CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 平行控制
Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting
Lu, Hao1,2; Zhu, Yifan1; Shi, Kaize1; Lv, Yisheng2; Shi, Pengfei1; Niu, Zhendong1,3
Source PublicationAPPLIED SCIENCES-BASEL
2018-07-01
Volume8Issue:7
SubtypeArticle
AbstractTraffic situation awareness and alerting assisted by adverse weather conditions contributes to improve traffic safety, disaster coping mechanisms, and route planning for government agencies, business sectors, and individual travelers. However, at the city level, the physical sensor-generated data are partly held by different transportation and meteorological departments, which causes problems of "isolated information" for data fusion. Furthermore, it makes traffic situation awareness and estimation challenging and ineffective. In this paper, we leverage the power of crowdsourcing knowledge in social media and propose a novel way to forecast and generate alerts for city-level traffic incidents based on a social approach rather than traditional physical approaches. Specifically, we first collect adverse weather topics and reports of traffic incidents from social media. Then, we extract temporal, spatial, and meteorological features as well as labeled traffic reaction values corresponding to the social media "heat" for each city. Afterwards, the regression and alerting model is proposed to estimate the city-level traffic situation and give the suggestion of warning levels. The experiments show that the proposed model equipped with gcForest achieves the best root mean square error (RMSE) and mean absolute percentage error (MAPE) score on the social traffic incidents test dataset. Moreover, we consider the news report as an objective measurement to flexibly validate the feasibility of proposed model from social cyberspace to physical space. Finally, a prototype system was deployed and applied to government agencies to provide an intuitive visualization solution as well as decision support assistance.
KeywordCity-level Traffic Alerting Adverse Weather Social Transportation Crowdsourcing Knowledge Intelligent Transportation System
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.3390/app8071193
WOS KeywordCLIMATE-CHANGE ; FLOW PREDICTION ; TWITTER ; TRANSPORTATION ; ACCIDENT ; NETWORKS ; MACHINE ; IMPACT
Indexed BySCI ; SSCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61671485 ; Ministry of Education-China Mobile Research Foundation(2016/2-7) ; Public Weather Service Center of China Meteorological Administration ; 61533019 ; 61233001 ; 61370137)
WOS Research AreaChemistry ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000441814300183
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21857
Collection复杂系统管理与控制国家重点实验室_平行控制
Affiliation1.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100081, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Univ Pittsburgh, Sch Comp & Informat, Pittsburgh, PA 15260 USA
Recommended Citation
GB/T 7714
Lu, Hao,Zhu, Yifan,Shi, Kaize,et al. Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting[J]. APPLIED SCIENCES-BASEL,2018,8(7).
APA Lu, Hao,Zhu, Yifan,Shi, Kaize,Lv, Yisheng,Shi, Pengfei,&Niu, Zhendong.(2018).Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting.APPLIED SCIENCES-BASEL,8(7).
MLA Lu, Hao,et al."Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting".APPLIED SCIENCES-BASEL 8.7(2018).
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