Leveraging Cloud-Enabled AI for Intelligent Traffic Prediction and Sustainable Urban Systems

Mallesham Goli

doi:10.15662/IJRAI.2023.0606026

Authors

Mallesham Goli Independent Researcher, India Author

DOI:

https://doi.org/10.15662/IJRAI.2023.0606026

Keywords:

Smart City Traffic Management, Cloud-Enabled Artificial Intelligence, AI-Driven Urban Mobility, Predictive Traffic Management Systems, Real-Time Traffic Control, Intelligent Incident Response, Urban Demand Management, Mobility-as-a-Service (MaaS), Cloud-Based Decision Support Systems, Data-Driven Traffic Optimization, Sustainable Urban Mobility Models, Metropolitan Traffic Analytics, AI Deployment in Cloud Environments, Traffic Prediction Models, Smart Transportation Architectures, Energy-Efficient Urban Mobility, Environmental Impact Reduction, Resilient Transportation Systems, Integrated Traffic Control Frameworks, AI-Enabled Smart Cities

Abstract

Urban mobility is a major concern for large metropolitan areas. Cloud-enabled Artificial Intelligence (AI) technology may help to manage travel demand and offer a more sustainable urban mobility model for smart cities. An AI-driven, cloud-enabled architectural framework applied to traffic management and control business processes is proposed. It integrates four AI-based predictive models with real-time traffic control and incident-response systems. Different data-driven use cases, proved in two metropolitan areas, illustrate the applicability of this framework in real operational environments. Results show that this approach is capable of managing urban traffic in real time. In addition, it demonstrates how AI-based models can be developed, deployed, and operated within cloud environments, offering a decision-support capability.

The central role of predictive traffic management systems in the cloud-enabled AI model for smart cities is assessed. Demand management processes and the integration of Mobility-as-a-Service platforms are also analyzed. These are mandatory steps to offer a sustainable traffic model for large metropolitan areas. Finally, other aspects such as environmental protection and energy consumption in urban mobility are examined. Traffic management by predictive systems enables a more accurate confrontation of real traffic conditions, improving the energy, environmental, and resilience contexts.

References

[1] Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.

[2] Abu-Elkheir, M., Hayajneh, M., & Ali, N. A. (2016). Data management for the Internet of Things: Design primitives and solutions. Sensors, 16(4), 454.

[3] Ahmed, M. S., & Cook, A. R. (1979). Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transportation Research Record, 722, 1–9.

[4] Uday Surendra Yandamuri. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 472–483. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8005.

[5] Al-Turjman, F. (2020). Intelligence-enabled sustainable smart cities: An Internet of Things (IoT) perspective. Elsevier.

[6] Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.

[7] Arel, I., Liu, C., Urbanik, T., & Kohls, A. (2010). Reinforcement learning-based multi-agent system for network traffic signal control. IET Intelligent Transport Systems, 4(2), 128–135.

[8] Arthurs, P., Gillam, L., Krause, P., Wang, N., Halder, K., & Mouzakitis, A. (2022). A taxonomy and survey of edge cloud computing for intelligent transportation systems and connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 23(7), 6206–6221.

[9] Meda, R. (2023). Developing AI-Powered Virtual Color Consultation Tools for Retail and Professional Customers. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3577.

[10] Aitha, A. R. (2021). Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks.

[11] Bellaïche, M., & Gruyer, D. (Eds.). (2013). Intelligent vehicles: Enabling technologies and future developments. Wiley.

[12] Goutham Kumar Sheelam, Hara Krishna Reddy Koppolu. (2022). Data Engineering And Analytics For 5G-Driven Customer Experience In Telecom, Media, And Healthcare. Migration Letters, 19(S2), 1920–1944. Retrieved from https://migrationletters.com/index.php/ml/article/view/11938.

[13] Bergstra, J., & Bengio, Y. (2012). Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13, 281–305.

[14] Kalisetty, S. (2023). Harnessing Big Data and Deep Learning for Real-Time Demand Forecasting in Retail: A Scalable AI-Driven Approach. American Online Journal of Science and Engineering (AOJSE)(ISSN: 3067-1140), 1(1).

[15] Bonnefon, J.-F., Shariff, A., & Rahwan, I. (2016). The social dilemma of autonomous vehicles. Science, 352(6293), 1573–1576.

[16] Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.

[17] Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1–7), 107–117.

[18] Burns, B., Beda, J., & Hightower, K. (2019). Kubernetes: Up & running (2nd ed.). O’Reilly Media.

[19] Kushvanth Chowdary Nagabhyru. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898–5910. https://doi.org/10.53555/kuey.v29i4.10932.

[20] Castro-Neto, M., Jeong, Y.-S., Jeong, M.-K., & Han, L. D. (2009). Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Systems with Applications, 36(3), 6164–6173.

[21] Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.

[22] Chen, X., Zahiri, M., & Zhang, S. (2017). Understanding ridesplitting behavior of on-demand ride services: An ensemble learning approach. Transportation Research Part C: Emerging Technologies, 76, 51–70.

[23] Cheng, W.-H., Kuo, C.-Y., Lin, W.-C., & Hsu, C.-T. (2018). Real-time traffic incident detection based on video analytics: A survey. ACM Computing Surveys, 50(3), 1–35.

[24] Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.

[25] Codd, E. F. (1970). A relational model of data for large shared data banks. Communications of the ACM, 13(6), 377–387.

[26] Avinash Reddy Segireddy. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965.

[27] Daganzo, C. F. (1994). The cell transmission model: A dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research Part B: Methodological, 28(4), 269–287.

[28] Rongali, S. K. (2022). AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN 5763022.

[29] Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.

[30] Guntupalli, R. (2023). AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN 5329158.

[31] Ding, W., & Rossiter, J. A. (2016). Model predictive control: A review and future directions. In 2016 IEEE 55th Conference on Decision and Control (CDC) (pp. 4149–4154). IEEE.

[32] Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227–237. https://doi.org/10.38124/ijsrmt.v1i12.1111.

[33] Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science, 9(3–4), 211–407.

[34] Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.

[35] European Commission. (2019). The ethics guidelines for trustworthy AI. Publications Office of the European Union.

[36] Kummari, D. N. (2023). Energy Consumption Optimization in Smart Factories Using AI-Based Analytics: Evidence from Automotive Plants. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3572.

[37] Farah, H., Bekhor, S., & Polus, A. (2014). Risk evaluation by modeling of passing behavior on two-lane rural highways. Accident Analysis & Prevention, 70, 109–118.

[38] Ramesh Inala. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493–5505. https://doi.org/10.53555/kuey.v29i4.10424.

[39] Gartner. (2019). ITIL 4: Create, deliver and support. TSO.

[40] Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.

[41] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

[42] AI Powered Fraud Detection Systems: Enhancing Risk Assessment in the Insurance Sector. (2023). American Journal of Analytics and Artificial Intelligence (ajaai) With ISSN 3067-283X, 1(1). https://ajaai.com/index.php/ajaai/article/view/14.

[43] Grieves, M., & Vickers, J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In F.-J. Kahlen, S. Flumerfelt, & A. Alves (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–113). Springer.

[44] Segireddy, A. R. (2020). Cloud Migration Strategies for High-Volume Financial Messaging Systems.

[45] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.

[46] Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment. (2023). American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140) , 1(1). https://aojse.com/index.php/aojse/article/view/19.

[47] International Organization for Standardization. (2018). ISO/IEC 27001:2018 Information security management systems—Requirements. ISO.

[48] Avinash Reddy Aitha. (2022). Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 1308–1318. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/8609.

[49] Jiang, X., Zhang, L., & Yang, Q. (2021). Edge computing for intelligent transportation systems: A survey. IEEE Internet of Things Journal, 8(19), 14185–14204.

[50] Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.

[51] Kaack, L. H., & Katzev, R. D. (1991). Behavioral intervention strategies for reducing traffic congestion. Transportation Research Part A: General, 25(1), 63–73.

[52] Amistapuram, K. (2021). Digital Transformation in Insurance: Migrating Enterprise Policy Systems to .NET Core. Universal Journal of Computer Sciences and Communications, 1(1), 1–17. Retrieved from https://www.scipublications.com/journal/index.php/ujcsc/article/view/1348.

[53] Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-T. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of EMNLP 2020 (pp. 6769–6781). Association for Computational Linguistics.

[54] Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of SIGIR 2020 (pp. 39–48). ACM.

[55] Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1–17. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1352.

[56] Kleinberg, J. (2000). Navigation in a small world. Nature, 406(6798), 845.

[57] Koonce, P., & Rodegerdts, L. (2008). Traffic signal timing manual. Transportation Research Board.

[58] Varri, D. B. S. (2023). Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN 5774926.

[59] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

[60] Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective

[61] Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015). Traffic prediction in a bike-sharing system. In Proceedings of SIGSPATIAL 2015 (pp. 1–10). ACM.

[62] Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256.

[63] Lopes, L., Bento, C., & Baptista, P. (2021). Environmental impacts of congestion pricing: A review of methods and evidence. Transport Reviews, 41(5), 623–647.

[64] Guntupalli, R. (2023). Optimizing Cloud Infrastructure Performance Using AI: Intelligent Resource Allocation and Predictive Maintenance. Available at SSRN 5329154.

[65] Lv, Y., Duan, Y., Kang, W., Li, Z., & Wang, F.-Y. (2015). Traffic flow prediction with big data: A deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2), 865–873.

[66] Ma, X., Tao, Z., Wang, Y., Yu, H., & Wang, Y. (2015). Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54, 187–197.

[67] Kummari, D. N. (2023). AI-Powered Demand Forecasting for Automotive Components: A Multi-Supplier Data Fusion Approach. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN 3050-9734 en e-ISSN 3050-9742, 1(1).

[68] NIST. (2020). Security and privacy controls for information systems and organizations (NIST SP 800-53 Rev. 5). U.S. Department of Commerce.

[69] Rongali, S. K. (2021). Cloud-Native API-Led Integration Using MuleSoft and .NET for Scalable Healthcare Interoperability. Available at SSRN 5814563.

[70] Object Management Group. (2016). Business process model and notation (BPMN), version 2.0.2. OMG.

[71] Meda, R. (2023). Data Engineering Architectures for Scalable AI in Paint Manufacturing Operations. European Data Science Journal (EDSJ) p-ISSN 3050-9572 en e-ISSN 3050-9580, 1(1).

[72] Pahl, C., Brogi, A., Soldani, J., & Jamshidi, P. (2019). Cloud container technologies: A state-of-the-art review. IEEE Transactions on Cloud Computing, 7(3), 677–692.

[73] Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650..

[74] Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., & Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In Proceedings of NeurIPS 2019 (pp. 8024–8035).

[75] Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.

[76] Polson, N. G., & Sokolov, V. O. (2017). Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies, 79, 1–17.

[77] Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.

[78] Rahman, M., & Liu, H. X. (2021). A review of connected and automated vehicle impacts on traffic flow and control. Transportation Research Part C: Emerging Technologies, 124, 102939.

[79] Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.

[80] Shaheen, S., & Cohen, A. (2020). Shared mobility policy playbook. Institute of Transportation Studies, UC Berkeley.

[81] Varri, D. B. S. (2022). AI-Driven Risk Assessment And Compliance Automation In Multi-Cloud Environments. Available at SSRN 5774924.

[82] Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.

[83] Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29 (4), 4555–4568.

[84] Ta, N., Liu, H., & Chen, M. (2020). Advanced travel demand management in smart cities: A survey. IEEE Access, 8, 152126–152145.

[85] Rongali, S. K. (2023). Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 22-31.

[86] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Proceedings of NeurIPS 2017 (pp. 5998–6008).

[87] Challa, K. Dynamic Neural Network Architectures for Real-Time Fraud Detection in Digital Payment Systems Using Machine Learning and Generative AI.

[88] Yu, B., Yin, H., & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of IJCAI 2018 (pp. 3634–3640).

[89] Kiran Reddy Burugulla, J. (2023). Transforming Payment Systems Through AI And ML: A Cloud-Native Approach. Educational Administration: Theory and Practice. https://doi.org/10.53555/kuey.v29i4.10144.

[90] Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5(3), 1–55.

[91] Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982