This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 37
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #307707
Title: Inference for Day-to-Day Dynamic Traffic Models
Author(s): Martin Luke Hazelton*+ and Katharina Parry
Companies: Massey University and Massey University
Address: Private Bag 11-222, Palmerston North, International, 4442, New Zealand
Keywords: MCMC ; network ; statistical linear inverse problem ; transportation
Abstract:

There is significant current interest in the transportation literature in the development of models to describe the day-to-day evolution of traffic flows over a network. We consider the problem of estimating the parameters of such models based on daily observations of traffic counts on a subset of network links. Like other inference problems for network-based models, the critical difficulty lies in the underdetermined linear system that relates link flows to the latent path flows. In particular, MCMC inference requires that we sample from the set of route flows consistent with the observed link flows, but enumeration of this set is usually computationally infeasible. By using a simple Markov model for traveller behaviour (conditional on link counts) we show that it is nonetheless possible to construct a suitable proposal distribution for use with certain classes of network.


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