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.
Highlights
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
K =i,j where n is the number of destinations, k is the area index of k th nearest neighbour to the area i, Pk is the population of the area k, and I dij > dik is the indicator variable which takes the value of 1 when dij > dik, and 0 for otherwise
Several studies tackled the assumption using other criteria that substituted for d istance[21,49,50] and some parameters reflecting opportunities perceived by their geographical extent[49,51]
Summary
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. Such classical definitions of intervening opportunities have often failed to predict realistic flow volumes, for short-distance flows. Whereas the distance decay is derived from empirical laws[2,5,6], the concept of intervening opportunities is rooted in spatial search behaviours of destinations for a moving individual This is described by Schneider’s model, which assumes that when an individual searches for a destination, the individual will select an area that is closest to the origin among possible candidates having higher opportunity benefits than those of the origin[7,10]. Despite the high predictability for a wide range of flows including commuting, migration, and commodity in the United States, most of the later studies reported poor agreement between predicted and observed flows in different countries, which indicated that the radiation model could not universally predict human mobilities[31,32,33,34,35,36,37,38,39]
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