THERMAL SCIENCE

International Scientific Journal

AN EFFICIENT ALGORITHM FOR TRAFFIC FLOW EVALUATION ON SMART CITIES BASED ON DEEP LEARNING

ABSTRACT
Following the emergence of the knowledge-based economy, the digital economy, and the intelligent economy, smart cities are poised to represent the next phase in urban development. These cities aim not only to leverage both physical and digital infrastructures for urban advancement but also to harness intellectual and social capital as essential elements of urbanization. Smart cities are recognized as pivotal catalysts for transforming various sectors by integrating multiple municipal systems such as transportation, healthcare, and operational frameworks. The concept of a smart society evolves from smart cities, characterized by a digitally interconnected, knowledge-driven community that actively pursues social, environmental, and economic sustainability. Recently, deep learning has gained traction due to its ability to effectively tackle complex problems across diverse applications using both supervised and unsupervised learning methods. This approach relies on advanced techniques for managing large datasets and multilayer neural networks, which often outperform traditional ANN in processing historical data. This paper introduces a novel algorithm based on deep learning designed to accurately predict traffic flow behavior. The algorithm learns from multivariate sequence data by analyzing spatio-temporal dependencies and non-linear correlations. Simulation results demonstrate that the proposed method surpasses existing algorithms in performance.
KEYWORDS
PAPER SUBMITTED: 2024-09-20
PAPER REVISED: 2024-11-13
PAPER ACCEPTED: 2024-12-17
PUBLISHED ONLINE: 2025-06-01
DOI REFERENCE: https://doi.org/10.2298/TSCI2502597B
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2025, VOLUME 29, ISSUE Issue 2, PAGES [1597 - 1606]
REFERENCES
  1. Atzori, L., et al., The Internet of Things: A Survey, Computer Networks, 54 (2010), 15, pp. 2787-2805
  2. Jiang, D., The Construction of Smart City Information System Based on the Internet of Things and Cloud Computing, Computer Communications, 150 (2020), Jan., pp. 158-166
  3. Gyrard, A., et al., Building IoT-based Applications for Smart Cities: How Can Onlogy Catalogs Help, IEEE Internet of Things Journal, 5 (2018), 5, pp. 3978-3990
  4. Kirimtat, A., et al., Future Trends and Current State of Smart City Concepts: A Survey, IEEE Access, 8 (2020), May, pp. 86448-86467
  5. Roblek, V., Meško, M., Smart City Knowledge Management: Holistic Review and the Analysis of the Urban Knowledge Management, Proceedings, 21st Annual International Conference on Digital Government Research, Seoul, Republic of Korea, 2020
  6. Tcholtchev, N., Schieferdecker, I., Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities, Smart Cities, 4 (2021), 1, pp. 156-176
  7. Al-Maqashi, S., et al., The Impact of ICTS in the Development of Smart City: Opportunities and Challenges, in: Smart Cities - Fund. and Perspe., In-Tech, Rijeka, Croatia, 2024
  8. Hui, C. X., et al., Greening Smart Cities: An Investigation of the Integration of Urban Natural Resources and Smart City Technologies for Promoting Environmental Sustainability, Sustainable Cities and Society, 99 (2023), 104985
  9. Alharbi, F., Fei, Z., Improving the Quality of Service for Critical Flows in Smart Grid Using Software-Defined Networking, Proceedings, International Conference on Smart Grid Communications (SmartGridComm), IEEE, Sydney, Australia, 2016
  10. Maha Vishnu, V., M. et al., Intelligent Traffic Video Surveillance and Accident Detection System with Dynamic Traffic Signal Control, Cluster Computing, 21 (2018), June, pp. 135-147
  11. Binsahaq, A., et al., A Survey on Autonomic Provisioning and Management of QoS in SDN Networks, IEEE Access, 7 (2019), May, pp. 73384-73435
  12. Braden, R., et al., Integrated Services in the Internet Architecture: An Overview, RFC1633, 1994
  13. Roddav, N., et al., On the Usage of DSCP and ECN Codepoints in Internet Backbone Traffic Traces for IPv4 and IPv6, Proceedings, International Symposium on Networks, Computers and Communications (ISNCC), IEEE, Istanbul, Turkey, 2019
  14. Tahaei, H., et al., The Rise of Traffic Classification in IoT networks: A Survey, Journal of Network and Computer Applications, 154 (2020), 102538
  15. Nguyen, T. T., Armitage, G., A Survey of Techniques for Internet Traffic Classification Using Machine Learning, IEEE Communications Surveys and Tutorials, 10 (2008), 4, pp. 56-76
  16. Salman, O., et al., A Review on Machine Learning-Based Approaches for Internet Traffic Classification, Annals of Telecommunications, 75 (2020), 11, pp. 673-710
  17. Soil, A. C., Standard test Methods for Liquid Limit, Plastic Limit, and Plasticity Index of Soils, ASTM International, D4318-17e1, 2010
  18. Prasad, P., Enhancing Security in Software-Defined Networking (SDN) based IP Multicast Systems: Challenges and Opportunities, M. Sc. thesis, Univ. of Turku, Turku, Finland, 2024
  19. Tongaonkar, A., et al., Challenges in Network Application Identification, Proceedings, LEET, San Jose, Cal., USA, 2012
  20. Jurcut, A., et al., Security Considerations for Internet of Things: A Survey, SN Computer Science, 1 (2020), June, pp. 1-19
  21. Mohamed, N., et al., A Cost-Effective Design for Combining Sensing Robots and Fixed Sensors for Fault-Tolerant Linear Wireless Sensor Networks, International Journal of Distributed Sensor Networks, 10 (2014), 3, 659356
  22. Alqudah, N., Yaseen, Q., Machine Learning for Traffic Analysis: A Review, Procedia Computer Science, 170 (2020), Jan., pp. 911-916
  23. Xie, J., et al., A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges, IEEE Communications Surveys and Tutorials, 21 (2018), 1, pp. 393-430
  24. Yao, H., et al., Capsule Network Assisted IoT Traffic Classification Mechanism for Smart Cities, IEEE Internet of Things Journal, 6 (2019), 5, pp. 7515-7525
  25. Miao, Y., et al., Comprehensive Analysis of Network Traffic Data, Concurrency and Computation, Practice and Experience, 30 (2018), 5, e4181
  26. Mohamed, K., et al., Clustering Smart Card Data for Urban Mobility Analysis, IEEE Transactions on Intelligent Transportation Systems, 18 (2016), 3, pp. 712-728
  27. Chen, R., Zhou, J., Fare Adjustment's Impacts on Travel Patterns and Farebox Revenue: An Empirical Study Based on Longitudinal Smartcard Data, Transportation Research - Part A: Policy and Practice, 164 (2022), Oct., pp. 111-133
  28. Chen, E., et al., Unraveling Latent Transfer Patterns Between Metro and Bus from Large-Scale Smart Card Data, IEEE Transactions on Intelligent Transportation Systems, 23 (2020), 4, pp. 3351-3365
  29. Ma, X., et al., Evaluation and Determinants of Metro Users' Regularity: Insights from Transit One-Card Data, Journal of Transport Geography, 118 (2024), 103933
  30. Asad, S., et al., Blockchain-Based Decentralized Federated Learning for Privacy-Preserving Traffic Flow Prediction: A Case Study with PeMS-8 Data, Journal of Computing and Biomedical Informatics, 7 (2024.), 01, pp. 204-214
  31. Kim, C., et al., Decision Tree Analysis for Developing Weigh-in-Motion Spectra in the Californian Pavement Management System (PaveM), Transportation Research Record, 2678 (2024), 3, pp. 546-559
  32. Sun, X., et al., Short-Term Traffic Flow Prediction Model Based on a Shared Weight Gate Recurrent Unit Neural Network. Physica A, Statistical Mechanics and its Applications, 618 (2023), 128650
  33. Sarri, P., et al., Incorporating Land Use and Transport Interaction Models to Evaluate Active Mobility Measures and Interventions in Urban Areas: A Case Study in Southampton, UK, Sustainable Cities and Society, 105 (2024), 105330
  34. Yao, H., Guizani, M., Intelligent IoT Network Awareness, in: Intelligent Internet of Things Networks, Springer, Berlin, Germany, 2023, pp. 37-109
  35. Rodrigues, C., et al. Using CDR Data to Understand Post-pandemic Mobility Patterns, in: EPIA Conference on Artificial Intelligence, Springer, Berlin, Germany, 2023
  36. Dong, H., et al. GPSense: Passive Sensing with Pervasive GPS Signals, Proceedings, 30th Annual International Conference on Mobile Computing and Networking, Washington D.C., USA, 2024
  37. Giangualano, M., Implementation and Evaluation of a Map Matching Algorithm, M. Sc. thesis, Politecnico di Milano, Milano, Italy, 2023
  38. Li, Z., et al., Real-Time GNSS Precise Point Positioning with Smartphones for Vehicle Navigation, Satellite Navigation, 3 (2022), 1, 19
  39. Moore, A. W., Zuev, D., Internet Traffic Classification Using Bayesian Analysis Techniques, Proceedings, ACM SIGMETRICS International Conference on Measurement and Modelling of Computer Systems, New York, USA, 2005
  40. Awad, M., et al., Support Vector Machines for Classification, in: Efficient Learning Machines: Theories, Concepts, and Applications for Engineers and System Designers, Springer, New York, USA, 2015, pp. 39-66

2025 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence