Capturing Correlation in Route Choice Models using Subnetworks
When using random utility models for a route choice problem, choice set generation and correlation among alternatives are two issues that make the modelling complex. In this paper, we propose a modelling approach where the path overlap is captured with a subnetwork. A subnetwork is a simplification of the road network only containing easy identifiable and behaviourally relevant roads. in practise, the subnetwork can easily be defined based on the route network hierarchy. We propose a model where the subnetwork is used for defining the correlation structure of the choice model. The motivation is to explicitly capture the most important correlation without considerably increasing the model complexity. We present estimation results of a factor analytic specification of a mixture of Multinomial Logit model, where the correlation among paths is captured both by a Path Size attribute and error components. The estimation is based on a GPS dataset collected in the Swedish city of Borlänge. The results show a significant increase in model fit for the Error Component model compared to a Path Size Logit and Multinomial Logit models. Moreover, the correlation parameters are significant. We also analyse the performance of the different models regarding prediction of choice probabilities. The results show a better performance of the Error Component model compared to the Path Size Logit and Multinomial Logit models.