TY - GEN
T1 - Detecting taxi speeding from sparse and low-sampled trajectory data
AU - ZHOU, Xibo
AU - LUO, Qiong
AU - ZHANG, Dian
AU - NI, Lionel M.
N1 - This work is supported in part by the Guangdong Pre-national project 2014GKXM054 and the Guangdong Province Key Laboratory of Popular High Performance Computers 2017B030314073.
PY - 2018
Y1 - 2018
N2 - Taxis are a major means of public transportation in large cities, and speeding is a common problem among motor vehicles, including taxis. Unless caught by sensors or patrol officers, many speeding incidents go unnoticed, which pose potential threat to road safety. In this paper, we propose to detect speeding behaviors of individual taxis from taxi trajectory data. Such detection results are useful for driver risk analysis and road safety management. However, the taxi trajectory data are geographically sparse and the sample rate is low. Furthermore, existing methods mainly deal with the estimation of collective road speeds whereas we focus on the speeds of individual vehicles. As such, we propose to use a two-fold collective matrix factorization (CMF)-based model to estimate the individual vehicle speed. We have evaluated our method on real-world datasets, and the results show the effectiveness of our method in detecting taxi speeding behaviors.
AB - Taxis are a major means of public transportation in large cities, and speeding is a common problem among motor vehicles, including taxis. Unless caught by sensors or patrol officers, many speeding incidents go unnoticed, which pose potential threat to road safety. In this paper, we propose to detect speeding behaviors of individual taxis from taxi trajectory data. Such detection results are useful for driver risk analysis and road safety management. However, the taxi trajectory data are geographically sparse and the sample rate is low. Furthermore, existing methods mainly deal with the estimation of collective road speeds whereas we focus on the speeds of individual vehicles. As such, we propose to use a two-fold collective matrix factorization (CMF)-based model to estimate the individual vehicle speed. We have evaluated our method on real-world datasets, and the results show the effectiveness of our method in detecting taxi speeding behaviors.
KW - Collective matrix factorization
KW - Speeding
KW - Trajectory
UR - http://www.scopus.com/inward/record.url?scp=85051133543&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-96893-3_16
DO - 10.1007/978-3-319-96893-3_16
M3 - Conference paper (refereed)
AN - SCOPUS:85051133543
SN - 9783319968926
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 214
EP - 222
BT - Web and Big Data : Second International Joint Conference, APWeb-WAIM 2018 : proceedings
A2 - CAI, Yi
A2 - ISHIKAWA, Yoshiharu
A2 - XU, Jianliang
PB - Springer-Verlag GmbH and Co. KG
CY - Cham
T2 - 2nd Asia Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2018
Y2 - 23 July 2018 through 25 July 2018
ER -
