Metropolis-Hastings sampling of paths

We consider the previously unsolved problem of sampling paths according to a given distribution from a general network. The problem is difficult because of the combinatorial number of alternatives, which prohibits a complete enumeration of all paths and hence also forbids to compute the normalizing constant of the sampling distribution. The problem is important because the ability to sample from a known distribution introduces mathematical rigor into many applications, including the estimation of choice models with sampling of alternatives that can be formalized as paths in a decision network (most obviously route choice), probabilistic map matching, dynamic traffic assignment, and route guidance. (C) 2012 Elsevier Ltd. All rights reserved.


Presented at:
Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), KU Leuven, Leuven, Belgium, June 22, 2011
Year:
2011
Publisher:
Oxford, Pergamon-Elsevier Science Ltd
Keywords:
Laboratories:




 Record created 2011-08-11, last modified 2018-03-17


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