No abstract

Our aim in this paper is to build and test a model which classifies and identifies pedestrian shopping behaviour in a shopping centre by using temporal and spatial choice heuristics. In particular, the temporal local-distance-minimising, total-distance-minimising, and global-distance-minimising heuristic choice rules and spatial nearest-destination-oriented, farthest-destination-oriented, and intermediate-destination-oriented choice rules are combined to classify and identify the stop sequences and route choices of shopping pedestrians. First, several linear networks with a single entry node and a few stop nodes are investigated. For these networks, the global-distance-minimising and spatial choice heuristics classify and identify the sequences 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-oriented heuristic is suggested to improve the identification ability of the model. This choice rule suggests that shopping pedestrians will never leave the attractive shopping streets before completing their shopping. The model is then applied to empirical data of pedestrian shopping behaviour in Veldhoven City Centre in The Netherlands. The findings of this application suggest that the model based on choice heuristics might be useful to classify and identify the sequences of stops and route choice behaviour of shopping pedestrians in a shopping centre.

This paper contains a report on the application of a spatial interaction model to the prediction of shopping pedestrian flow along the streets of a small district with a shopping center. A model where the shopping trip distributions are determined using a partially doubly-constrained type of model is proposed. This model uses only the cross-sectional data of pedestrian traffic volume and is tested using data from Meinohama district (Fukuoka City, Japan).

No abstract

No abstract