CPS: Small: Intelligent Prediction of Traffic Conditions via Integrated Data-Driven Crowdsourcing and Learning

该项目旨在通过对来自日益普及的仪表化和互联车辆、基础设施和人员的丰富数据流进行预测分析，从根本上改变交通管理、应急响应和城市规划实践。道路安全和拥堵是社区面临的巨大挑战。目前的事故管理措施主要是对道路使用者报告作出反应。通过该项目的成果，城市可以主动部署资产和管理交通。这将缩短应急响应时间，挽救生命，并最大限度地减少对交通的干扰。计划在幼儿园到12年级的推广、本科教育、向女性和少数民族学生的推广以及将研究纳入课程中做出努力，目标是激励和培养下一代科学家的多元化群体，并为他们应对智能和互联社区带来的挑战做好准备。为了实现所设想的系统，采用集成研究方法解决以下密切相关的研究任务：(1)采用新的稀疏多任务多视图特征融合方法集成异构数据流；(2)设计新颖的高阶低秩模型进行交通事故预测；(3)联网车辆与路边传感器系统的组队；(4)实时众包用户报告的地面真相，验证交通状况预测；(5)选择众包参与者，招募和选择仪表联网车辆的自愿操作员提供车载传感读数；(6)从众包车辆中选取高质量、多样化的图像和视频，为交通预测提供更好的数据；7)设计最优改道策略，以改善通勤者在潜在交通中断时的路线。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 project aims to radically transform traffic management, emergency response, and urban planning practices via predictive analytics on rich data streams from increasingly prevalent instrumented and connected vehicles, infrastructure, and people. Road safety and congestion are a formidable challenge for communities. Current incident management practices are largely reactive in response to road user reports. With the outcome of this project, cities could proactively deploy assets and manage traffic. This would reduce emergency response times, saving lives, and minimizing disruptions to traffic. Efforts are planned in Kindergarten-12 outreach, undergraduate education, outreach to women and minority students, and incorporation of the research into courses, with the goal to inspire and train a diverse cohort for the next-generation of scientists and prepare them for taking on challenges arising from smart and connected communities. To realize the envisioned system, an integrated research approach is taken to tackle the following closely related research tasks: (1) integration of heterogeneous data streams using a new sparse multi-task multi-view feature fusing method; (2) prediction of traffic incidents by designing a novel high-order low-rank model; (3) teaming of connected vehicles and roadside sensor systems; (4) verification of traffic condition prediction by crowdsourcing the ground truth from user reports in real-time; (5) selection of crowdsourcing participants that recruits and selects voluntary operators of instrumented connected vehicles to provide onboard sensing readings; (6) selection of high quality and diverse images and videos from crowdsourcing vehicles to provide better data for traffic prediction; and 7) design of optimal rerouting strategies to improve commuters' routes in the time of potential traffic disruption.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.