Transportation shapes the daily lives of millions of people and is much more than just infrastructure and vehicles. Representatives from government agencies, logistics companies and the social research community who participated in the workshop “Mobility, Artificial Intelligence (AI) and Society: Strengthening Vietnam’s Transport Future” reflected on the current situation, which is both encouraging and calls for more efforts to ensure that no one is left behind in the development of smart transportation.
The role of AI
Luong Duc Thanh, Deputy Director of the Transport Infrastructure Management Department of the Hanoi Construction Department, said the role of AI in urban traffic management has already been clearly identified in several short-term areas, such as traffic enforcement, operating a green transportation ecosystem, and improving traffic flow. However, the longer-term direction of AI adoption and how to effectively utilize large datasets for governance and operations are still areas that require further research and clarification.
In fact, Hanoi has installed more than 1,800 AI-enabled cameras for traffic and public security purposes by 2025, and plans to add more than 2,100 more this year. This number reflects the impressive pace of adoption. However, according to Tan, what regulators really need is not just more equipment, but a consistent big data strategy from collection to practical use in urban governance. “Every policy must answer one question: How will the public benefit from its implementation?” he said. “This is still a new field and requires stronger involvement of AI experts to form a clearer direction.”
As Hanoi adjusts its master plan and expands urban development, building a unified transportation data platform from data collection to operational deployment is an urgent requirement, requiring coordination among regulators, technology companies and experts, Tan asserted.
Mr. Nguyen Duy Hong, Head of Operations for North and Central Vietnam at YCH Group, shared his first-hand experience applying AI from the perspective of a logistics company facing increasing trade volumes and increasing pressure to optimize transportation costs. In the past, shipping planning relied heavily on experience and assumptions. The truck was booked at 8am and the operator just had to wait. If you do not arrive on time, follow-up will only begin at that time. Now, with the integration of AI and GPS, logistics teams can proactively determine where vehicles are and when they are expected to arrive.
Real-time visibility not only reduces uncertainty but also fundamentally changes how resources are allocated and operational plans are designed. “Thanks to AI, we have been able to reduce planning time by 80-85%, while at the same time reducing costs by approximately 30-40% by automating repetitive tasks,” said Hong.
At the same time, he highlighted the paradox that even though AI has been integrated into corporate management systems, many employees continue to use personal AI tools to handle day-to-day tasks. This raises concerns about the security of customer data, but more fundamentally reflects the absence of a shared data infrastructure that is accessible across the supply chain. “A supply chain is only as strong as its weakest link, and no one wants to be its weakest link,” he added.
So instead of forcing businesses to invest in fragmented cloud infrastructure individually, he called on governments to establish a central data hub where businesses can securely upload and access information. This approach is costly and risks deepening data silos across the industry.
At the same time, the green transition is creating new practical pressures. Many logistics companies have only recently invested in truck fleets that have yet to fully depreciate, but are already being forced to consider electric vehicle (EV) alternatives. However, the electric trucks currently available in Vietnam are still insufficient in terms of payload and operating range required by the logistics sector. To do so, he argued, businesses need appropriate policy support to survive the transition.
The role of AI in urban traffic management is already clearly recognized in several near-term areas, such as traffic enforcement, operating green transportation ecosystems, and improving traffic flow. However, the broader long-term direction of AI adoption and how to effectively leverage large datasets for governance and operations are still areas that require further research and clarification.
available choices
Dr. Nguyen Duc Binh, former director of the Institute of Sociology at the Vietnam Academy of Social Sciences, highlighted issues of accessibility and social equity as transportation systems rapidly evolve. As policies change and technology advances, different population groups will inevitably have different capacities to adapt and have different transportation options available to them.
He cited motorcycles as an example. Vietnam’s widespread dependence on motorbikes reflects a practical reality. Bikes remain the best option for many people, given their affordability, flexibility, and lack of dependence on a fixed schedule.
In his view, travel behavior will naturally evolve only if transportation truly offers more convenient and suitable alternatives. “Some groups adapt to technology very quickly, while other groups are almost excluded from it,” he explained. “Every time policy or technology changes, we have to ask ourselves: What transportation options do these groups realistically have?”
He also stressed that AI cannot replace human decision-making in transportation participation. Transitioning to EVs, expanding public transport, and deploying AI all have significant value, but they also need to be evaluated against social indicators such as affordability, accessibility, and impact on quality of life across different population groups.
Vietnam’s transportation sector is experiencing an intelligent digital transition amidst rapid AI development and faces multiple challenges. Increasingly, integrated and interoperable transportation data platforms are seen as essential for AI applications to operate effectively at a system level, rather than being confined to individual agencies or companies.
At the same time, policymakers need to consider differences in public accessibility, including among groups with less capacity to adapt to changes in technology and policy, to ensure that the benefits of smart transport are shared broadly across society.
