Framework for fusing traffic information from social and physical transportation data

doi:10.1371/journal.pone.0201531

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 平行智能技术与系统团队

	Framework for fusing traffic information from social and physical transportation data
	Zheng, Zhihao 1; Wang, Chengcheng 1; Wang, Pu 1; Xiong, Yusha 1; Zhang, Fan 2; Lv, Yisheng3
发表期刊	PLOS ONE
	2018-08-02
卷号	13 期号:8
文章类型	Article
摘要	Tremendous volumes of messages on social media platforms provide supplementary traffic information and encapsulate crowd wisdom for solving transportation problems. However, social media messages manifested in human languages are usually characterized with redundant, fuzzy and subjective features. Here, we develop a data fusion framework to identify social media messages reporting non-recurring traffic events by connecting the traffic events with traffic states inferred from taxi global positioning system (GPS) data. Temporal-patial information of traffic anomalies caused by the traffic events are then retrieved from anomalous traffic states. The proposed framework successfully identified accidental traffic events with various scales and exhibited strong performance in event descriptions. Even though social media messages are generally posted after the occurrence of anomalous traffic states, resourceful event descriptions in the messages are helpful in explaining traffic anomalies and for deploying suitable countermeasures.
WOS标题词	Science & Technology
DOI	10.1371/journal.pone.0201531
关键词[WOS]	TAXI GPS TRACES ; MEDIA DATA ; BIG-DATA ; UTILITY-MAXIMIZATION ; TRAVEL BEHAVIOR ; TIME ; RELIABILITY ; CHALLENGES ; NETWORK ; TWITTER
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61473320) ; Fok Ying Tong Education Foundation(141075) ; Project of Innovation-driven Plan in Central South University(2016CSX014)
WOS研究方向	Science & Technology - Other Topics
WOS类目	Multidisciplinary Sciences
WOS记录号	WOS:000440778600053
引用统计	被引频次：15[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/173211/21846
专题	多模态人工智能系统全国重点实验室_平行智能技术与系统团队
作者单位	1.Cent S Univ, Sch Traff & Transportat Engn, Changsha, Hunan, Peoples R China 2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Guangdong, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zheng, Zhihao,Wang, Chengcheng,Wang, Pu,et al. Framework for fusing traffic information from social and physical transportation data[J]. PLOS ONE,2018,13(8).
APA	Zheng, Zhihao,Wang, Chengcheng,Wang, Pu,Xiong, Yusha,Zhang, Fan,&Lv, Yisheng.(2018).Framework for fusing traffic information from social and physical transportation data.PLOS ONE,13(8).
MLA	Zheng, Zhihao,et al."Framework for fusing traffic information from social and physical transportation data".PLOS ONE 13.8(2018).