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Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 217)


New Internet technologies can considerably enhance contemporary traffic control and management systems (TCMS). Such systems need to process increasing volumes of data available in clouds, and so new algorithms and techniques for statistical data analysis are required. A very important problem for cloud-based TCMS is the selection of the shortest itinerary, which requires route comparison on the basis of historical data and dynamic observations. In the paper we compare two non-overlapping routes in a stochastic graph. The weights of the edges are considered to be independent random variables with unknown distributions. Only historical samples of the weights are available, and some edges may have common samples. Our purpose is to estimate the probability that the weight of the first route is greater than that of the second one. We consider the resampling estimator of the probability in the case of small samples and compare it with the parametric plugin estimator. The analytical expressions for the expectations and variances of the proposed estimators are derived, which allow theoretical evaluation of the estimators’ quality. The experimental results demonstrate that the resampling estimator is a suitable alternative to the parametric plug-in estimator. This problem is very important for a vehicle decision-making procedure to choose route from the available alternatives.


  • traffic control and management
  • future Internet
  • stochastic graph
  • shortest route
  • resampling
  • small samples
  • estimation
  • simulation

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  • DOI: 10.1007/978-3-319-00551-5_13
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Correspondence to Jelena Fiosina .

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© 2013 Springer International Publishing Switzerland

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Fiosina, J., Fiosins, M. (2013). Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00550-8

  • Online ISBN: 978-3-319-00551-5

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