TY - GEN
T1 - Detect Hidden Road Hazards combining Multiple Social Media Data
AU - Jin, Fang
AU - Liu, Hongchao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - It is imperatively important to spot hidden road hazards which cause a high proportion of traffic incidents. Social media data is featured by its innumerable and up-to-date information, and provides a promising approach to road hazard spotting. However, this research area is not well studied yet. In this paper, we present our view of the important research issues, including challenges of mining spatio-temporal dataset, road hazards reasoning and etc.
AB - It is imperatively important to spot hidden road hazards which cause a high proportion of traffic incidents. Social media data is featured by its innumerable and up-to-date information, and provides a promising approach to road hazard spotting. However, this research area is not well studied yet. In this paper, we present our view of the important research issues, including challenges of mining spatio-temporal dataset, road hazards reasoning and etc.
UR - http://www.scopus.com/inward/record.url?scp=85062642535&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622256
DO - 10.1109/BigData.2018.8622256
M3 - Conference contribution
AN - SCOPUS:85062642535
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 4559
EP - 4560
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Abe, Naoki
A2 - Liu, Huan
A2 - Pu, Calton
A2 - Hu, Xiaohua
A2 - Ahmed, Nesreen
A2 - Qiao, Mu
A2 - Song, Yang
A2 - Kossmann, Donald
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Saltz, Jeffrey
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Big Data, Big Data 2018
Y2 - 10 December 2018 through 13 December 2018
ER -