Taxi Fraud Detection via Speed-based Clustering

MPhil Thesis Defence


Title: "Taxi Fraud Detection via Speed-based Clustering"

By

Mr. Yiming Luo


Abstract

Taxi is a major transportation in urban area, offering great benefits and 
convenience to our daily life. But one of the major business frauds in 
taxi is the charging fraud, specifically, charging more fees than the 
actual service distance. In practice, it is hard for us to always monitor 
taxis and detect such fraud. Thanks to the Global Position System (GPS) 
embedded in taxis, we can collect the GPS reports from the taxis, having 
the location, time, and driving information. Intuitively, we can utilize 
such data to compute the actual service distance of taxis in the city map. 
But due to the extremely limited report, notable location errors, complex 
city map and road networks, our task to detect the taxi fraud faces great 
challenges and the naive method does not work well.

In this thesis, we have a basic observation that the fraud taxi always 
changes the taximeter in a much larger scale, and as a result it not only 
makes the service distance larger, but also the reported taxi speed much 
larger. Fortunately, the speed information collected from the GPS report 
is accurate. Hence, we utilize the speed information to design a novel 
speed-based clustering method to detect the fraud taxi, which is robust to 
the location errors, and independent of the map and road networks. The 
experiments on the real life data sets confirm the better accuracy, 
scalability and more efficient computation of our method, comparing to the 
current map-matching methods.

Keywords— speed, clustering, taxi fraud detection.


Date:			Wednesday, 7 July 2010

Time:			4:00pm – 6:00pm

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Qiong Luo (Chairperson)
 			Dr. Lei Chen


**** ALL are Welcome ****