Kernel-based formulation of intervening opportunities for spatial interaction modelling

Abstract

Understanding spatial interactions such as human mobility has been one of the main analytical themes in geography, spatial economics, and traffic engineering for a long time. The intervening opportunities models, including the radiation model, provide a framework to elucidate spatial interactions generated by an individual’s distance-ordered decision-making process. However, such classical definitions of intervening opportunities have often failed to predict realistic flow volumes, particularly for short-distance flows. To overcome this problem, we have proposed a new formulation of intervening opportunities with a kernel function to introduce a fuzziness in spatial search behaviours of destinations, to develop a new variant of the radiation model. The mobility patterns resulting from the modified radiation model that included kernel-based intervening opportunities outperformed the original radiation model when fitted to four datasets of inter-regional flows.