SCC-IRG Track 2: Towards Quality Aware Crowdsourced Road Sensing for Smart Cities

SCC-IRG 第 2 轨：迈向智慧城市的质量意识众包道路传感

• 批准号: 1737590
• 负责人: Chunming Qiao
• 金额: $ 100万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1737590&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Towards; Quality

With nearly a billion automobiles on the road today, the current transportation systems have begun to show signs of serious strain, such as congestions, traffic accidents, road surface defects, and malfunctioning traffic regulation infrastructures. Therefore, it is of great importance to collect and disseminate road/traffic condition information accurately, efficiently, and timely. Traditionally, road and traffic monitoring are conducted through either stationary sensors or instrumented probe vehicles. Unfortunately, the prohibitively high deployment cost of such devices makes it impossible to achieve large-scale deployment, leading to limited road coverage and delayed information update. To mitigate these problems, this project develops QuicRoad, a Quality of Information (QoI) aware crowdsourced road sensing system that can collect road/traffic information from a variety of sources, including smartphones, social media and transportation authorities (as well as future connected vehicles), and then distribute the collected information in real time. The PIs team up with local transportation agencies in the Buffalo-Niagara region on applications related to road surface and traffic condition monitoring, border crossing delay estimation, and incident management.This project integrates across both social and technological research dimensions. In the technological dimension, it leads to a novel Quality of Information (QoI) aware information integration framework that can jointly optimize the estimation of the QoI of various sources, and the information-integration as well as decision-making process. In the social dimension, it answers fundamental questions such as whether and to what degree the road/traffic condition information provided by the proposed QuicRoad system would change the social behavior of the travelers. By seamlessly integrating the technological and social dimensions, the proposed research can not only improve the coverage and quality of assisted driving and road navigation services for travelers, but also support policy-making in traffic planning and operations by transportation authorities. The research will potentially benefit a wide spectrum of real-world road sensing applications aimed at improving road safety, mitigating traffic congestions, and reducing fuel consumption and emissions, and eventually contribute to building a sustainable society.

随着近10亿辆汽车在道路上行驶，目前的交通系统已经开始显示出严重紧张的迹象，如交通事故，交通事故，路面缺陷和交通管制基础设施故障。因此，准确、高效、及时地收集和发布道路/交通状况信息具有重要意义。传统上，道路和交通监测是通过固定传感器或仪表探测车辆进行的。不幸的是，这种设备的高昂部署成本使其无法实现大规模部署，导致道路覆盖范围有限和信息更新延迟。为了缓解这些问题，该项目开发了QuicRoad，这是一种信息质量（QoI）感知的众包道路传感系统，可以从各种来源收集道路/交通信息，包括智能手机，社交媒体和交通管理机构（以及未来的联网车辆），然后以真实的时间分发收集的信息。PI与布法罗-尼亚加拉地区的当地交通机构合作，开发与路面和交通状况监测、过境延误估计和事故管理相关的应用程序。该项目整合了社会和技术研究维度。在技术层面，它导致了一种新的信息质量（QoI）意识的信息集成框架，可以联合优化各种来源的QoI的估计，信息集成以及决策过程。在社会层面，它回答了一些基本问题，如是否以及在何种程度上所提出的QuicRoad系统提供的道路/交通状况信息将改变社会行为的旅客。通过无缝整合技术和社会维度，拟议的研究不仅可以提高辅助驾驶和道路导航服务的覆盖范围和质量，还可以支持交通管理部门在交通规划和运营方面的政策制定。该研究将潜在地有益于广泛的现实世界道路传感应用，旨在提高道路安全性，减轻交通拥堵，减少燃料消耗和排放，并最终为建设可持续发展的社会做出贡献。

项目成果

Travel purpose inference with GPS trajectories, POIs, and geo-tagged social media data

• DOI: 10.1109/bigdata.2017.8258062
• 发表时间: 2017-12
• 期刊: 2017 IEEE International Conference on Big Data (Big Data)
• 作者: Chuishi Meng;Yu Cui;Qi He;Lu Su;Jing Gao

A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling

• DOI: 10.1145/3369815
• 发表时间: 2019-12
• 期刊: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
• 作者: Weida Zhong;Qiuling Suo;Fenglong Ma;Yunfei Hou;Abhishek Gupta;C. Qiao;Lu Su

Towards the Inference of Travel Purpose with Heterogeneous Urban Data

利用异构城市数据推断出行目的

• DOI: 10.1109/tbdata.2019.2921823
• 发表时间: 2022
• 期刊: IEEE Transactions on Big Data
• 影响因子: 7.2
• 作者: Meng, Chuishi;Cui, Yu;He, Qing;Su, Lu;Gao, Jing
• 通讯作者: Gao, Jing

Forecasting current and next trip purpose with social media data and Google Places

• DOI: 10.1016/j.trc.2018.10.017
• 发表时间: 2018-12-01
• 期刊: TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
• 影响因子: 8.3
• 作者: Cui, Yu;Meng, Chuishi;Gao, Jing
• 通讯作者: Gao, Jing

Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing

• DOI: 10.1109/tmc.2018.2868106
• 发表时间: 2019-08-01
• 期刊: IEEE TRANSACTIONS ON MOBILE COMPUTING
• 影响因子: 7.9
• 作者: Jin, Haiming;Su, Lu;Xu, Jinhui
• 通讯作者: Xu, Jinhui