Transportation is not simply about infrastructure or vehicles, as it shapes the daily lives of millions of people. Representatives from government agencies, logistics businesses, and the social research community attending the “Mobility, Artificial Intelligence (AI) and Society: Strengthening the Future of Transportation in Vietnam” workshop reflected on a landscape that is both encouraging and demanding of greater effort to ensure no one is left behind in the development of smart transportation.
AI’s role
Mr. Luong Duc Thang, Deputy Head of the Transport Infrastructure Management Division at the Hanoi Department of Construction, said AI’s role in urban transportation management has already been clearly identified in several near-term areas, including traffic enforcement, the operation of green transportation ecosystems, and traffic flow improvement. However, broader long-term direction for AI deployment, as well as how to effectively harness large datasets for governance and operations, remains an area requiring further study and clarification.
In practice, Hanoi had installed more than 1,800 AI-enabled cameras for transportation and public security purposes by 2025 and plans to add more than 2,100 additional cameras this year. The figures reflect an impressive pace of implementation. Yet according to Mr. Thang, what regulators truly need is not simply more equipment, but a coherent big data strategy, from collection to practical use in urban governance. “Every policy must answer one question: what benefits will citizens receive from its implementation?,” he said. “This remains a new area, and we need stronger engagement from AI experts to help shape clearer direction.”
As Hanoi adjusts its master plan and expands urban development, Mr. Thang argued that building a unified transportation data platform, from data collection to operational deployment, has become an urgent requirement, demanding coordination between regulators, technology companies, and experts.
Mr. Nguyen Duy Hong, Operations Head, North & Central Vietnam, at the YCH Group, shared his first-hand experience applying AI from the perspective of a logistics company facing growing trade volumes and mounting pressure to optimize transportation costs. In the past, shipment planning relied heavily on experience and assumptions: a truck would be booked for 8am, and operators simply waited. If it failed to arrive on time, only then would any follow-up begin. Today, through AI and GPS integration, logistics teams can determine well in advance where vehicles are and when they are expected to arrive.
That real-time visibility not only reduces uncertainty but fundamentally changes how resources are allocated and operational plans are designed. “AI has helped us reduce planning time by 80-85 per cent, while cutting costs by around 30-40 per cent through the automation of repetitive tasks,” Mr. Hong said.
At the same time, he highlighted a paradox: while AI is integrated into corporate management systems, many employees continue using personal AI tools to handle day-to-day work. This raises concerns over customer data security, but more fundamentally reflects the absence of shared data infrastructure accessible across supply chains. “A supply chain is only as strong as its weakest link, and no one wants to be that weakest link,” he added.
He therefore called on the government to establish a centralized data hub where businesses could securely upload and access information, rather than forcing each company to independently invest in fragmented cloud infrastructure; an approach that is both costly and risks deepening data silos across the industry.
At the same time, the green transition is creating new practical pressures. Many logistics firms have only recently invested in truck fleets that have yet to fully depreciate, but are already being pushed to consider electric vehicle (EV) alternatives. However, electric trucks currently available in Vietnam still fall short in terms of payload capacity and operational range required by the logistics sector. This, he argued, requires appropriate policy support to help businesses navigate the transition.
AI’s role in urban transportation management has already been clearly identified in several near-term areas, including traffic enforcement, the operation of green transportation ecosystems, and traffic flow improvement. However, broader long-term direction for AI deployment, as well as how to effectively harness large datasets for governance and operations, remains an area requiring further study and clarification.
Choices available
Dr. Nguyen Duc Vinh, former Director of the Institute of Sociology at the Vietnam Academy of Social Sciences, emphasized issues of accessibility and social equity as transportation systems evolve rapidly. As policies shift and technologies advance, different population groups will inevitably have different capacities to adapt and varying transportation choices available to them.
He pointed to motorcycles as an example. The widespread reliance on motorbikes in Vietnam reflects a practical reality: for many people they remain the best option given current conditions - affordable, flexible, and independent of fixed schedules.
In his view, travel behavior will naturally evolve only when transportation systems provide genuinely more convenient and suitable alternatives. “Some groups adapt to technology very quickly, while others are almost excluded from it,” he explained. “Whenever policies or technologies change, we need to ask: what transportation options will these groups realistically have?”
He also stressed that AI cannot replace human decision-making in transportation participation. Transitioning to EVs, expanding public transit, and deploying AI all hold significant value, but should also be evaluated against social indicators, including affordability, accessibility, and impacts on quality of life across different population groups.
As Vietnam’s transportation sector undergoes an intelligent digital transition amid rapid AI development, it faces multiple challenges. A unified and interoperable transportation data platform is increasingly viewed as essential for AI applications to operate effectively at the system level, rather than remaining confined to individual agencies or businesses.
At the same time, policymakers must account for differences in public accessibility, including among groups less equipped to adapt to technological or policy shifts, to ensure the benefits of smart transportation are shared broadly across society.
