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In designing a structured AI learning program, some examples of key areas of focus include assessing anticipated upskilling of the workforce and preparing a skills development plan, defining objectives and KPIs for AI in transportation that are linked to organizational goals, differentiating learning by role and seniority and embedding mechanisms for feedback that allow for continuous improvement.
What role can AI play in mitigating the environmental impact of transportation systems? The possibilities are broad – from dedicated large data models discovering novel materials for batteries or sustainable fuel to route optimization for fleet vehicles, in-depot applications around maximizing efficiency or operation improvements at logistics hubs, through to system optimization across wider multimodal transport using new shared data systems for training data.
As AI becomes more embedded in mobility systems, how can organizations ensure that ethical principles are built in from the start in terms of bias, access, and fairness? By the time an AI system is viable there may already be assumptions about what datasets will be used( or have already been used!)- so dataset bias review or bias audits for discriminatory outcomes are critical to establishing a foundation for the future.
Explainable or interpretable models may be necessary to understand and adapt decision processes to make equitable decisions. Universal design principles, and affordability modelling can all play a part.
Structured assurance frameworks can help identify ethical issues alongside making sure AI is effective, transparent, and safe.
Many AI projects remain in the pilot phase. What practical steps can transportation agencies take to move from experimentation to full-scale, real-world deployment? In general terms, thinking about the intended aim of a pilot from the outset – if it’ s successful what needs to be true for this to deploy at scale and be effective. That’ s a combination of broad topics; regulatory, scalability, cost effectiveness, underlying infrastructure, IT and OT, sensing, data and communications. And scaling isn’ t just about volume – it’ s also the buy-in from a wider set of stakeholders to allow for integration and compatibility between systems, models and decision-making processes.
Standards( or selecting from emerging standards) are key, enabling integration and allowing for improved competition between suppliers. They need to be tied into cohesive legislation that addresses the transport industry as a whole, and are potentially a suite of standards covering DataOps, SecOps, MLOps, quality and safety – all while integrating with existing workflows.
Plans need to be in place to train people effectively, for the business case at scale – bearing in mind this may be ROI in terms of spend, but also safety or displacing tasks that are hard to resource.
Finally, resilience is key for system scales, balancing cost and redundancy – requiring upfront implementation before being tuned in the optimization stage.
These themes are the core of our‘ AI deployment guide’ for transportation organizations – in this we define a pathway to AI maturity including a ten-point action plan for success.
Looking ahead, how do you see AI reshaping the transportation landscape and what should decision-makers be doing now to prepare for that future? Delivering safer, more efficient transportation hinges on our ability to embrace and responsibly deploy transformative technologies
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