- Open Access
- Article
Fuzzy-Based Approach for Classifying Road Traffic Conditions: A Case Study on the Padua-Venice Motorway
by Gizem Erdinc1 , Chiara Colombaroni 2 and Gaetano Fusco 3
1 gizemerdincc@gmail.com, Dept. of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Rome, 00184, Italy
2 chiara.colombaroni@uniroma1.it, Dept. of Civil, Constructional and Environmental Eng, Sapienza University of Rome, Rome, 00184, Italy
3 gaetano.fusco@uniroma1.it, Dept. of Civil, Constructional and Environmental Engineering, Sapienza University of Rome, Rome, 00184, Italy
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 11, Page # 1-2, 2024; DOI: 10.55708/js0311003
Keywords: K Fuzzy traffic congestion estimation, Qualitative traffic state classification, Fuzzy approach
Received: 11 September 2024, Revised: 14 October 2024, Accepted: 15 October 2024, Published Online: 07 November 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Erdinc, G., Colombaroni, C., & Fusco, G. (2024). Fuzzy-based approach for classifying road traffic conditions: A case study on the Padua-Venice motorway. Journal of Engineering Research and Sciences, 3(11), 31-40. https://doi.org/10.55708/js03110003
Chicago/Turabian Style
Erdinc, Gizem, Chiara Colombaroni, and Gaetano Fusco. “Fuzzy-Based Approach for Classifying Road Traffic Conditions: A Case Study on the Padua-Venice Motorway.” Journal of Engineering Research and Sciences 3, no. 11 (2024): 31-40. https://doi.org/10.55708/js03110003.
IEEE Style
G. Erdinc, C. Colombaroni, and G. Fusco, “Fuzzy-based approach for classifying road traffic conditions: A case study on the Padua-Venice motorway,” Journal of Engineering Research and Sciences, vol. 3, no. 11, pp. 31-40, 2024, doi: 10.55708/js03110003.
This study offers a fuzzy-based method for determining the variety of traffic conditions on roads. The fuzzy approach appears more appropriate than the deterministic technique for giving drivers qualitative information about the present traffic condition, as drivers have a shaky understanding of the traffic status. It was used in an analysis that included flow, occupancy, and speed measurements from the Italian freeway that runs between Padua and Venice. MATLAB is used in the application’s development. The empirical findings demonstrate how effectively the suggested study performs in classification. The experiment can offer a straightforward and distinctive viewpoint for induction and traffic control on motorways.
- Y. Liu, C. Zhen, D. Huili, “Highway traffic congestion detection and evaluation based on deep learning techniques.” Soft Computing vol. 27, no.17, 12249-12265, 2023, doi.org/10.1007/s00500-023-08821-6.
- C. Wang, C. Yuting, W. Jie, Q. Jinjin, “An improved crowddet algorithm for traffic congestion detection in expressway scenarios.” Applied Sciences , vol. 13, no. 12, 2023, 7174, doi.org/10.3390/app13127174.
- T. G. Huang, L. H. Xu, and X. Y. Kuang. “Urban road traffic state identification based on fuzzy C-mean clustering.” Journal of Chongqing Jiaotong University (Natural Science), vol. 34, no. 2, 101-106, 2015.
- H.W. Liu, Y.T. Wang, X.K. Wang, Y. Liu, Y. Liu, X.Y. Zhang, F. Xiao, “Cloud Model-Based Fuzzy Inference System for Short-Term Traffic Flow Prediction”, Mathematics, vol. 11, no. 11, 2509, 2023, doi.org/10.3390/math11112509.
- Z. Liu, C. Lyu, Z. Wang, S. Wang, P. Liu, Q. Meng, 2023 “A Gaussian-process-based data-driven traffic flow model and its application in road capacity analysis.” IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 2, 1544-1563, 2023, doi.org/10.1109/TITS.2022.3223982.
- K. Zhao, G. Dudu, S. Miao, Z. Chenao, S. Hongbo, “Short-term traffic flow prediction based on VMD and IDBO-LSTM.” IEEE Access 2023, doi.org/10.1109/ACCESS.2023.3312711.
- J, Ying, “Research on methods of urban road traffic condition identification”, Qingdao Univ. (Eng. Technol. Ed.), vol. 27, no. 3, 84–87, 2012, doi.org/10.1007/978-981-19-1057-9_26.
- P.H. Bovy, I. Salomon, “Congestion in Europe: Measurements, patterns and policies.” Travel behaviour: spatial patterns, congestion and modelling, 143-179, 2002, doi.org/10.4337/9781781951187.
- A. Almeida, S. Brás, S. Sargento, I. Oliveira, “Exploring bus tracking data to characterize urban traffic congestion”. Journal of Urban Mobility, vol. 4, 100065, 2023, doi.org/10.1016/j.urbmob.2023.100065.
- M.A.P. Taylor, “Exploring the nature of urban traffic congestion: concepts, parameters, theories and models.” In Proceedings, 16th Arrb Conference, 9-13 November, Perth, Western Australia, Vol. 16, No. 5. 1992.
- I. Pedram, S. Salek, R. Omrani, Defining and Measuring Urban Congestion—Guidelines for Defining and Measuring Urban Congestion, Transportation Association of Canada: Ottawa, ON, Canada. 2017, Access (December 2020).
- ECM, The spread of congestion in Europe, 11th Round Table on Transport Economics, OECD Publication Service, 1999.
- M. Aftabuzzaman, “Measuring traffic congestion – a critical review”, Proceedings of the 30th Australasian Transport Research Forum, 16, 2007.
- J. Xia, W. Huang, J. Guo, “A clustering approach to online freeway traffic state identification using ITS data”, KSCE Journal of Civil Engineering, vol. 16, 426-432, 2012, doi 10.1007/s12205-012-1233-1.
- B. Priambodo, A. Azlin, A.K. Rabiah, “Prediction of average speed based on relationships between neighbouring roads using K-NN and neural network.”, 18-33, 2020, doi.org/10.3991/ijoe.v16i01.11671.
- N. Tilahun, P. Thakuriah, “Incorporating weather information into real-time speed estimates: Comparison of alternative models”, J. Transp. Eng., vol. 139, no.4, 379–389, 2012, doi.org/10.1061/(ASCE)TE.1943-5436.0000506.
- L. Zhang, J. Ma, X. Liu, M. Zhang, X. Duan, Z. Wang, “A novel support vector machine model of traffic state identification of urban expressway integrating parallel genetic and C-means clustering algorithm”, Tehnički vjesnik, vol. 29, no. 3, 731-741, 2022, doi.org/10.17559/TV-20211201014622.
- Y. Zhu, Y. Zheng, “Retracted article: traffic identification and traffic analysis based on support vector machine.” Neural Computing and Applications, vol. 32, no. 7, 1903-1911, 2020, doi.org/10.1007/s00521-019-04493-2.
- S. Dong, “Multi class SVM algorithm with active learning for network traffic classification.” Expert Systems with Applications vol. 176, 114885, 2021, doi.org/10.1016/j.eswa.2021.114885.
- D. Mladenović, S. Janković, S. Zdravković, S. Mladenović, A. Uzelac, “Night traffic flow prediction using K-nearest neighbors algorithm”, Operational research in engineering sciences: theory and applications, vol. 5, no. 1, 152-168, 2022, doi.org/10.31181/oresta240322136m.
- F. Harrou, Z. Abdelhafid, S. Ying, “Traffic congestion monitoring using an improved kNN strategy.” Measurement, vol. 156, 107534, 2020, doi.org/10.1016/j.measurement.2020.107534.
- T. Seo, T. Kusakabe, Y. Asakura, “Calibration of fundamental diagram using trajectories of probe vehicles: Basic formulation and heuristic algorithm”, Transportation Research Procedia, vol. 21, 6-17, 2017, doi.org/10.1016/j.trpro.2017.03.073.
- C.G. Claudel, A.M. Bayen, “Guaranteed bounds for traffic flow parameters estimation using mixed Lagrangian-Eulerian sensing,” 46th Annual Allerton Conference on Communication, Control, and Computing, 636–645, 2008, doi.org/10.1109/ALLERTON.2008.4797618.
- J. Jiang, N. Dellaert, T. Van Woensel, L. Wu, “Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances”, Transportation, vol. 47, 2951-2980, 2020, doi.org/10.1007/s11116-019-09997-3.
- G.B. Whitham, Linear and Nonlinear Waves, Wiley-Interscience, 1974.
- R. Pradhan, S. Shrestha, D. Gurung, “Mathematical modeling of mixed-traffic in urban areas.” Mathematical modeling and computing, vol. 9, No. 2, 226-240, 2022, doi: 10.23939/mmc2022.02.226.
- A. Iftikhar, Z.H. Khan, T.A. Gulliver, K.S. Khattak, M.A. Khan, M. Ali, N, Minallahe, “Macroscopic traffic flow characterization at bottlenecks”, Civil Engineering Journal, vol. 6, no. 7, 1227-1242, 2020, doi.org/10.28991/cej-2020-03091543.
- J. Junfeng, H. Wang, “Traffic behavior recognition from traffic videos under occlusion condition: a Kalman filter approach.” Transportation research record, vol. 2676, no. 7, 55-65, 2022, doi.org/10.1177/03611981221076426.
- F. Zheng, S.E. Jabari, H.X. Liu, D. Lin, “Traffic state estimation using stochastic Lagrangian dynamics”, Transportation Research Part B: Methodological, vol. 115, 143-165, 2018, doi.org/10.1016/j.trb.2018.07.004.
- Y. Liu, Z. Cai, H. Dou, “Highway traffic congestion detection and evaluation based on deep learning techniques”, Soft Comput, vol. 27, no. 20, 12249–12265, 2023, https://doi.org/10.1007/s00500-023-08821-6.
- A. Nantes, D. Ngoduy, A. Bhaskar, M. Miska, E. Chung, “Real-time traffic state estimation in urban corridors from heterogeneous data”, Transportation Research Part C: Emerging Technologies, vol. 66, 99-118, 2016, doi.org/10.1016/j.trc.2015.07.005.
- C. Wang, W. Zhang, C. Wu, H. Hu, H. Ding, W. Zhu, “A traffic state recognition model based on feature map and deep learning” Physica A: Statistical Mechanics and its Applications, vol. 607, 128-198, 2022, doi.org/10.1016/j.physa.2022.128198.
- T.D. Toan, Y.D. Wong, “Fuzzy logic-based methodology for quantification of traffic congestion”, Physica A: Statistical Mechanics and its Applications, vol. 570, p.125784, 2021, doi.org/10.1016/j.physa.2021.125784.
- A. Skabardonis, P. Varaiya, K.F. Petty, “Measuring recurrent and nonrecurrent traffic congestion”, Transportation Research Record, vol. 1856, no. 1, 118-124, 2003, doi.org/10.3141/1856-12.
- Erdinç, G., Colombaroni, C. and Fusco, G., “Two-stage fuzzy traffic congestion detector”, Future transportation, vol.3, no. 3, 840-857, 2023, doi.org/10.3390/futuretransp3030047.
- G. Erdinç, C. Colombaroni, G. Fusco, “Application of a Mamdani-Based Fuzzy Traffic State Identifier to a Real Case Study”, In 2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) 1-6, IEEE, 2023, doi.org/10.1109/MT-ITS56129.2023.10241428.
- National Academies of Sciences, Engineering, and Medicine, Highway Capacity Manual 7th Edition: A Guide for Multimodal Mobility Analysis, Washington, DC: The National Academies Press, 2022, doi.org/10.17226/26432.
- G. Hewei, A.S. Sadiq, M.A. Taheir, “Adaptive fuzzy-logic traffic control approach based on volunteer IoT agent mechanism”, SN Computer Science, vol. 3, no. 1, p.68, 2022, doi.org/10.1007/s42979-021-00956-3.
- B. Yulianto, “Adaptive Traffic Signal Control Using Fuzzy Logic Under Mixed Traffic Conditions”, Proceedings of the 5th International Conference on Rehabilitation and Maintenance in Civil Engineering. ICRMCE 2021. Lecture Notes in Civil Engineering, vol 225. Springer, Singapore, doi.org/10.1007/978-981-16-9348-9_59.
- A. Agrahari, M.M. Dhabu, P.S. Deshpande, A. Tiwari, M.A. Baig, A.D. Sawarkar, “Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review”, Electronics, vol.13, no. 19, p.3875, 2024, doi.org/10.3390/electronics13193875.