Highlights ► Sampling of paths according to given distributions in general networks. ► Wide range of transportation-related applications. ► Applicability to large real-world problem instances. ► Thorough experimental analysis of algorithmic correctness and efficiency.

    Abstract 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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Metropolis–Hastings sampling of paths


    Contributors:


    Publication date :

    2012-11-28


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English