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Prediction System for Traffic Congestion using GPS Data on Hadoop Cloud Storage

http://hdl.handle.net/20.500.12678/0000004803
c57376bb-2dbf-4dba-9680-08ab788a75d0
6712a097-624a-45dc-bbb0-c5dd096a8fb7
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12013.pdf 12013.pdf (118 Kb)
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Article
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Publication
Title
Title Prediction System for Traffic Congestion using GPS Data on Hadoop Cloud Storage
Language en
Publication date 2014-02-17
Authors
Lwin, Hnin Thant
Naing, Thinn Thu
Description
The high values of vehicles, the inadequateinfrastructure cause traffic congestion. Congestedroads can be avoided by determining the travel-timefor a particular road ahead of time. Traffic predictionand travel time estimation has traditionally relied onexpensive measuring methods such as loop detectors,vehicle identification devices. In this paper, we usemobile GPS equipments on vehicles to gather data forcheaper and real time travel-time estimation. We usethis data to develop the prediction system for trafficcongestion in order to improve the quality and safetyof vehicle movement and for minimization the timeand costs when vehicles are moved at the specifiedroutes. We collect the GPS data and classify themwith K-Means algorithm. Moreover, framework basedon Markov model is used to predict traffic andHadoop is used as cloud storage and platform, toaccelerate the processing computing speed and allow handling of large-scale data.
Keywords
Traffic Prediction, GPS, Markov, Hadoop, MapReduce, K-Means
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/70
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