热门
An Unlicensed Taxi Identification Model Based on Big Data Analysis
文献类型：期刊论文
| 作者 | Yuan, W ; Deng, P ; Taleb, T ; Wan, JF ; Bi, CF |
| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
 |
| 出版日期 | 2016 |
| 卷号 | 17期号:6页码:1703-1713 |
| 关键词 | Big data intelligent transportation systems machine learning data-driven ITS unlicensed taxi |
| ISSN号 | 1524-9050 |
| 中文摘要 | Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS ((DITS)-I-2). Along with traffic growth, (DITS)-I-2 need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs. |
| 英文摘要 | Social networks and mobile networks are exposing human beings to a big data era. With the support of big data analytics, conventional intelligent transportation systems (ITS) are gradually changing into data-driven ITS ((DITS)-I-2). Along with traffic growth, (DITS)-I-2 need to solve more real-life problems, including the issue of unlicensed taxis and their identification, which potentially disrupts the taxi business sector and endangers society safety. As a remedy to this issue, a smart model is proposed in this paper to identify unlicensed taxis. The proposed model consists of two submodel components, namely, candidate selection model and candidate refined model. The former is used to screen out a coarse-grained suspected unlicensed taxi candidate list. The list is taken as an input for the candidate refined model, which is based on machine learning to get a fine-grained list of suspected unlicensed taxis. The proposed model is evaluated using real-life data, and the obtained results are encouraging, demonstrating its efficiency and accuracy in identifying unlicensed taxis, helping governments to better regulate the traffic operation and reduce associated costs. |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000377457200019 |
| 公开日期 | 2016-12-09 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/17326]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Yuan, W,Deng, P,Taleb, T,et al. An Unlicensed Taxi Identification Model Based on Big Data Analysis[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2016,17(6):1703-1713. |
| APA | Yuan, W,Deng, P,Taleb, T,Wan, JF,&Bi, CF.(2016).An Unlicensed Taxi Identification Model Based on Big Data Analysis.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,17(6),1703-1713. |
| MLA | Yuan, W,et al."An Unlicensed Taxi Identification Model Based on Big Data Analysis".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 17.6(2016):1703-1713. |
入库方式： OAI收割
来源：软件研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。