Ai-Driven Smart Mobility Frameworks for Sustainable Urban Business Ecosystems

Abstract

The emergence of urbanisation has created a necessity for the development and enhancement of intelligent mobility systems that may serve the purpose of sustainable economic growth as well as efficient urban transport systems. The current research is looking at how Artificial Intelligence and Machine Learning can help businesses and cities. It wants to see if Artificial Intelligence and Machine Learning can make business economic systems better and stronger.  In addition to the research objectives mentioned above, this research aims to find out whether artificial intelligence and machine learning can help to enhance and sustain urban mobility in the future. It should be noted that regions and cities are considered a focus of this research to examine the role of AI and machine learning in this respect. The objective of this research is to discover how the acceptance level of an AI-based transportation system can be increased. The way people accepted and supported something was looked at by using things that describe how useful it seems, how easy it is to use the good it does to the environment, and the cost people think it has. The risk people think it has. A survey with questions was given to 250 people, including people who travel to work and people who have jobs. Numerous statistical techniques were applied, including discriminant validity analysis, regression analysis, and reliability analysis. The findings indicate that stakeholders' adoption intention is positively impacted by operational convenience and environmental sustainability, and that perceived usefulness is the best predictor of adoption intention. On the other hand, adoption behaviour is adversely affected by technology hazards and economic worries. By providing useful implications for policymakers, transportation planners, and urban business stakeholders, the study adds to the expanding conversation on smart mobility.