Abstract:
The high values of vehicles, the inadequate
infrastructure cause traffic congestion. Congested
roads can be avoided by determining the travel-time
for a particular road ahead of time. Traffic prediction
and travel time estimation has traditionally relied on
expensive measuring methods such as loop detectors,
vehicle identification devices. In this paper, we use
mobile GPS equipments on vehicles to gather data for
cheaper and real time travel-time estimation. We use
this data to develop the prediction system for traffic
congestion in order to improve the quality and safety
of vehicle movement and for minimization the time
and costs when vehicles are moved at the specified
routes. We collect the GPS data and classify them
with K-Means algorithm. Moreover, framework based
on Markov model is used to predict traffic and
Hadoop is used as cloud storage and platform, to
accelerate the processing computing speed and allow handling of large-scale data.
