Results 1  10
of
80
Mapmatching for lowsamplingrate GPS trajectories
 In Proc. ACM SIGSPATIAL GIS
, 2009
"... Mapmatching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental preprocessing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount o ..."
Abstract

Cited by 52 (6 self)
 Add to MetaCart
Mapmatching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental preprocessing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of lowsamplingrate (e.g., one point every 25 minutes) GPS trajectories. Unfortunately, most current mapmatching approaches only deal with highsamplingrate (typically one point every 1030s) GPS data, and become less effective for lowsamplingrate points as the uncertainty in data increases. In this paper, we propose a novel global mapmatching algorithm called STMatching for lowsamplingrate GPS trajectories. STMatching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatiotemporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare STMatching with the incremental algorithm and AverageFréchetDistance (AFD) based global mapmatching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our STmatching algorithm significantly outperform incremental algorithm in terms of matching accuracy for lowsampling trajectories. Meanwhile, when compared with AFDbased global algorithm, STMatching also improves accuracy as well as running time.
The V*Diagram: A QueryDependent Approach to Moving KNN Queries
, 2008
"... The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database ..."
Abstract

Cited by 34 (9 self)
 Add to MetaCart
(Show Context)
The moving k nearest neighbor (MkNN) query finds the k nearest neighbors of a moving query point continuously. The high potential of reducing the query processing cost as well as the large spectrum of associated applications have attracted considerable attention to this query type from the database community. This paper presents an incremental saferegionbased technique for answering MkNN queries, called the V*Diagram. In general, a safe region is a set of points where the query point can move without changing the query answer. Traditional saferegion approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the current knowledge of the query point and the search space in addition to the data objects. As a result, the V*Diagram has much smaller IO and computation costs than existing methods. The experimental results show that the V*Diagram outperforms the best existing technique by two orders of magnitude.
Monitoring path nearest neighbor in road networks
 In SIGMOD
, 2009
"... This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the kNN with respect to the shortest path connecting the destination and the user’s current location, and thus ..."
Abstract

Cited by 25 (3 self)
 Add to MetaCart
(Show Context)
This paper addresses the problem of monitoring the k nearest neighbors to a dynamically changing path in road networks. Given a destination where a user is going to, this new query returns the kNN with respect to the shortest path connecting the destination and the user’s current location, and thus provides a list of nearest candidates for reference by considering the whole coming journey. We name this query the kPath Nearest Neighbor query (kPNN). As the user is moving and may not always follow the shortest path, the query path keeps changing. The challenge of monitoring the kPNN for an arbitrarily moving user is to dynamically determine the update locations and then refresh the kPNN efficiently. We propose a threephase Bestfirst Network Expansion (BNE) algorithm for monitoring the kPNN and the corresponding shortest path. In the searching phase, the BNE finds the shortest path to the destination, during which a candidate set that guarantees to include the kPNN is generated at the same time. Then in the verification phase, a heuristic algorithm runs for examining candidates’ exact distances to the query path, and it achieves significant reduction in the number of visited nodes. The monitoring phase deals with computing update locations as well as refreshing the kPNN in different user movements. Since determining the network distance is a costly process, an expansion tree and the candidate set are carefully maintained by the BNE algorithm, which can provide efficient update on the shortest path and the kPNN results. Finally, we conduct extensive experiments on real road networks and show that our methods achieve satisfactory performance.
Path Oracles for Spatial Networks
, 2009
"... The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A line ..."
Abstract

Cited by 22 (6 self)
 Add to MetaCart
(Show Context)
The advent of locationbased services has led to an increased demand for performing operations on spatial networks in real time. The challenge lies in being able to cast operations on spatial networks in terms of relational operators so that they can be performed in the context of a database. A linearsized construct termed a path oracle is introduced that compactly encodes the n2 shortest paths between every pair of vertices in a spatial network having n vertices thereby reducing each of the paths to a single tuple in a relational database and enables finding shortest paths by repeated application of a single SQL SELECT operator. The construction of the path oracle is based on the observed coherence between the spatial positions of both source and destination vertices and the shortest paths between them which facilitates the aggregation of source and destination vertices into groups that share common vertices or edges on the shortest paths between them. With the aid of the WellSeparated Pair (WSP) technique, which has been applied to spatial networks using the network distance measure, a path oracle is proposed that takes O(sdn) space, where s is empirically estimated to be around 12 for road networks, but that can retrieve an intermediate link in a shortest path in O(logn) time using a Btree. An additional construct termed the pathdistance oracle of size O(n · max(sd, 1 d ε)) (empirically (n · max(122, 2.5 2 ε))) is proposed that can retrieve an intermediate vertex as well as an εapproximation of the network distances in O(logn) time using a Btree. Experimental results indicate that the proposed oracles are linear in n which means that they are scalable and can enable complicated query processing scenarios on massive spatial network datasets.
Efficiently Indexing Shortest Paths by Exploiting Symmetry in Graphs
 In EDBT 2009
"... Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries onthefly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest path ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
(Show Context)
Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries onthefly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N 2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadthfirst
Distance Oracles for Spatial Networks
"... Abstract — The popularity of locationbased services and the need to do realtime processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge is that these operations involve the computation o ..."
Abstract

Cited by 12 (5 self)
 Add to MetaCart
(Show Context)
Abstract — The popularity of locationbased services and the need to do realtime processing on them has led to an interest in performing queries on transportation networks, such as finding shortest paths and finding nearest neighbors. The challenge is that these operations involve the computation of distance along a spatial network rather than “as the crow flies. ” In many applications an estimate of the distance is sufficient, which can be achieved by use of an oracle. An approximate distance oracle is proposed for spatial networks that exploits the coherence between the spatial position of vertices and the network distance between them. Using this observation, a distance oracle is introduced that is able to obtain the εapproximate network distance between two vertices of the spatial network. The network distance between every pair of vertices in the spatial network is efficiently represented by adapting the wellseparated pair technique to spatial networks. Initially, use is made of an εapproximate distance oracle of size O ( n εd) that is capable of retrieving the approximate network distance in O(logn) time using a Btree. The retrieval time can be theoretically reduced to O(1) time by proposing another εapproximate distance oracle of size O ( nlogn εd) that uses a hash table. Experimental results indicate that the proposed technique is scalable and can be applied to sufficiently large road networks. A 10%approximate oracle (ε = 0.1) on a large network yielded an average error of 0.9 % with 90 % of the answers making an error of 2 % or less and an average retrieval time of 68µ seconds. Finally, a strategy for the integration of the distance oracle into any relational database system as well as using it to perform a variety of spatial queries such as region search, knearest neighbor search, and spatial joins on spatial networks is discussed. I.
Efficient continuous nearest neighbor query in spatial networks using euclidean restriction
 In SSTD
, 2009
"... Abstract. In this paper, we propose an efficient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN contin ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
(Show Context)
Abstract. In this paper, we propose an efficient method to answer continuous k nearest neighbor (CkNN) queries in spatial networks. Assuming a moving query object and a set of data objects that make frequent and arbitrary moves on a spatial network with dynamically changing edge weights, CkNN continuously monitors the nearest (in network distance) neighboring objects to the query. Previous CkNN methods are inefficient and, hence, fail to scale in large networks with numerous data objects because: 1) they heavily rely on Dijkstrabased blind expansion for network distance computation that incurs excessively redundant cost particularly in large networks, and 2) they blindly map all object location updates to the network disregarding whether the updates are relevant to the CkNN query result. With our method, termed ERCkNN (short for Euclidian Restriction based CkNN), we utilize ER to address both of these shortcomings. Specifically, with ER we enable 1) guided search (rather than blind expansion) for efficient network distance calculation, and 2) localized mapping (rather than blind mapping) to avoid the intolerable cost of redundant object location mapping. We demonstrate the efficiency of ERCkNN via extensive experimental evaluations with real world datasets consisting of a variety of large spatial networks with numerous moving objects. 1
Efficient KNearest Neighbor Search in TimeDependent Spatial Networks
, 2010
"... The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., traveltime) of each edge in the spatial network is constant. However, in realworld, edgeweights a ..."
Abstract

Cited by 10 (1 self)
 Add to MetaCart
(Show Context)
The class of k Nearest Neighbor (kNN) queries in spatial networks has been widely studied in the literature. All existing approaches for kNN search in spatial networks assume that the weight (e.g., traveltime) of each edge in the spatial network is constant. However, in realworld, edgeweights are timedependent and vary significantly in short durations, hence invalidating the existing solutions. In this paper, we study the problem of kNN search in timedependent spatial networks where the weight of each edge is a function of time. We propose two novel indexing schemes, namely Tight Network Index (T NI) and Loose Network Index (LNI) to minimize the number of candidate nearest neighbor objects and, hence, reduce the invocation of the expensive fastestpath computation in timedependent spatial networks. We demonstrate the efficiency of our proposed solution via experimental evaluations with realworld datasets, including a variety of large spatial networks with real trafficdata.
Shortest Path and Distance Queries on Road Networks: An Experimental Evaluation
"... Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The stateoftheart solutions for the problem can be divided into two categories: spatialcoherencebased methods and ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
Computing the shortest path between two given locations in a road network is an important problem that finds applications in various map services and commercial navigation products. The stateoftheart solutions for the problem can be divided into two categories: spatialcoherencebased methods and verteximportancebased approaches. The two categories of techniques, however, have not been compared systematically under the same experimental framework, as they were developed from two independent lines of research that do not refer to each other. This renders it difficult for a practitioner to decide which technique should be adopted for a specific application. Furthermore, the experimental evaluation of the existing techniques, as presented in previous work, falls short in several aspects. Some methods were tested only on small road networks with up to one hundred thousand vertices; some approaches were evaluated using distance queries (instead of shortest path queries), namely, queries that ask only for the length of the shortest path; a stateoftheart technique was examined based on a faulty implementation that led to incorrect query results. To address the above issues, this paper presents a comprehensive comparison of the most advanced spatialcoherencebased and verteximportancebased approaches. Using a variety of real road networks with up to twenty million vertices, we evaluated each technique in terms of its preprocessing time, space consumption, and query efficiency (for both shortest path and distance queries). Our experimental results reveal the characteristics of different techniques, based on which we provide guidelines on selecting appropriate methods for various scenarios. 1.