Artificial Intelligence in Traffic Management: A Review of Smart Solutions and Urban Impact

References

[1] Abbasi, S., & Rahmani, A. M. (2023). Artificial intelligence and software modeling approaches in autonomous vehicles for safety management: A systematic review. Information, 14(10), 555.

[2] Adebukola, A. A., Navya, A. N., Jordan, F. J., Jenifer, N. J., & Begley, R. D. (2022). Cyber Security as a Threat to Health Care. Journal of Technology and Systems, 4(1), 32-64.

[3] Adegoke, A., (2023). Patients’ Reaction to Online Access to Their Electronic Medical Records: The Case of Diabetic Patients in the US. International Journal of Applied Sciences: Current and Future Research Trends, 19 (1), pp 105-115

[4] Adel, A. (2023). Unlocking the Future: Fostering Human–Machine Collaboration and Driving Intelligent Automation through Industry 5.0 in Smart Cities. Smart Cities, 6(5), 2742-2782.

[5] Allioui, H., & Mourdi, Y. (2023). Exploring the full potentials of IoT for better financial growth and stability: A comprehensive survey. Sensors, 23(19), 8015.

[6] Almulhim, A. I., & Cobbinah, P. B. (2023). Can rapid urbanization be sustainable? The case of Saudi Arabian cities. Habitat International, 139, 102884.

[7] Antal, H., & Bhutani, S. (2023). Identifying linkages between climate change, urbanisation, and population ageing for understanding vulnerability and risk to older people: A review. Ageing International, 48(3), 816-839.

[8] Atitallah, S. B., Driss, M., Boulila, W., & Ghézala, H. B. (2020). Leveraging Deep Learning and IoT big data analytics to support the smart cities development: Review and future directions. Computer Science Review, 38, 100303.

[9] Bazzan, A. L., & Klügl, F. (2022). Introduction to intelligent systems in traffic and transportation. Springer Nature.

[10] Bharadiya, J. (2023). Artificial Intelligence in Transportation Systems A Critical Review. American Journal of Computing and Engineering, 6(1), 34-45.

[11] Bibri, S. E. (2019). On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review. Journal of Big Data, 6(1), 1-64.

[12] Chen, Z., van Lierop, D., & Ettema, D. (2020). Dockless bike-sharing systems: what are the implications?. Transport Reviews, 40(3), 333-353.

[13] Chi, H. R., Wu, C. K., Huang, N. F., Tsang, K. F., & Radwan, A. (2022). A survey of network automation for industrial internet-of-things toward industry 5.0. IEEE Transactions on Industrial Informatics, 19(2), 2065-2077.

[14] Chi, X. (2023). Uber’s Influence on The Urban Transportation Ecosystem: Impacts, Challenges and Prospects. Highlights in Business, Economics and Management, 20, 447-453.

[15] Chidolue, O. and Iqbal, T., 2023, March. System Monitoring and Data logging using PLX-DAQ for Solar-Powered Oil Well Pumping. In 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC) (pp. 0690-0694). IEEE.

[16] Cunneen, M., Mullins, M., & Murphy, F. (2019). Autonomous vehicles and embedded artificial intelligence: The challenges of framing machine driving decisions. Applied Artificial Intelligence, 33(8), 706-731.

[17] Dikshit, S., Atiq, A., Shahid, M., Dwivedi, V., & Thusu, A. (2023). The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas. EAI Endorsed Transactions on Energy Web, 10.

[18] Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961-974.

[19] Enebe, G.C., Ukoba, K. and Jen, T.C., 2019. Numerical modeling of effect of annealing on nanostructured CuO/TiO2 pn heterojunction solar cells using SCAPS.

[20] Englund, C., Aksoy, E. E., Alonso-Fernandez, F., Cooney, M. D., Pashami, S., & Åstrand, B. (2021). AI perspectives in Smart Cities and Communities to enable road vehicle automation and smart traffic control. Smart Cities, 4(2), 783-802.

[21] Ewim, D.R.E., Okwu, M.O., Onyiriuka, E.J., Abiodun, A.S., Abolarin, S.M. and Kaood, A., 2021. A quick review of the applications of artificial neural networks (ANN) in the modelling of thermal systems.

[22] Gill, S. S., Xu, M., Ottaviani, C., Patros, P., Bahsoon, R., Shaghaghi, A., ... & Uhlig, S. (2022). AI for next generation computing: Emerging trends and future directions. Internet of Things, 19, 100514.

[23] Green, B. (2019). The smart enough city: putting technology in its place to reclaim our urban future. MIT Press.

[24] Haluza, D., & Jungwirth, D. (2023). Artificial Intelligence and Ten Societal Megatrends: An Exploratory Study Using GPT-3. Systems, 11(3), 120.

[25] Javed, A. R., Shahzad, F., ur Rehman, S., Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022). Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. Cities, 129, 103794.

[26] Kanonhuhwa, T. N., Dumba, S., & Chirisa, I. (2023). Transportation and Mobility: Strides Toward Decarbonizing the Urban System. In The Palgrave Encyclopedia of Urban and Regional Futures (pp. 1913-1922). Cham: Springer International Publishing.

[27] Khonturaev, S. I., Khoitkulov, A. A., & Abdullayeva, M. R. (2023). Revolutionizing Security: The Transformative Role Of Artificial Intelligence. Лучшие интеллектуальные исследования, 7(2), 129-135.

[28] Kolekar, S., Gite, S., Pradhan, B., & Kotecha, K. (2021). Behavior prediction of traffic actors for intelligent vehicle using artificial intelligence techniques: A Review. IEEE Access, 9, 135034-135058.

[29] Kumar, K., & Singh, D. P. (2023). Assessing the Dynamics of Urbanization: A Comprehensive Review of Associated Risks and Mitigation Strategies. Journal of Research in Infrastructure Designing, 6(3), 1-10.

[30] Kumar, S., Gupta, U., Singh, A. K., & Singh, A. K. (2023). Artificial intelligence: revolutionizing cyber security in the digital era. Journal of Computers, Mechanical and Management, 2(3), 31-42.

[31] Lee, H., Chatterjee, I., & Cho, G. (2023). A Systematic Review of Computer Vision and AI in Parking Space Allocation in a Seaport. Applied Sciences, 13(18), 10254.

[32] Li, J., Yu, C., Shen, Z., Su, Z., & Ma, W. (2023). A survey on urban traffic control under mixed traffic environment with connected automated vehicles. Transportation research part C: emerging technologies, 154, 104258.

[33] Mangla, M., Shinde, S. K., Mehta, V., Sharma, N., & Mohanty, S. N. (Eds.). (2022). Handbook of Research on Machine Learning: Foundations and Applications. CRC Press.

[34] Mokoele, N. J. (2021). Towards effective planning and management of urbanisation to mitigate climate change: a case of the city of Polokwane, South Africa (Doctoral dissertation).

[35] Mouchou, R., Laseinde, T., Jen, T.C. and Ukoba, K., 2021. Developments in the Application of Nano Materials for Photovoltaic Solar Cell Design, Based on Industry 4.0 Integration Scheme. In Advances in Artificial Intelligence, Software and Systems Engineering: Proceedings of the AHFE 2021 Virtual Conferences on Human Factors in Software and Systems Engineering, Artificial Intelligence and Social Computing, and Energy, July 25-29, 2021, USA (pp. 510-521). Springer International Publishing.

[36] Okunade, B. A., Adediran, F. E., Maduka, C. P., & Adegoke, A. A. (2023). Community-Based Mental Health Interventions In Africa: A Review And Its Implications For Us Healthcare Practices. International Medical Science Research Journal, 3(3), 68-91.

[37] Rane, N. (2023). Integrating Leading-Edge Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies for Smart and Sustainable Architecture, Engineering and Construction (AEC) Industry: Challenges and Future Directions. Engineering and Construction (AEC) Industry: Challenges and Future Directions (September 24, 2023).

[38] Rane, N. (2023). Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry. Available at SSRN 4598258.

[39] Rangaraju, S. (2023). Secure by Intelligence: Enhancing Products with AI-Driven Security Measures. EPH-International Journal of Science And Engineering, 9(3), 36-41.

[40] Ravish, R., & Swamy, S. R. (2021). Intelligent traffic management: A review of challenges, solutions, and future perspectives. Transport and Telecommunication Journal, 22(2), 163-182.

[41] Ray, P. P. (2023). Web3: A comprehensive review on background, technologies, applications, zero-trust architectures, challenges and future directions. Internet of Things and Cyber-Physical Systems.

[42] Rehman, A. (2022). Realizing Trust Dynamics and Governance for Humanizing Driverless Technology (Doctoral dissertation, Auckland University of Technology).

[43] Sangaré, M. (2022). Exploring Prediction Strategies in Vehicular Networks Through Machine Learning Techniques and Hybrid Intelligence (Doctoral dissertation, Cnam Paris, 292 rue Saint Martin, 75141 Paris).

[44] Sanni, O., Adeleke, O., Ukoba, K., Ren, J. and Jen, T.C., 2024. Prediction of inhibition performance of agro-waste extract in simulated acidizing media via machine learning. Fuel, 356, p.129527.

[45] Sarker, I. H. (2022). Ai-based modeling: Techniques, applications and research issues towards automation, intelligent and smart systems. SN Computer Science, 3(2), 158.

[46] Siddiqi, S. J., Naeem, F., Khan, S., Khan, K. S., & Tariq, M. (2022). Towards AI-enabled traffic management in multipath TCP: A survey. Computer Communications, 181, 412-427.

[47] Stahl, B. C. (2021). Artificial intelligence for a better future: an ecosystem perspective on the ethics of AI and emerging digital technologies (p. 124). Springer Nature.

[48] Stecuła, K., Wolniak, R., & Grebski, W. W. (2023). AI-Driven Urban Energy Solutions—From Individuals to Society: A Review. Energies, 16(24), 7988.

[49] Tamagusko, T., Gomes Correia, M., Rita, L., Bostan, T. C., Peliteiro, M., Martins, R., ... & Ferreira, A. (2023). Data-Driven Approach for Urban Micromobility Enhancement through Safety Mapping and Intelligent Route Planning. Smart Cities, 6(4), 2035-2056.

[50] Tomar, I., Sreedevi, I., & Pandey, N. (2022). State-of-Art review of traffic light synchronization for intelligent vehicles: current status, challenges, and emerging trends. Electronics, 11(3), 465.

[51] Uddin, S.U., Chidolue, O., Azeez, A. and Iqbal, T., 2022, June. Design and Analysis of a Solar Powered Water Filtration System for a Community in Black Tickle-Domino. In 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-6). IEEE.

[52] Ukoba, K. and Jen, T.C., 2023. Thin films, atomic layer deposition, and 3D Printing: demystifying the concepts and their relevance in industry 4.0. CRC Press.

[53] Ukoba, K., Fadare, O. and Jen, T.C., 2019, December. Powering Africa using an off-grid, stand-alone, solar photovoltaic model. In Journal of Physics: Conference Series (Vol. 1378, No. 2, p. 022031). IOP Publishing.

[54] Van Cuong, N., & Aziz, M. T. (2023). AI-Driven Vehicle Recognition for Enhanced Traffic Management: Implications and Strategies. AI, IoT and the Fourth Industrial Revolution Review, 13(7), 27-35.

[55] Van Hieu, D., & Van Khanh, N. (2023). Traffic Management with AI-Powered Vehicle Recognition: Implications and Strategies. International Journal of Sustainable Infrastructure for Cities and Societies, 8(11), 1-11.

[56] Weiwei, C., & Jingjing, Z. (2023). Navigating the Roads of Tomorrow: How Intelligent Vehicles are Changing the Game. Journal of Sustainable Technologies and Infrastructure Planning, 7(1), 1-24.

[57] Yang, L., & Shami, A. (2022). IoT data analytics in dynamic environments: From an automated machine learning perspective. Engineering Applications of Artificial Intelligence, 116, 105366.

[58] Yu, D., & Fang, C. (2023). Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades. Remote Sensing, 15(5), 1307.

[59] Yue, W., Li, C., Mao, G., Cheng, N., & Zhou, D. (2021). Evolution of road traffic congestion control: A survey from perspective of sensing, communication, and computation. China Communications, 18(12), 151-177.

@article{1705886,
      author = {Afees Olanrewaju Akinade, Peter Adeyemo Adepoju, Adebimpe Bolatito Ige, Adeoye Idowu Afolabi},
      title = {Artificial Intelligence in Traffic Management: A Review of Smart Solutions and Urban Impact},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {12},
      pages = {511-522},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705886.pdf},
      abstract = {Artificial Intelligence (AI) has emerged as a transformative force in the realm of traffic management, revolutionizing urban mobility and addressing the challenges posed by increasing population density. This paper presents a comprehensive review of smart solutions powered by AI in the context of traffic management and evaluates their impact on urban environments. The integration of AI into traffic management systems has paved the way for dynamic and adaptive solutions that optimize traffic flow, reduce congestion, and enhance overall transportation efficiency. Machine learning algorithms enable the prediction and analysis of traffic patterns, allowing for real-time adjustments and responsive control mechanisms. Smart traffic signal systems, for instance, utilize AI to adapt signal timings based on live traffic conditions, minimizing delays and improving the overall flow of vehicles. Moreover, AI-driven technologies play a crucial role in enhancing safety on roadways. Advanced driver assistance systems (ADAS) leverage AI algorithms to detect potential hazards, monitor driver behavior, and mitigate risks through features like collision avoidance and automatic emergency braking. This not only contributes to reducing accidents but also enhances the overall safety of urban transportation. The impact of AI in traffic management extends beyond mere efficiency gains. Sustainable urban development is facilitated by intelligent transportation systems that encourage the use of public transport, cycling, and walking. Additionally, the integration of AI supports environmental goals by optimizing traffic patterns, reducing emissions, and promoting eco-friendly modes of transportation. However, challenges such as data privacy, ethical considerations, and the need for robust infrastructure persist. This review highlights the potential of AI in reshaping urban mobility while emphasizing the importance of addressing associated challenges. As cities continue to grow and face escalating transportation demands, the adoption of AI in traffic management emerges as a pivotal strategy for creating smart, efficient, and sustainable urban environments.},
      keywords = {AI; Traffic Management; Urban Impact; Smart Solution; Review},
      month = {June},
  }