Classifying pedestrian shopping behaviour according to implied heuristic choice rules

Our aim in this paper is to build and test a model which classifies and ide ntifies pedestrian shopping behaviour in a shopping centre by using tempora l and spatial choice heuristics. In particular, the temporal local-distance -minimising, total-distance-minimising, and global-distance-minimising heur istic choice rules and spatial nearest-destination-oriented, farthest-desti nation-oriented, and intermediate-destination-oriented choice rules are com bined to classify and identify the stop sequences and route choices of shop ping pedestrians. First, several linear networks with a single entry node a nd a few stop nodes are investigated. For these networks, the global-distan ce-minimising and spatial choice heuristics classify and identify the seque nces of stops very well. Although the local-distance-minimising choice rule identifies pedestrian route choice quite well, another heuristic is needed to improve the identification. In this paper a new, attractive-street-orie nted heuristic is suggested to improve the identification ability of the mo del. This choice rule suggests that shopping pedestrians will never leave t he attractive shopping streets before completing their shopping. The model is then applied to empirical data of pedestrian shopping behaviour in Veldh oven City Centre in The Netherlands. The findings of this application sugge st that the model based on choice heuristics might be useful to classify an d identify the sequences of stops and route choice behaviour of shopping pe destrians in a shopping centre.