Optimal Time-dependent Sequenced Route Queries in Road Networks

In this paper we present an algorithm for optimal processing of time-dependent sequenced route queries in road networks, i.e., given a road network where the travel time over an edge is time-dependent and a given ordered list of categories of interest, we find the fastest route between an origin and destination that passes through a sequence of points of interest belonging to each of the specified categories of interest. For instance, considering a city road network at a given departure time, one can find the fastest route between one's work and his/her home, passing through a bank, a supermarket and a restaurant, in this order. The main contribution of our work is the consideration of the time dependency of the network, a realistic characteristic of urban road networks, which has not been considered previously when addressing the optimal sequenced route query. Our approach uses the A* search paradigm that is equipped with an admissible heuristic function, thus guaranteed to yield the optimal solution, along with a pruning scheme for further reducing the search space. In order to compare our proposal we extended a previously proposed solution aimed at non-time dependent sequenced route queries, enabling it to deal with the time-dependency. Our experiments using real and synthetic data sets have shown our proposed solution to be up to two orders of magnitude faster than the temporally extended previous solution.Comment: 10 pages, 12 figures To be published as a short paper in the 23rd ACM SIGSPATIA

Processing Time-Dependent Shortest Path Queries Without Pre-computed Speed Information on Road Networks

Shortest path (or least travel time path) identification has been actively studied for direct application to road networks. In addition, the processing of time-dependent shortest-path queries, which use past traffic data to compute the speed variations of road segments, has been investigated in order to incorporate speed variations over time. However, speed information pre-computed from static past traffic data is often invalid because road traffic is inherently dynamic. This paper addresses a new problem in processing a Dynamic Time-Dependent Shortest Path (DTDSP) query, which considers the current road status without assuming pre-determined speed patterns on roads. By dynamically adjusting the speed patterns of roads instead of fixing them based on past traffic data, the recommended paths, which reflect the current road status, are more effective in distributing the road traffic and thus reducing the travel time. To process DTDSP queries, we first propose a Continuous Piece-wise Linear Speed Pattern (CPLSP) model to compute the vehicle speed patterns, which is more flexible and realistic than previously adopted piece-wise constant speed pattern models. Using dynamically computed CPLSPs, we process a DTDSP query in two phases: (1) the least travel time path is found for the query and (2) the speed patterns of the following vehicles, which are affected by the participation of the new vehicle on the road network, are updated. We propose efficient algorithms for finding the least travel time path of a new query (vehicle) and for updating the speed patterns of the existing vehicles. Experiments on real data sets show that our query processing algorithms effectively distribute road traffic, and thus, significantly reduce both global and individual travel times. (C) 2013 Elsevier Inc. All rights reserved.X1179sciescopu