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)


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.


MapReduce Parallel computation Traffic problems GPS Modified velocity–time model 



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


  1. 1.
    Jiang G, Chang A, Li Q, Yi F (2009) Estimation Approaches of average link travel time using GPS data[J]. J Jilin Univ: Eng Technol 39(2):182–186Google Scholar
  2. 2.
    Zhu L, Lu Y, Mao S (2006) Estimation of parameters of mixed exponential distribution. Chin J Appl Prob Stat 22(2):137–150Google Scholar
  3. 3.
    Guan Y, Zhang N, Zhu J, et al (2010) Modeling on-ramp capacity with driver behavior variation[J]. J Transport Syst Eng Info Technol 12(1):122–127Google Scholar
  4. 4.
    Casella G, Berger RL (2002) Statistical inference [M], China Machine Press, BeijingGoogle Scholar
  5. 5.
    Jin S, Wang D, Tao P et al (2010) Nonlane based full velocity difference car following model[J]. Phys A 389(21):4654–4662Google Scholar
  6. 6.
    Stonebraker M, Abadi D, DeWitt DJ, Madden S, Paulson E, Pavlo A, Rasin A et al (2010) MapReduce and parallel DBMSs: Friends or Foes? Commun ACM 53(1):64–71CrossRefGoogle Scholar
  7. 7.
    Dean J, Ghemawat S (2004) MapReduce: simplified data processing on large clusters. OSDI 23:9–27Google Scholar
  8. 8.
    Abadi DJ, Madden SR, Hachem N (2008) Column-stores vs. row-stores: how different are they really? In: Proceedings of the SIGMOD conference on management of data, vol 1. pp 22–29Google Scholar
  9. 9.
    Boral H et al (2009) Prototyping Bubba, a highly parallel database system. IEEE T Knowl and Data En 2:4–24Google Scholar
  10. 10.
    Stonebraker M, Rowe L (1986) The design of Postgres. In: Proceedings of the SIGMOD conference, pp 340–355Google Scholar
  11. 11.
    Teradata Corp. (1985) Database computer system manual, Release 1.3, Los Angeles, CAGoogle Scholar
  12. 12.
    Bai H, Yao L, Lu S (2011) An alert aggregation algorithm based on MapReduce computing model. Inform Technol 4:85–92Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations