Modeling Travel Demand in Portland, Oregon

  • Katja Ickstadt
  • Robert L. Wolpert
  • Xuedong Lu
Part of the Lecture Notes in Statistics book series (LNS, volume 133)


An important problem in transportation planning is the modeling of patterns of trip-making—especially trips from home (origin) to work (destination) within a fixed geographic area. The standard tool used to study such OD flows is the so-called “gravity model”, a Poisson log-linear regression model for studying the number of trips from origins within one element of a fixed partition area (traffic analysis zone, for example) to destinations within another.

We present an alternative: a gridless Bayesian hierarchical Poisson/gamma random field model, allowing us to incorporate spatial correlation explicitly. The models are fitted to a subset of data from the 1994/95 METRO survey of Portland, Oregon, and are internally validated on a reserved portion of the survey data. Bayesian posterior probability distributions are calculated using a Markov chain Monte Carlo integration scheme based on a novel method for simulating samples from gamma random fields.

Some key words

Bayesian mixture models gamma process Markov chain Monte Carlo simulation 


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Copyright information

© Springer Science+Business Media New York 1998

Authors and Affiliations

  • Katja Ickstadt
  • Robert L. Wolpert
  • Xuedong Lu

There are no affiliations available

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