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This study performs an attribute-level analysis of the global and local pathpreferences of network travelers. To this end, a reward decomposition approachis proposed and integrated into a link-based recursive (Markovian) path choicemodel. The approach decomposes the instantaneous reward function associatedwith each state-action pair into the global utility, a function of attributesglobally perceived from anywhere in the network, and the local utility, afunction 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. Thisglobal-local path choice model with decomposed reward functions allows us toanalyze to what extent and which attributes affect the global and local pathchoices of agents. Moreover, unlike most adaptive path choice models, theproposed model can be estimated based on revealed path observations (withoutthe information of plans) and as efficiently as deterministic recursive pathchoice models. The model was applied to the real pedestrian path choiceobservations in an urban street network where the green view index wasextracted as a visual street quality from Google Street View images. The resultrevealed that pedestrians locally perceive and react to the visual streetquality, rather than they have the pre-trip global perception on it.Furthermore, the simulation results using the estimated models suggested theimportance of location selection of interventions when policy-relatedattributes are only locally perceived by travelers.

Global path preference and local response: A reward decomposition  approach for network path choice analysis in the presence of locally  perceived attributes