EAGER: Crowd-AI Sensing Based Traffic Analysis for Ho Chi Minh City Planning Simulation

这项活动是为了响应NSF亲爱的同事信，支持通过美国东盟（东南亚国家城市联盟）智慧城市伙伴关系与NSF和美国国务院合作，将研究转移到城市。胡志明市（HCMC）是越南的一个东盟城市，以其交通拥堵和高密度的车辆而闻名，汽车，公共汽车，卡车和大量的摩托车（超过840万居民的730万辆摩托车）淹没了城市街道。大规模的发展项目使城市状况恶化，使交通堵塞更加严重。此外，交通拥堵是城市噪音和灰尘污染的主要原因之一。总而言之，交通拥堵是城市生活质量的主要障碍，但解决方案很复杂。胡志明市的交通有两个主要问题。首先，胡志明市与其他人口密集的城市地区一样，需要大量的财政和技术资源来解决其交通和基础设施问题。第二，由于交通监控是由有限的工作人员进行的，他们通过多个屏幕上的数千个摄像头观看交通活动，因此人员能够提供的实时交通问题响应的数量和有效性有限。本项目的目标是将视觉人群AI感知用于HCMC规划模拟器。该项目将利用城市摄像头系统（人工智能感知）进行实时交通分析。它旨在检测“异常事件”，如交通违规，交通堵塞和事故，减少监控人员的干预，从而使工作人员能够更好地应对出现的交通问题。根据分析的交通数据，将开发一个城市规划模拟器。该模拟器将用于支持大都市交通规划。项目研究结果不仅将解决胡志明市特定的城市挑战和解决这些挑战所需的创新技术解决方案，还将提供其他环境中使用的模型，包括美国城市的交通，拥堵和城市基础设施挑战可以从AI中受益。该项目将由胡志明市的专业人员进行验证，他们可以评估其在减少人类观察的情况下检测异常事件的有效性，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.The goal of this project is to use visual crowd AI sensing for the HCMC planning simulator. The project will make use of the city camera system (crowd-AI sensing) for traffic analysis in real-time. It seeks to detect “anomaly events” such as traffic violations, traffic jams, and accidents, with reduced intervention from monitoring staff, allowing staff, in turn, to better respond to traffic problems as they arise. A city planning simulator will be developed upon the analyzed traffic data. The simulator will be used to support metropolitan transportation planning. Project findings will not only address specific urban challenges and the innovative technical solutions needed to solve them in HCMC, but also will provide models use in other contexts, including U.S. cities where traffic, congestion, and urban infrastructure challenges can benefit from AI.The project will be validated by professionals in HCMC who can evaluate its effectiveness for detecting anomaly events with reduced human observation, who are better able to respond to traffic problems as a result of implementing aspects of the project, and who can make use of the project data for traffic analysis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.