the class of k nearest neighbor queries in spatial networks has been studied in the literature . existing approaches for k nn search in spatial networks assume that the weight of each edge in the spatial network is constant . however , real - world edge - weights are time - dependent and vary significantly in short durations , hence invalidating the existing solutions . the problem of k nn search in time - dependent spatial networks , where the weight of each edge is a function of time , is addressed herein . two indexing schemes are proposed to minimize the number of candidate nearest neighbor objects and reduce the invocation of the expensive fastest - path computation in time - dependent spatial networks . we demonstrate the efficiency of our proposed solution via experimental evaluations with real - world data - sets , including a variety of large spatial networks with real traffic - data .