Domain Impact,” Physica A: Statistical Mechanics and its Applications 580: 126145. DOI: https://doi.org/10.1016/j.physa.2021.126145.
Duan, J., Zeng, G., Serok, N., Li, D., Lieberthal, E. B., Huang, H.-J., and Havlin, S., 2023, “Spatiotemporal Dynamics of Traffic Bottlenecks Yields an Early Signal of Heavy Congestions,” Nature Communications, 14(1): 8002. DOI: https://doi.org/10.1038/s41467-023-43591-7.
Jin, S., Yang, J., and Liu, Z., 2022, “Modeling and Analysis of Car-Following for Intelligent Connected Vehicles Considering Expected Speed in Helical Ramps,” Sustainability, 14(24): 16732. DOI: https://doi.org/10.3390/su142416732.
Asadifakhr, K., Roy, S. G., Taherkhani, A. H., Han, F., Bell, E. S., and Mo, W., 2025, “A Multi-Objective Genetic Algorithm Approach to Sustainable Road–Stream Crossing Management,” Sustainability, 17(9): 3987. DOI: https://doi.org/10.3390/su17093987.
Atrchian, C., Effati, M., and Davudi, M., 2023, “Seasonal Impact Analysis of Climatic Conditions on Freeways Light- Vehicle Traffic Volume with Temporal Adaptation of Weather Parameters and Traffic Information,” Amirkabir Journal of Civil Engineering, 54(10): 3699–3722. DOI: https://doi.org/10.22060/ceej.2022.20503.7443.
Hu, Y., Ma, T., and Chen, J., 2021, “Multi-Anticipative Bi-Directional Visual Field Traffic Flow Models in the Connected Vehicle Environment,” Physica A: Statistical Mechanics and its Applications,584: 126372. DOI: https://doi.org/10.1016/j.physa.2021.126372.
Zhang, X., Shi, Z., Yu, S., and Ma, L., 2023, “A New Car-Following Model Considering Driver’s Desired Visual Angle on Sharp Curves,” Physica A: Statistical Mechanics and its Applications, 615: 128551. DOI: https://doi.org/10.1016/j.physa.2023.128551.
Liu, Y., Zhou, A., Wang, Y., and Peeta, S., 2023, “Proactive Longitudinal Control to Preclude Disruptive Lane Changes of Human-Driven Vehicles in Mixed-Flow Traffic,” Control Engineering Practice, 136: 105522. DOI: https://doi.org/10.1016/j.conengprac.2023.105522.
Szymlet, N., Rymaniak, Ł., and Lijewski, P., 2024, “Two-Wheeled Urban Vehicles—A Review of Emissions Test Regulations and Literature,” Energies, 17(3): 586. DOI: https://doi.org/10.3390/en17030586.
Pangsy-Kania, S., Biegańska, J., and Flouros, F., 2024, “Alternative Fuels as a Sustainable Innovation in Vehicle Fleet Across the EU – 27 : Diagnosis and Prospects for Development,” Comparative Economic Research. Central and Eastern Europe 27(4): 173–194. DOI: https://doi.org/10.18778/1508-2008.27.36.
Gulc, A., and Budna, K., 2024, “Classification of Smart and Sustainable Urban Mobility,” Energies, 17(9), p. 2148. DOI: https://doi.org/10.3390/en17092148.
Mohd Shafie, S. H., and Mahmud, M., 2020, “Urban Air Pollutant from Motor Vehicle Emissions in Kuala Lumpur, Malaysia,” Aerosol and Air Quality Research 20(12): 2793–2804. DOI: https://doi.org/10.4209/aaqr.2020.02.0074.
Liu, Y., Chen, H., Yin, C., Michalaki, V., Proctor, P., Rowley, G., Wang, X., and Wei, H., 2025, “A Flow-Speed Model for Motorways in England: Analysis Under Various Weather Conditions,” Atmosphere. 16(2). DOI: https://doi.org/10.3390/atmos16020117.
Albalate, D., and Fageda, X., 2021, “On the Relationship between Congestion and Road Safety in Cities,” Transport Policy 05(May 2020): 145–152. DOI: https://doi.org/10.1016/j.tranpol.2021.03.011.
Almeida, A., Brás, S., Oliveira, I., and Sargento, S., 2022, “Vehicular Traffic Flow Prediction Using Deployed Traffic Counters in a City,” Future Generation Computer Systems. 128: 429–442. DOI: https://doi.org/10.1016/j.future.2021.10.022.
Pániková, Z., Drliciak, M., Celko, J., and Cingel, M., 2023, “Evaluation of the Traffic Flow Characteristics in Relation to the Platoon Occurrences,” Transportation Research Procedia, 74(2022): 1046–1053. DOI: https://doi.org/10.1016/j.trpro.2023.11.242.
(Eric) Li, Y., Hao, H., Gibbons, R. B., and Medina, A., 2021, “Understanding Gap Acceptance Behavior at Unsignalized Intersections Using Naturalistic Driving Study Data,” Transportation Research Record. 2675(9): 1345–1358. DOI: https://doi.org/10.1177/03611981211007140.
Islam, S., and Filipovska, M., 2024, “Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach,” Transportation Research Record 2678(11): 2092–2109. DOI: https://doi.org/10.1177/03611981241247050.
