American Journal of Advanced Multidisciplinary Research and Innovation

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Sustainable Smart Transportation Systems: Emerging Technologies and Challenges

Author(s) Charles L. Isbell Jr.
Country United States
Abstract Rapid urbanisation, population growth, traffic congestion, environmental pollution, and increasing mobility demands have accelerated the development of Sustainable Smart Transportation Systems that integrate Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), Connected and Autonomous Vehicles (CAVs), Electric Vehicles (EVs), Intelligent Transportation Systems (ITS), 5G/6G Communication Networks, Cloud Computing, Edge Computing, Big Data Analytics, Blockchain Technology, Digital Twin Technology, Geographic Information Systems (GIS), Global Positioning System (GPS), Computer Vision, and Vehicle-to-Everything (V2X) communication. These technologies support intelligent traffic management, sustainable mobility, energy-efficient transportation, real-time decision-making, and improved public safety while reducing greenhouse gas emissions and enhancing urban resilience.
This study presents a comprehensive analysis of Sustainable Smart Transportation Systems: Emerging Technologies and Challenges. A qualitative analytical research methodology based on secondary data is adopted to investigate intelligent transportation architectures, enabling technologies, sustainable mobility solutions, implementation challenges, and future research directions. The study examines how AI-driven analytics, IoT-enabled sensing, autonomous mobility, digital twins, and cloud-based transportation platforms improve traffic efficiency, road safety, environmental sustainability, and public transport performance across urban and regional transportation networks.
The findings indicate that intelligent transportation systems significantly improve traffic flow optimisation, accident prevention, route planning, predictive maintenance, multimodal transport integration, and energy management. Artificial Intelligence enables adaptive traffic signal control, congestion prediction, and autonomous driving support. IoT sensors provide continuous monitoring of vehicles, roads, and environmental conditions, while V2X communication enhances cooperative mobility and road safety. Digital twins support simulation and optimisation of transportation infrastructure, and electric mobility contributes to carbon emission reduction and sustainable urban development.
Despite these opportunities, transportation authorities continue to face challenges related to cybersecurity, infrastructure investment, interoperability, data privacy, regulatory uncertainty, public acceptance of autonomous vehicles, charging infrastructure limitations, and climate resilience. Future research should investigate AI-native transportation systems, autonomous public transit, quantum optimisation for traffic management, blockchain-enabled mobility services, digital twins for smart cities, and sustainable multimodal transportation ecosystems.
The study concludes that sustainable smart transportation systems provide a transformative framework for future mobility by integrating intelligent technologies, environmental sustainability, resilient infrastructure, and citizen-centric transportation services to support smart cities and sustainable socioeconomic development.
Keywords Smart Transportation, Intelligent Transportation Systems, Sustainable Mobility, Artificial Intelligence, Internet of Things, Electric Vehicles, Autonomous Vehicles, Digital Twin, Smart Cities, Traffic Management.
Field Engineering
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-12-10
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