Sustainable Urban Mobility: The Role of Emerging Technologies in Smart Transportation
Abstract
Urban mobility is a major contributor to carbon emissions and environmental degradation. This paper explores how emerging technologies—such as electric vehicles (EVs), intelligent transportation systems (ITS), and Mobility-as-a-Service (MaaS)—can drive sustainable urban transport. Through urban case studies and data analysis, we evaluate energy consumption, emission reductions, and commuter behavior. The paper also investigates policy implications and infrastructure requirements, presenting a roadmap for cities to transition toward low-emission, tech-enabled mobility ecosystems.
References
Chittineni, S. (2019). Optimizing Microservices Performance with Reinforcement Learning: A Case Study in Spring Boot Applications. American Journal of AI & Innovation, 1(1).
Chittineni, S. (2019). AI-Driven Optimization of Backend Systems: Enhancing Performance and Reducing Latency in Large-Scale Applications. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Mohammed, C. S. A. (2019). Exploring the Features and Scope of SAP S/4HANA for Financial Products Subledger Management. Australian Journal of Cross-Disciplinary Innovation, 1(1).
Chittineni, S. (2020). Leveraging Machine Learning for Automated Thread Dump Analysis and Performance Tuning in Enterprise Java Applications. Australian Journal of Cross-Disciplinary Innovation, 2(2).
Mohammed, C. (2021). Revolutionizing Financial Operations: A Comprehensive Study on the Impact of SAP and Kyriba Integration. International Journal of Sustainable Development in Computing Science, 3(2), 1-19. Retrieved from https://ijsdcs.com/index.php/ijsdcs/article/view/696/260
The Critical Role of Accurate Balance Carry Forward in Preventing Financial Irregularities. (2022). International Journal of Interdisciplinary Finance Insights, 1(1), 1-13. https://injmr.com/index.php/ijifi/article/view/143
Chittineni, S. (2022). Automated API Performance Testing and Anomaly Detection Using Machine Learning in RESTful Architectures. American Journal of AI & Innovation, 4(4).
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable Algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Narayanan, A., & Shmatikov, V. (2010). Myths and fallacies of "personally identifiable information". Communications of the ACM, 53(6), 24-26.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
