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

CPS：小型：通过集成数据驱动的众包和学习智能预测交通状况

基本信息

批准号：
1932482
负责人：
Qi Han
金额：
$ 49.82万
依托单位：
Colorado School of Mines
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-01 至 2024-11-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932482&HistoricalAwards=false
关键词：
CPS Small Intelligent Prediction Traffic

项目摘要

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.

该项目旨在通过对来自日益普及的仪表和联网车辆、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。道路安全和交通拥堵是社区面临的严峻挑战。目前的事故管理做法在很大程度上是对道路使用者报告的反应。有了这个项目的成果，城市可以主动部署资产和管理交通。这将减少紧急响应时间，挽救生命，并最大限度地减少交通中断。计划在幼儿园-12推广，本科教育，推广到妇女和少数民族学生，并将研究纳入课程，目标是激励和培训下一代科学家的多元化群体，并为他们做好准备应对智能和互联社区带来的挑战。为了实现该系统，本文采用了一种集成的研究方法来解决以下密切相关的研究任务：（1）使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）通过设计新的高阶低秩模型来预测交通事件;（3）联网车辆和路边传感器系统的协作;（4）通过实时众包来自用户报告的地面真相来验证交通状况预测;（5）选择众包参与者，该众包参与者招募并选择仪表化联网车辆的自愿操作者以提供车载感测读数;（6）从众包车辆中选择高质量和多样化的图像和视频，为交通预测提供更好的数据;和第7段）设计最佳的改道策略，在潜在的交通中断时改善通勤者的路线。该奖项反映了NSF的法定使命，并通过评估被认为值得支持使用基金会的知识价值和更广泛的影响审查标准。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Compact Scheduling for Task Graph Oriented Mobile Crowdsourcing

面向任务图的移动众包的紧凑调度

DOI：
10.1109/tmc.2020.3040007
发表时间：
2020
期刊：
IEEE Transactions on Mobile Computing
影响因子：
7.9
作者：
Wang, Liang;Yu, Zhiwen;Han, Qi;Yang, Dingqi;Pan, Shirui;Yao, Yuan;Zhang, Daqing
通讯作者：
Zhang, Daqing

On Mean-Optimal Robust Linear Discriminant Analysis