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

这项活动是对NSF亲爱的同事来信的回应，信中支持通过美国东盟(东南亚国家城市联盟)智能城市伙伴关系与NSF和美国国务院合作将研究转化为城市。胡志明市是越南的一个东盟城市，以其交通拥堵和车辆、小汽车、公交车、卡车的高密度而闻名，摩托车成群结队(超过840万居民拥有730万辆摩托车)淹没了城市街道。大规模的开发项目加剧了城市状况，使交通拥堵更加严重。此外，交通拥堵是造成该市噪音和粉尘污染的主要因素之一。总而言之，交通拥堵对城市生活质量构成了主要障碍，但解决方案很复杂。胡志明市的交通有两个主要问题。首先，像其他人口密集的城市地区一样，HCMC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。其次，由于交通监控是由有限数量的工作人员执行的，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是有限的。本项目的目标是将可视化人群人工智能传感用于HCMC规划模拟器。该项目将利用城市摄像头系统(人群AI传感)进行实时交通分析。它寻求检测交通违规、交通拥堵和事故等“异常事件”，减少监控人员的干预，从而使工作人员能够在交通问题出现时更好地做出反应。在分析交通数据的基础上，开发城市规划模拟器。该模拟器将用于支持大都市交通规划。项目成果不仅将解决具体的城市挑战和在HCMC中解决这些挑战所需的创新技术解决方案，还将提供在其他环境中使用的模型，包括交通、拥堵和城市基础设施挑战可以受益于人工智能的美国城市。项目将由HCMC的专业人员验证，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，他们能够更好地因实施项目的各个方面而应对交通问题。谁可以利用项目数据进行交通分析。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，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.