Abstract:
Road traffic congestion is major
problem in urban area of both developing and
developed countries. In order to reduce this
problem, traffic congestion states of road
networks are estimated so that congested road
can be avoided. In this system, we estimate the
current traffic congestion states of user’s desired
source and destination and present the estimated
results in Google Map. To get the traffic data we
use GPS data from mobile phones on vehicles
but this GPS points can have ambiguity. The
decision support topological based map
matching algorithm that can solve the ambiguity
of GPS data is used to identify which vehicles
are on which road by matching these GPS data
with road networks. The historical traffic
condition data of each road network on each
time using the pre-collected data are utilized in
this research. We use Hidden Markov model
(HMM) for estimating the traffic condition states
of these road network using historical traffic
data. These estimated traffic probabilities states
are presented by coloring (traffic jam for red,
traffic heavy for blue and traffic normal for
green) users’ desired source and destination road
segments on Google Map. We evaluate our
estimating system using dataset generated by
collect data from mobile phone-equipped
vehicles over a period of 4 months in Yangon.
