Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting

doi:10.3390/app8071193

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	Using Adverse Weather Data in Social Media to Assist with City-Level Traffic Situation Awareness and Alerting
	Lu, Hao1,2 ; Zhu, Yifan 1; Shi, Kaize 1; Lv, Yisheng2 ; Shi, Pengfei 1; Niu, Zhendong 1,3
发表期刊	APPLIED SCIENCES-BASEL
	2018-07-01
卷号	8 期号:7
文章类型	Article
摘要	Traffic 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.
关键词	City-level Traffic Alerting Adverse Weather Social Transportation Crowdsourcing Knowledge Intelligent Transportation System
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.3390/app8071193
关键词[WOS]	CLIMATE-CHANGE ; FLOW PREDICTION ; TWITTER ; TRANSPORTATION ; ACCIDENT ; NETWORKS ; MACHINE ; IMPACT
收录类别	SCI ; SSCI
语种	英语
项目资助者	National 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研究方向	Chemistry ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:000441814300183
引用统计	被引频次：21[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/21857
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队
作者单位	1.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
第一作者单位	中国科学院自动化研究所
推荐引用方式 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).