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 statis- tical data analysis are required. A very important problem for cloud-based TCMS is the selection of the shortest itinerary, which requires rou te 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 sam- ples 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 plug-in estimator. The analytical expressions for the expectations and variances of the proposed estimators are derived, which allow theoretical evaluation of the esti mators' quality. The ex- perimental 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.
['Jelena Fiosina', 'Maksims Fiosins']
Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network