  This study performs an attribute-level analysis of the global and local path
preferences of network travelers. To this end, a reward decomposition approach
is proposed and integrated into a link-based recursive (Markovian) path choice
model. The approach decomposes the instantaneous reward function associated
with each state-action pair into the global utility, a function of attributes
globally perceived from anywhere in the network, and the local utility, a
function of attributes that are only locally perceived from the current state.
Only the global utility then enters the value function of each state,
representing the future expected utility toward the destination. This
global-local path choice model with decomposed reward functions allows us to
analyze to what extent and which attributes affect the global and local path
choices of agents. Moreover, unlike most adaptive path choice models, the
proposed model can be estimated based on revealed path observations (without
the information of plans) and as efficiently as deterministic recursive path
choice models. The model was applied to the real pedestrian path choice
observations in an urban street network where the green view index was
extracted as a visual street quality from Google Street View images. The result
revealed that pedestrians locally perceive and react to the visual street
quality, rather than they have the pre-trip global perception on it.
Furthermore, the simulation results using the estimated models suggested the
importance of location selection of interventions when policy-related
attributes are only locally perceived by travelers.
