Volume 3 Issue 3 | 2026 | View PDF
Paper Id: IJMSM-V3I3P111
doi: 10.71141/30485037/V3I3P111

Reducing Traffic Congestion in Hanoi, Vietnam Using Artificial Intelligence and Internet of Things

Nguyen Xuan Hung, Lam An Binh, Le Huy Hoang, Dao Duy Bach, Nguyen Viet Anh, Bui Thai An, Vu Xuan Manh

Citation:
Nguyen Xuan Hung, Lam An Binh, Le Huy Hoang, Dao Duy Bach, Nguyen Viet Anh, Bui Thai An, Vu Xuan Manh, "Reducing Traffic Congestion in Hanoi, Vietnam Using Artificial Intelligence and Internet of Things" International Journal of Multidisciplinary on Science and Management, Vol. 3, No. 3, pp. 120-129, 2026.

Abstract:
Hanoi is one of the largest cities in Vietnam. Traffic congestion is one of the most serious urban transportation problems in Hanoi, Vietnam. Rapid urbanization, increasing private vehicle ownership, limited road capacity, frequent traffic violations, and highly variable traffic demand contribute to long travel times, fuel consumption, air pollution, and reduced quality of life. Traditional traffic management systems based on fixed-time traffic signals and manual monitoring are often unable to respond quickly to changing traffic conditions. Artificial Intelligence (AI) and the Internet of Things (IoT) provide an opportunity to develop a more adaptive and intelligent transportation system. This paper proposes an AI- and IoT-based traffic management solution for Hanoi. The proposed system combines IoT sensors, AI-enabled cameras, GPS/mobile data, edge computing, cloud platforms, adaptive traffic signals, and an intelligent traffic control center. Traffic information is collected continuously and analyzed using computer vision, machine learning, and reinforcement learning. Based on predicted traffic conditions, traffic signals can automatically adjust green, yellow, and red-light durations and coordinate neighboring intersections to create green-wave corridors. The system can also detect incidents, illegal parking, traffic violations, and abnormal congestion and provide real-time information to road users. The proposed approach is supported by recent research on AI, IoT, adaptive traffic signal control, and intelligent transportation systems. Importantly, Hanoi has already begun deploying AI cameras and intelligent traffic control infrastructure. Recent operational results reported by Hanoi indicate substantial improvements in travel time and intersection throughput. The paper therefore combines AI and IoT technologies to significantly reduce the traffic congestion in Hanoi, Vietnam.

Keywords: Internet of Things, AI, Traffic Congestion Management.

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