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CRSM: a practical crowdsourcing-based road surface monitoring system
文献类型：期刊论文
| 作者 | Chen, Kongyang; Tan, Guang; Lu, Mingming; Wu, Jie |
| 刊名 | WIRELESS NETWORKS
 |
| 出版日期 | 2016 |
| 英文摘要 | Detecting road potholes and road roughness levels is key to road condition monitoring, which impacts transport safety and driving comfort. We propose a crowdsourcing-based road surface monitoring system, simply called CRSM. CRSM can effectively detect road potholes and evaluate road roughness levels using hardware modules mounted on distributed vehicles. These modules use low-end accelerometers and GPS devices to obtain vibration patterns, locations, and vehicle velocities. Considering the high cost of onboard storage and wireless transmission, a novel light-weight data mining algorithm is proposed to detect roadsurface events and transmit potential pothole information to a central server. The central server gathers reports from multiple vehicles, and makes a comprehensive evaluation on road surface quality. We have implemented a product-quality system, and have deployed it on 100 taxies in the Shenzhen urban area. The results show that CRSM can detect road potholes with 90 % accuracy, with nearly zero false alarms.CRSM can also evaluate road roughness levels correctly, even with some interferences from small bumps or potholes. |
| 收录类别 | SCI |
| 原文出处 | http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=105&SID=W2QBM5MNpeVQwHYBFGv&page=1&doc=1 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10258]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | WIRELESS NETWORKS |
| 推荐引用方式 GB/T 7714 | Chen, Kongyang,Tan, Guang,Lu, Mingming,et al. CRSM: a practical crowdsourcing-based road surface monitoring system[J]. WIRELESS NETWORKS,2016. |
| APA | Chen, Kongyang,Tan, Guang,Lu, Mingming,&Wu, Jie.(2016).CRSM: a practical crowdsourcing-based road surface monitoring system.WIRELESS NETWORKS. |
| MLA | Chen, Kongyang,et al."CRSM: a practical crowdsourcing-based road surface monitoring system".WIRELESS NETWORKS (2016). |
入库方式： OAI收割
来源：深圳先进技术研究院
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