TY - GEN
T1 - A taxi driving fraud detection system
AU - Ge, Yong
AU - Xiong, Hui
AU - Liu, Chuanren
AU - Zhou, Zhi Hua
PY - 2011
Y1 - 2011
N2 - Advances in GPS tracking technology have enabled us to install GPS tracking devices in city taxis to collect a large amount of GPS traces under operational time constraints. These GPS traces provide unparallel opportunities for us to uncover taxi driving fraud activities. In this paper, we develop a taxi driving fraud detection system, which is able to systematically investigate taxi driving fraud. In this system, we first provide functions to find two aspects of evidences: travel route evidence and driving distance evidence. Furthermore, a third function is designed to combine the two aspects of evidences based on Dempster-Shafer theory. To implement the system, we first identify interesting sites from a large amount of taxi GPS logs. Then, we propose a parameter-free method to mine the travel route evidences. Also, we introduce routemark to represent a typical driving path from an interesting site to another one. Based on routemark, we exploit a generative statistical model to characterize the distribution of driving distance and identify the driving distance evidences. Finally, we evaluate the taxi driving fraud detection system with large scale real-world taxi GPS logs. In the experiments, we uncover some regularity of driving fraud activities and investigate the motivation of drivers to commit a driving fraud by analyzing the produced taxi fraud data.
AB - Advances in GPS tracking technology have enabled us to install GPS tracking devices in city taxis to collect a large amount of GPS traces under operational time constraints. These GPS traces provide unparallel opportunities for us to uncover taxi driving fraud activities. In this paper, we develop a taxi driving fraud detection system, which is able to systematically investigate taxi driving fraud. In this system, we first provide functions to find two aspects of evidences: travel route evidence and driving distance evidence. Furthermore, a third function is designed to combine the two aspects of evidences based on Dempster-Shafer theory. To implement the system, we first identify interesting sites from a large amount of taxi GPS logs. Then, we propose a parameter-free method to mine the travel route evidences. Also, we introduce routemark to represent a typical driving path from an interesting site to another one. Based on routemark, we exploit a generative statistical model to characterize the distribution of driving distance and identify the driving distance evidences. Finally, we evaluate the taxi driving fraud detection system with large scale real-world taxi GPS logs. In the experiments, we uncover some regularity of driving fraud activities and investigate the motivation of drivers to commit a driving fraud by analyzing the produced taxi fraud data.
KW - Dempster-shafer theory
KW - Location traces
KW - Taxi driving fraud
UR - http://www.scopus.com/inward/record.url?scp=84863127286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84863127286&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2011.18
DO - 10.1109/ICDM.2011.18
M3 - Conference contribution
AN - SCOPUS:84863127286
SN - 9780769544083
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 181
EP - 190
BT - Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011
T2 - 11th IEEE International Conference on Data Mining, ICDM 2011
Y2 - 11 December 2011 through 14 December 2011
ER -