The implementation of a sustainable transport framework based on Artificial Intelligence (AI) in multiple countries involves a number of obstacles. Inadequate or obsolete transportation infrastructure necessitates the use of dependable communication networks, sensors, and data gathering stations for real-time data transmission. Data availability and quality are critical for AI models, while local context and customisation are required to meet unique difficulties in each region.
Affordability and accessibility are also critical, as AI systems can be expensive, necessitating collaboration among governments, industry, and researchers to balance cost-effectiveness and performance. Regulatory frameworks and rules are critical for AI-powered transportation because they handle safety, privacy, liability, and ethical issues. AI creation, deployment, and maintenance require capacity growth and skill development, as well as professional training in AI, data science, and related topics.
Sustainable transport strives to minimise energy use and emissions, but achieving economic growth while protecting the environment remains difficult. Public acceptability and trust are critical for using AI technology in transportation while demonstrating safety, dependability, and transparency. Interdisciplinary collaboration across disciplines is required for successful AI implementation, while long-term viability and scalability are critical for sustainable transportation solutions. In conclusion, attaining sustainable transport using AI in the countries necessitates a multifaceted approach that includes technical innovation, legislative assistance, and community participation. By tackling these difficulties, we may develop future transportation systems that are efficient, ecologically sustainable, and equitable.
#Author – RB
