A Framework for Smart City Traffic Management utilizing BDA and IoT
Received: 3 June 2024 | Revised: 18 June 2024 | Accepted: 20 June 2024 | Online: 2 December 2024
Corresponding author: Jayalakshmi Nagalapuram
Abstract
This paper explores a new approach to traffic flow optimization in smart cities, harnessing the combined power of Big Data Analytics (BDA) and the Internet of Things (IoT). The system utilizes a citywide network of connected sensors to acquire live traffic information, including vehicle speeds, density, and congestion points. These data are thereafter processed applying some top-notch BDA algorithms to identify traffic anomalies and forecast congestion levels, and generate actionable insights. By analyzing this information, the system can dynamically adjust traffic signals, recommend alternative routes, and improve traffic efficiency in real-time. The system's adaptive learning capabilities allow it to continuously enhance its predictions based on new data, ensuring its effectiveness in managing evolving traffic patterns. This intelligent traffic management solution promises to significantly reduce congestion, ameliorate overall mobility and road safety, and contribute to a more sustainable city environment.
Keywords:
big data, BDA, IoT, industrial internetDownloads
References
H. Khayyam, B. Javadi, M. Jalili, and R. N. Jazar, "Artificial Intelligence and Internet of Things for Autonomous Vehicles," in Nonlinear Approaches in Engineering Applications: Automotive Applications of Engineering Problems, R. N. Jazar and L. Dai, Eds. New York, NY, USA: Springer, 2020, pp. 39–68.
V. K. Quy et al., "IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges," Applied Sciences, vol. 12, no. 7, Jan. 2022, Art. no. 3396.
Z. Lv et al., "Managing Big City Information Based on WebVRGIS," IEEE Access, vol. 4, pp. 407–415, 2016.
A. Ndikumana, S. Ullah, K. Thar, N. H. Tran, B. J. Park, and C. S. Hong, "Novel Cooperative and Fully-Distributed Congestion Control Mechanism for Content Centric Networking," IEEE Access, vol. 5, pp. 27691–27706, 2017.
J. Nie, J. Zhang, W. Ding, X. Wan, X. Chen, and B. Ran, "Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles," IEEE Access, vol. 4, pp. 9413–9420, 2016.
B. Liu, T. Zhang, and W. Hu, "Intelligent Traffic Flow Prediction and Analysis Based on Internet of Things and Big Data," Computational Intelligence and Neuroscience, vol. 2022, no. 1, 2022, Art. no. 6420799.
G. Kotapati, M. A. Ali, and R. Vatambeti, "Deep Learning-Enhanced Hybrid Fruit Fly Optimization for Intelligent Traffic Control in Smart Urban Communities," Mechatronics and Intelligent Transportation Systems, vol. 2, no. 2, pp. 89–101, Jun. 2023.
A. B. F. Khan and P. Ivan, "Integrating Machine Learning and Deep Learning in Smart Cities for Enhanced Traffic Congestion Management: An Empirical Review," Journal of Urban Development and Management, vol. 2, no. 4, pp. 211–221, Dec. 2023.
M. W. Levin, S. D. Boyles, J. Duthie, and C. M. Pool, "Demand Profiling for Dynamic Traffic Assignment by Integrating Departure Time Choice and Trip Distribution," Computer-Aided Civil and Infrastructure Engineering, vol. 31, no. 2, pp. 86–99, 2016.
D. Xia, B. Wang, H. Li, Y. Li, and Z. Zhang, "A distributed spatial–temporal weighted model on MapReduce for short-term traffic flow forecasting," Neurocomputing, vol. 179, pp. 246–263, Feb. 2016.
X. Kong, X. Song, F. Xia, H. Guo, J. Wang, and A. Tolba, "LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data," World Wide Web, vol. 21, no. 3, pp. 825–847, May 2018.
S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, "Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting," Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 922–929, Jul. 2019.
Y. Zhang, Q. Cheng, Y. Liu, and Z. Liu, "Full-scale spatio-temporal traffic flow estimation for city-wide networks: a transfer learning based approach," Transportmetrica B: Transport Dynamics, vol. 11, no. 1, pp. 869–895, Dec. 2023.
G. Sun, R. Qi, Y. Liu, and F. Xu, "A dynamic traffic signal scheduling system based on improved greedy algorithm," PLOS ONE, vol. 19, no. 3, Mar. 2024, Art. no. e0298417.
T. Kabudi, I. Pappas, and D. H. Olsen, "AI-enabled adaptive learning systems: A systematic mapping of the literature," Computers and Education: Artificial Intelligence, vol. 2, Jan. 2021, Art. no. 100017.
I. Lee and K. Lee, "The Internet of Things (IoT): Applications, investments, and challenges for enterprises," Business Horizons, vol. 58, no. 4, pp. 431–440, Jul. 2015.
J. Mills, J. Hu, and G. Min, "Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT," IEEE Internet of Things Journal, vol. 7, no. 7, pp. 5986–5994, Jul. 2020.
M. Elassy, M. Al-Hattab, M. Takruri, and S. Badawi, "Intelligent transportation systems for sustainable smart cities," Transportation Engineering, vol. 16, Jun. 2024, Art. no. 100252.
Y. Gong, L. Zhang, R. Liu, K. Yu, and G. Srivastava, "Nonlinear MIMO for Industrial Internet of Things in Cyber–Physical Systems," IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5533–5541, Aug. 2021.
S. Uppoor, O. Trullols-Cruces, M. Fiore, and J. M. Barcelo-Ordinas, "Generation and Analysis of a Large-Scale Urban Vehicular Mobility Dataset," IEEE Transactions on Mobile Computing, vol. 13, no. 5, pp. 1061–1075, May 2014.
M. Khan, B. N. Silva, and K. Han, "Internet of Things Based Energy Aware Smart Home Control System," IEEE Access, vol. 4, pp. 7556–7566, 2016.
"Tabular Playground Series - Mar 2022." [Online]. Available: https://kaggle.com/competitions/tabular-playground-series-mar-2022.
Downloads
How to Cite
License
Copyright (c) 2024 Jayalakshmi Nagalapuram, S. Samundeeswari
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
