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Location-based services are increasingly popular recently. Many applications aim to support a large number of users in metro area (i.e., dense networks). To cope with this challenge, we present a framework that supports location-based services on MOVing objects in road Networks (MOVNet, for short) . MOVNet's dual-index design utilizes an on-disk R-tree to store the network connectivities and an in-memory grid structure to maintain moving object position updates. In this paper, we extend the functionality of MOVNet to support snapshot range queries as well as snapshot k nearest neighbor queries. Given an arbitrary edge in the space, we analyze the minimum and maximum number of grid cells that are possibly affected. We show that the maximum bound can be used in snapshot range query processing to prune the search space. We demonstrate via theoretical analysis and experimental results that MOVNet yields excellent performance with various networks while scaling to a very large number of moving objects.