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A Solution of Traffic Problems Based on MapReduce

  • Yuanhao Wang
  • Pan Chen
  • Lei Cheng
  • Hui Tong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 216)

Abstract

With the number of cars increasing sharply, traffic becomes a big problem in our daily life. We may think of solving the problem by analyzing the global position system (GPS) data got from cars, but another problem arises, that is, how we can analyze the large-scale data set. The MapReduce programming model is inspired by Google and targets data-intensive parallel computations. This paper presents the results of the situations of several roads at different time and the trends of traffic in each road, after analyzing the huge database by taking a modified velocity–time integration we proposed and run on the MapReduce parallel model. The experiment results show that the algorithm is effective and efficient and the model is efficient to handle with large-scale data set.

Keywords

MapReduce Parallel computation Traffic problems GPS Modified velocity–time model 

Notes

Acknowledgments

This research was jointly supported by Research Innovation Fund for College Student of Beijing University of Posts and Telecommunications, P. R. China.

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Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

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