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

这一活动是为了响应美国国家科学基金会与美国国务院合作，通过美国-东盟（东南亚国家城市联盟）智慧城市伙伴关系，支持研究向城市过渡的致同事信。胡志明市（HCMC）是越南的一个东盟城市，以交通拥堵和高密度的车辆、汽车、公共汽车、卡车和大量的摩托车（840多万居民骑着730万辆摩托车）而闻名。大型开发项目恶化了城市状况，使交通拥堵更加严重。此外，交通拥堵是城市噪音和粉尘污染的主要原因之一。总之，交通拥堵对城市生活质量构成了重大障碍，但解决办法很复杂。胡志明市的交通有两个主要问题。首先，胡志明市和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交通和基础设施问题。其次，鉴于交通监控是由数量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监控交通活动，因此人员能够提供的实时交通问题响应的数量和有效性受到限制。这个项目的目标是在胡志明市规划模拟器中使用视觉人群人工智能感知。该项目将利用城市摄像头系统（人群人工智能传感）进行实时交通分析。它旨在检测交通违规、交通堵塞和事故等“异常事件”，减少监控人员的干预，从而使工作人员能够更好地应对交通问题。将根据分析的交通数据开发城市规划模拟器。该模拟器将用于支持都市交通规划。项目成果不仅将解决HCMC中具体的城市挑战和解决这些挑战所需的创新技术解决方案，还将提供在其他情况下使用的模型，包括交通、拥堵和城市基础设施挑战可以从人工智能中受益的美国城市。该项目将由HCMC的专业人员进行验证，他们可以评估其在减少人工观察的情况下检测异常事件的有效性，他们能够更好地响应由于实施该项目而产生的交通问题，他们可以利用该项目数据进行交通分析。该奖项反映了美国国家科学基金会的法定使命，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.