A nested recursive logit model for route choice analysis

TY - JOUR

T1 - A nested recursive logit model for route choice analysis

AU - Mai, Tien

AU - Fosgerau, Mogens

AU - Frejinger, Emma

PY - 2015/5/1

Y1 - 2015/5/1

N2 - We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems.We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible parameter estimates and the fit is significantly better than the RL model. Moreover, the NRL model outperforms the RL model in terms of prediction.

AB - We propose a route choice model that relaxes the independence from irrelevant alternatives property of the logit model by allowing scale parameters to be link specific. Similar to the recursive logit (RL) model proposed by Fosgerau et al. (2013), the choice of path is modeled as a sequence of link choices and the model does not require any sampling of choice sets. Furthermore, the model can be consistently estimated and efficiently used for prediction.A key challenge lies in the computation of the value functions, i.e. the expected maximum utility from any position in the network to a destination. The value functions are the solution to a system of non-linear equations. We propose an iterative method with dynamic accuracy that allows to efficiently solve these systems.We report estimation results and a cross-validation study for a real network. The results show that the NRL model yields sensible parameter estimates and the fit is significantly better than the RL model. Moreover, the NRL model outperforms the RL model in terms of prediction.

KW - Cross-validation

KW - Maximum likelihood estimation

KW - Nested recursive logit

KW - Route choice modeling

KW - Substitution patterns

KW - Value iterations

UR - http://www.scopus.com/inward/record.url?scp=84926429454&partnerID=8YFLogxK

U2 - 10.1016/j.trb.2015.03.015

DO - 10.1016/j.trb.2015.03.015

M3 - Journal article

AN - SCOPUS:84926429454

SN - 0191-2615

VL - 75

SP - 100

EP - 112

JO - Transportation Research Part B: Methodological

JF - Transportation Research Part B: Methodological

ER -