SUSTAINABLE TRANSPORTATION SOLUTIONS: THE ROLE OF AI AND CLOUD TECHNOLOGIES
Keywords:
Real-time Data Streaming, Stream Processing Architecture, Event-driven Processing, Data Pipeline Optimization, Edge Computing IntegrationAbstract
This article examines the transformative role of Artificial Intelligence and cloud computing technologies in developing sustainable transportation solutions for modern urban environments. Through comprehensive analysis of implementation cases across major metropolitan areas, the article investigates the integration of smart systems in traffic management, predictive maintenance, and real-time optimization of transportation networks. The article employs a mixed-methods approach, combining quantitative analysis of transportation data with qualitative assessment of implementation frameworks, revealing significant improvements in operational efficiency and environmental sustainability. Key findings demonstrate a 30% reduction in average commute times, a 40% decrease in peak-hour congestion, and a 25% reduction in transportation-related CO2 emissions across studied cities. The article also addresses critical challenges in scalability, data privacy, and infrastructure readiness while providing insights into cost-benefit considerations and implementation strategies. Furthermore, it explores emerging trends and future implications for transportation policy, highlighting the importance of standardized protocols and updated regulatory frameworks. This article contributes to the growing body of knowledge on sustainable urban mobility, offering practical insights for city planners, policymakers, and technology implementers in developing efficient, environmentally conscious transportation systems for future cities.
References
Carbone, Paris, et al. "Apache Flink: Stream and Batch Processing in a Single Engine." Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, vol. 36, no. 4, 2015, pp. 28-38, https://asterios.katsifodimos.com/assets/publications/flink-deb.pdf
Gwen, Shapira, et al. "Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale." O'Reilly Media, 2nd Edition, 2021, www.oreilly.com/library/view/kafka-the-definitive/9781492043072/
Zaharia, Matei, and Bill Chambers. "Spark: The Definitive Guide: Big Data Processing Made Simple." O'Reilly Media, 2018, www.oreilly.com/library/view/spark-the-definitive/9781491912201/
Muhammad Eid Balbaa, Olim Astanakulov, Nilufar Ismailova, and Nilufar Batirova. 2024. Real-time Analytics in Financial Market Forecasting: A Big Data Approach. In Proceedings of the 7th International Conference on Future Networks and Distributed Systems (ICFNDS '23). Association for Computing Machinery, New York, NY, USA, 230–233. https://doi.org/10.1145/3644713.3644743
Dupljak, Elzana & Halili, Festim. (2024). Leveraging Big Data Analytics for Enhanced Healthcare. https://www.researchgate.net/publication/381231980_Leveraging_Big_Data_Analytics_for_Enhanced_Healthcare
Fragkoulis, Marios, et al. "A survey on the evolution of stream processing systems." The VLDB Journal 33.2 (2024): 507-541. https://link.springer.com/article/10.1007/s00778-023-00819-8
Z. Milosevic, W. Chen, A. Berry, F.A. Rabhi,Chapter 2 - Real-Time Analytics, Editor(s): Rajkumar Buyya, Rodrigo N. Calheiros, Amir Vahid Dastjerdi,Big Data, Morgan Kaufmann, 2016, Pages 39-61,
ISBN 9780128053942, https://doi.org/10.1016/B978-0-12-805394-2.00002-7
Kai Waehner. " The Past, Present and Future of Stream Processing” https://www.kai-waehner.de/blog/2024/03/20/the-past-present-and-future-of-stream-processing/
