Calibration and Analysis of Taxi Trajectories

PhD Thesis Proposal Defence


Title: "Calibration and Analysis of Taxi Trajectories"

by

Mr. Xibo ZHOU


Abstract:

Taxis are an important part of the public transportation system
in large cities, providing convenience for our daily life. In practice,
the information contained in taxi trajectory data are imprecise and
incomplete due to various factors such as measurement noise, low sampling
rate, and geographic sparsity. In this thesis proposal, we study the
problem of data calibration and applications of knowledge discovery from
taxi trajectory data, namely location information, passenger occupancy
status, and travel speed. Specifically, we design an interactive
map-matching system to involve human users in the loop to achieve high
map-matching accuracy, and propose various query selection strategies to
reduce the annotation cost effectively. Furthermore, we identify a new
type of taxi fraud called unmetered taxi rides, propose a learning model
to predict the passenger occupancy status of taxis, and develop a
heuristic algorithm to find fraudulent trajectories. Finally, we propose a
learning model to predict the travel speeds of individual taxis, which
will be applied for detecting taxi speeding.


Date:			Tuesday, 14 November 2017

Time:                  	2:00pm - 4:00pm

Venue:                  Room 3494
                         (lifts 25/26)

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


**** ALL are Welcome ****