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 -