Easiest-to-reach neighbor search

Studies in cognitive science have shown that people have different optimization goals in mind for route selection: beyond shortest travel distance (or time), criteria such as smallest number of turns or straightest path are often considered. A common query that a traveller in a foreign city may ask is "where is a facility of type X". When multiple facilities of the same type are available in the nearby area, usually not the nearest neighbor but the one which is easiest to find is preferred for giving instructions by locals, especially in an unfamiliar and complex urban environment. This paper studies a novel type of neighboring object selection problem, taking cognitive complexity of navigation into account. The main difficulty arises from incorporating spatial chunking and landmark information into neighbor comparisons. We propose an algorithm based on network expansion, which uses incremental processing of graph transformation that models instruction complexity. Our approach can efficiently find the easiest-to-reach neighbor with the guaranteed smallest navigation cost. Through experimental evaluation on real road networks, the performance of the proposed algorithm is demonstrated under various settings. Our comparison results reveal that on average the travel distance of the easiest-to-reach neighbor is only 19.3% longer than that of the nearest neighbor, whereas the navigation cost can achieve a 64.8% reduction.