American Journal of Advanced Multidisciplinary Research and Innovation

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Sustainable Smart Transportation Systems Through Artificial Intelligence and Intelligent Mobility Solutions

Author(s) Lucio Soibelman
Country United States
Abstract The rapid growth of urban populations, increasing vehicle ownership, traffic congestion, environmental pollution, and climate change have created significant challenges for modern transportation systems. Traditional transportation infrastructures often suffer from inefficient traffic management, high fuel consumption, greenhouse gas emissions, road safety issues, and limited integration between transportation modes. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), Intelligent Transportation Systems (ITS), Connected and Autonomous Vehicles (CAVs), Big Data Analytics, Cloud Computing, Edge Computing, Digital Twin Technology, Geographic Information Systems (GIS), Blockchain, 5G Communication, and Electric Mobility have transformed transportation by enabling intelligent traffic management, predictive mobility planning, autonomous transportation, and sustainable urban mobility.
This study presents a comprehensive analysis of Sustainable Smart Transportation Systems Through Artificial Intelligence and Intelligent Mobility Solutions. A qualitative analytical research methodology based on secondary data is employed to investigate AI-driven traffic management, intelligent mobility platforms, autonomous transportation, multimodal transport integration, predictive transportation analytics, and sustainable mobility planning. The research evaluates how intelligent technologies improve transportation efficiency, environmental sustainability, road safety, operational resilience, and user experience.
The findings indicate that AI-enabled transportation systems significantly improve traffic flow optimisation, congestion prediction, route planning, accident prevention, fleet management, energy efficiency, and public transportation performance. Machine Learning enhances demand forecasting and predictive traffic analytics, while IoT-enabled sensors provide real-time monitoring of vehicles, infrastructure, and environmental conditions. Digital Twin Technology enables virtual simulation of transportation networks for infrastructure planning and operational optimisation. Furthermore, blockchain improves secure mobility transactions, and cloud-based mobility platforms facilitate integrated transport services.
Despite these opportunities, challenges remain concerning cybersecurity, data privacy, infrastructure investment, regulatory frameworks, interoperability, ethical AI implementation, digital inequality, and workforce readiness. Future research should investigate explainable AI for transportation, federated mobility intelligence, quantum-assisted traffic optimisation, autonomous multimodal mobility ecosystems, and carbon-neutral transportation networks.
The study concludes that Artificial Intelligence and intelligent mobility technologies provide a transformative framework for sustainable transportation systems by improving operational efficiency, environmental performance, public safety, and resilient urban mobility while supporting global sustainable development goals.
Keywords Artificial Intelligence, Smart Transportation, Intelligent Mobility, Intelligent Transportation Systems, Machine Learning, Sustainable Mobility, Internet of Things, Connected Vehicles, Autonomous Vehicles, Traffic Management.
Field Engineering
Published In Volume 2, Issue 3, May-June 2020
Published On 2020-05-31
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