Estimating route choice models from stochastically generated choice sets on large-scale networks correcting for unequal sampling probability

Vacca, Alessandro, Prato, Carlo Giacomo and Meloni, Italo (2015) Estimating route choice models from stochastically generated choice sets on large-scale networks correcting for unequal sampling probability. Transportation Research Record, 2493 2493: 11-18. doi:10.3141/2493-02


Author Vacca, Alessandro
Prato, Carlo Giacomo
Meloni, Italo
Title Estimating route choice models from stochastically generated choice sets on large-scale networks correcting for unequal sampling probability
Journal name Transportation Research Record   Check publisher's open access policy
ISSN 0361-1981
2169-4052
Publication date 2015-01-01
Year available 2015
Sub-type Article (original research)
DOI 10.3141/2493-02
Open Access Status Not Open Access
Volume 2493
Issue 2493
Start page 11
End page 18
Total pages 8
Place of publication Washington, DC United States
Publisher U.S. National Research Council * Transportation Research Board
Language eng
Abstract Route choice is one of the most complex decision-making contexts to represent mathematically, and the most frequently used approach to model route choice consists of generating alternative routes and modeling the preferences of utility-maximizing travelers. The main drawback of this approach is the dependency of the parameter estimates from the choice set generation technique. Bias introduced in model estimation has been corrected only for the random walk algorithm, which has problematic applicability to large-scale networks. This study proposes a correction term for the sampling probability of routes extracted with stochastic route generation. The term is easily applicable to large-scale networks and various environments, given its dependence only on a random number generator and the Dijkstra shortest path algorithm. The implementation for revealed preferences data, which consist of actual route choices collected in Cagliari, Italy, shows the feasibility of generating routes stochastically in a high-resolution network and calculating the correction factor. The model estimation with and without correction illustrates how the correction not only improves the goodness of fit but also turns illogical signs for parameter estimates to logical signs.
Keyword Constraints
Patterns
Behavior
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Civil Engineering Publications
Non HERDC
 
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Created: Tue, 19 Apr 2016, 01:45:47 EST by Carlo Prato on behalf of School of Civil Engineering