EAGER: Crowd-AI Sensing Based Traffic Analysis for Ho Chi Minh City Planning Simulation
Lead PI:
Tam Nguyen
Co-Pi:
Abstract

This activity is in response to NSF Dear Colleague Letter Supporting Transition of Research into Cities through the US ASEAN (Association of Southeast Asian Nations Cities) Smart Cities Partnership in collaboration with NSF and the US State Department. Ho Chi Minh City (HCMC), an ASEAN city in Vietnam, is well-known for its traffic congestion and high density of vehicles, cars, buses, trucks, and a swarm of motorbikes (7.3 million motorbikes for more than 8.4 million residents) that overwhelm city streets. Large-scale development projects have exacerbated urban conditions, making traffic congestion more severe. Additionally, traffic congestion is one of the leading contributors to noise and dust pollution in the city. Altogether, traffic congestion poses major barriers to urban quality of life, but the solutions are complex. There are two main problems with traffic in HCMC. First, HCMC, like other dense urban areas, needs significant financial and technical resources to solve its traffic and infrastructure problems. Second, given that traffic monitoring is carried out by a limited number of staff who watch traffic activities from thousands of camera feeds on multiple screens, there are limits to the number and effectiveness of responses that personnel are able to offer in response to real-time traffic problems.

Tam Nguyen
Performance Period: 08/01/2020 - 07/31/2024
Institution: University of Dayton
Sponsor: NSF
Award Number: 2025234