TRAVEL COST ESTIMATION AND PREDICTION ON ROAD NETWORK

MPhil Thesis Defence


Title: "TRAVEL COST ESTIMATION AND PREDICTION ON ROAD NETWORK"

By

Mr. Zihao YE


Abstract

With the development of modern technology, advanced GPS tracking solutions 
offer us the best opportunity to learn the city’s traffic knowledge. Travel 
cost on road segments is the important hidden knowledge in the metropolitan 
city, which can be leveraged by travel route planning, traffic event discovery, 
and city’s fraud detection, etc. This paper addresses the problem of travel 
cost estimation and prediction on road network with a not only innovative but 
also practical solution. Although some previous work deal with the same topic, 
they estimate travel cost on road segments based on the spatial and temporal 
smoothness of adjacent neighbors, which is unreasonable under some 
circumstances. Other works predicting travel cost ignore the obstacle of data 
sparsity by not predicting the travel cost of road segments without traffic 
data. In viewing this defect, we proposed the methodology to estimate and 
predict travel cost based on objects similarity. To be more specifically, we 
define the spatial temporal dividing sample as the minimal unit whose travel 
cost is to be estimated or predicted. Knowledgeable samples present those who 
have traffic data points, and their travel cost can be estimated by the traffic 
data. While unknowledgeable samples are those who encounter the problem of data 
sparsity, and have no estimation of travel cost since the lack of traffic data 
points. By extracting both the static and dynamic features for those samples, 
we profile them and apply the clustering algorithm on them to identify similar 
samples. Within each cluster, we leverage the artificial neural network to 
build the mapping relationship between knowledgeable samples features and their 
travel cost. With the help of this mapping relationship, we finally infer the 
clusters unknowledgeable samples travel cost. In terms of travel cost 
prediction, based on the intuition that similar road segments share similar 
travel cost pattern, we put all the road segments into different clusters and 
share the observed traffic data in the same cluster to overcome the obstacle of 
data sparsity. And finally we leverage the time series predicting model to 
predict the travel cost in the future. We evaluate our methodology on one-month 
real traffic data from Shanghai. The experimental results on both small data 
set and large data set show the validity and practicality of our methodology.


Date:			Thursday, 29 May 2014

Time:			5:00pm - 7:00pm

Venue:			Room 4483
 			Lifts 25/26

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Lei Chen (Chairperson)
 			Dr. Raymond Wong


**** ALL are Welcome ****