  Although the recursive logit (RL) model has been recently popular and has led
to many applications and extensions, an important numerical issue with respect
to the computation of value functions remains unsolved. This issue is
particularly significant for model estimation, during which the parameters are
updated every iteration and may violate the feasibility condition of the value
function. To solve this numerical issue of the value function in the model
estimation, this study performs an extensive analysis of a prism-constrained RL
(Prism-RL) model proposed by Oyama and Hato (2019), which has a path set
constrained by the prism defined based upon a state-extended network
representation. The numerical experiments have shown two important properties
of the Prism-RL model for parameter estimation. First, the prism-based approach
enables estimation regardless of the initial and true parameter values, even in
cases where the original RL model cannot be estimated due to the numerical
problem. We also successfully captured a positive effect of the presence of
street green on pedestrian route choice in a real application. Second, the
Prism-RL model achieved better fit and prediction performance than the RL
model, by implicitly restricting paths with large detour or many loops.
Defining the prism-based path set in a data-oriented manner, we demonstrated
the possibility of the Prism-RL model describing more realistic route choice
behavior. The capture of positive network attributes while retaining the
diversity of path alternatives is important in many applications such as
pedestrian route choice and sequential destination choice behavior, and thus
the prism-based approach significantly extends the practical applicability of
the RL model.
