Traffic forecast is a critical aspect of effective traffic management and planning in cyber-physical systems (CPS). In this study, we present a novel approach to traffic prediction and regulation within cyber-physical systems (CPS), introducing the Gradient Rule based Fuzzy Controller. This innovative methodology utilizes dynamic fuzzy logic control enhanced with gradient-based rules to adapt signal timings in real-time, effectively addressing the variable nature of traffic. Our results demonstrate significant improvements in reducing total queue length and delay at intersections, with reductions of up to 91.23%. Furthermore, extensive simulations and evaluations underscore the superiority of our approach compared to state-of-the-art models, highlighting its flexibility and adaptability to diverse traffic scenarios. This research emphasizes the novelty of integrating gradient-based rules into fuzzy control techniques, offering a promising avenue for advancing traffic management systems in CPS environments.
L. Li, X. Qu, J. Zhang, Y. Wang, and B. Ran, “Traffic speed prediction for intelligent transportation system based on a deep feature fusion model,” Journal of Intelligent Transportation Systems, vol. 23, no. 6, pp. 605–616, Mar. 2019, doi: 10.1080/15472450.2019.1583965.
S. Li, H. He, and P. Zhao, “Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective,” Energy, vol. 230, p. 120890, Sep. 2021, doi: 10.1016/j.energy.2021.120890.
J. Wang and Q. Shi, “Short-term traffic speed forecasting hybrid model based on Chaos–Wavelet Analysis-Support Vector Machine theory,” Transportation Research Part C: Emerging Technologies, vol. 27, pp. 219–232, Feb. 2013, doi: 10.1016/j.trc.2012.08.004.
T. Afrin and N. Yodo, “A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System,” Sustainability, vol. 12, no. 11, p. 4660, Jun. 2020, doi: 10.3390/su12114660.
C. Chen, X. Liu, T. Qiu, and A. K. Sangaiah, “A short-term traffic prediction model in the vehicular cyber–physical systems,” Future Generation Computer Systems, vol. 105, pp. 894–903, Apr. 2020, doi: 10.1016/j.future.2017.06.006.
S. Luo, L. Zhang, and N. Guo, “Architecture of Cyber-Physical Systems Based on Cloud,” 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), May 2019, doi: 10.1109/bigdatasecurity-hpsc-ids.2019.00055.
Y. Meng and L. Kwok, “A case study: Intelligent false alarm reduction using fuzzy if-then rules in network intrusion detection,” 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, May 2012, doi: 10.1109/fskd.2012.6233768.
W. Li, W. Meng, C. Su, and L. F. Kwok, “Towards False Alarm Reduction Using Fuzzy If-Then Rules for Medical Cyber Physical Systems,” IEEE Access, vol. 6, pp. 6530–6539, 2018, doi: 10.1109/access.2018.2794685.
R. S. Nandhini and R. Lakshmanan, “QCNN_BaOpt: Multi-Dimensional Data-Based Traffic-Volume Prediction in Cyber–Physical Systems,” Sensors, vol. 23, no. 3, p. 1485, Jan. 2023, doi: 10.3390/s23031485.
K. S. Umadevi, K. S. Thakare, S. Patil, R. Raut, A. K. Dwivedi, and A. H, “Dynamic hidden feature space detection of noisy image set by weight binarization,” Signal, Image and Video Processing, vol. 17, no. 3, pp. 761–768, Aug. 2022, doi: 10.1007/s11760-022-02284-2.
A. E. Ibor, O. B. Okunoye, F. A. Oladeji, and K. A. Abdulsalam, “Novel Hybrid Model for Intrusion Prediction on Cyber Physical Systems’ Communication Networks based on Bio-inspired Deep Neural Network Structure,” Journal of Information Security and Applications, vol. 65, p. 103107, Mar. 2022, doi: 10.1016/j.jisa.2021.103107.
H. I. Kure, S. Islam, M. Ghazanfar, A. Raza, and M. Pasha, “Asset criticality and risk prediction for an effective cybersecurity risk management of cyber-physical system,” Neural Computing and Applications, vol. 34, no. 1, pp. 493–514, Aug. 2021, doi: 10.1007/s00521-021-06400-0.
J. A. Guzman and F. Nunez, “A Cyber-Physical Systems Approach to Collaborative Intersection Management and Control,” IEEE Access, vol. 9, pp. 99617–99632, 2021, doi: 10.1109/access.2021.3096330.
J. Tang, J. Zeng, Y. Wang, H. Yuan, F. Liu, and H. Huang, “Traffic flow prediction on urban road network based on License Plate Recognition data: combining attention-LSTM with Genetic Algorithm,” Transportmetrica A: Transport Science, vol. 17, no. 4, pp. 1217–1243, Dec. 2020, doi: 10.1080/23249935.2020.1845250.
S. Jafari, Z. Shahbazi, and Y.-C. Byun, “Traffic Control Prediction Design Based on Fuzzy Logic and Lyapunov Approaches to Improve the Performance of Road Intersection,” Processes, vol. 9, no. 12, p. 2205, Dec. 2021, doi: 10.3390/pr9122205.
J. Bethge, B. Morabito, H. Rewald, A. Ahsan, S. Sorgatz, and R. Findeisen, “Modelling Human Driving Behavior for Constrained Model Predictive Control in Mixed Traffic at Intersections,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 14356–14362, 2020, doi: 10.1016/j.ifacol.2020.12.1387.
V. Padmajothi and J. L. M. Iqbal, “RETRACTED ARTICLE: Adaptive neural fuzzy inference system-based scheduler for cyber–physical system,” Soft Computing, vol. 24, no. 22, pp. 17309–17318, May 2020, doi: 10.1007/s00500-020-05020-5.
S. S. Lakshmi and M. U. Rani, “Multi-Document Text Summarization Using Deep Learning Algorithm with Fuzzy Logic,” SSRN Electronic Journal, 2018, doi: 10.2139/ssrn.3165331.
J. Zhou, H.-N. Dai, H. Wang, and T. Wang, “Wide-Attention and Deep-Composite Model for Traffic Flow Prediction in Transportation Cyber–Physical Systems,” IEEE Transactions on Industrial Informatics, vol. 17, no. 5, pp. 3431–3440, May 2021, doi: 10.1109/tii.2020.3003133.
W. Chen et al., “A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial–temporal data features,” Future Generation Computer Systems, vol. 89, pp. 78–88, Dec. 2018, doi: 10.1016/j.future.2018.06.021.
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Ramanathan Lakshmanan
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Cite this article
Ramesh Sneka Nandhini and Ramanathan Lakshmanan, “Enhancing Traffic Management in
Cyber Physical Systems – A Gradient Based Fuzzy Controller Approach”,
Journal of Machine and Computing, pp. 886-894, October 2024. doi:10.53759/7669/jmc202404082.
