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Book ChapterDOI: 10.1007/978-3-642-37134-9_21

Scalable Method for k Optimal Meeting Points (k-OMP) Computation in the Road Network Databases

25 Mar 2013-pp 277-292
Abstract: Given a set of points Q on a road network G = (V,E), an optimal meeting point (OMP) query offers a point on a road network with the smallest sum-of-distances (SoD) to all the points in Q. For example, a travel agency may issue OMP query to decide the location for a tourist bus to pick up the tourists thus minimizing the total travel cost for tourist. The OMP problem has been well studied in the Euclidean space. The currently available algorithms for solving this problem in the context of road networks are still not efficient for the practical applications and are in-memory algorithms which do not guarantee the scalability for the large road databases. Further, the most of the research work has been carried out around the single point OMP; however, the k-OMP problem on the road network setting is still unexplored. In this paper, we are proposing multiple variants of the scalable external-memory based algorithms for computing the optimal meeting point. There are mainly three variants of the proposed grid based algorithms i.e. Basic Grid based, Hierarchical Grid based and Greedy Centroid based OMP search. Later we used single point OMP as a start point to explore the k points OMP using breadth first search. The I/O optimized spatial grids are loaded from the secondary storage as and when required and hence the I/O complexity is reduced to O(N/B) as opposed to O(N) in the existing methods; where B is the average number of road vertices of the grid block. Extensive experiments are conducted on both real and synthetic datasets.

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Topics: Grid (52%)
Citations
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Open accessJournal ArticleDOI: 10.3969/J.ISSN.1673-5188.2015.01.007
25 Mar 2015-ZTE communications
Abstract: This paper describes the fundamentals of cloud computing and current big-data key technologies. We categorize big-data processing as batch-based, stream-based, graph-based, DAG-based, interactive-based, or visual-based according to the processing technique. We highlight the strengths and weaknesses of various big-data cloud processing techniques in order to help the big-data community select the appropriate processing technique. We also provide big data research challenges and future directions in aspect to transportation management systems.

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Topics: Cloud computing (56%), Big data (54%)

24 Citations


Journal ArticleDOI: 10.1631/FITEE.1600027
Abstract: Smart cities have given a significant impetus to manage traffic and use transport networks in an intelligent way. For the above reason, intelligent transportation systems (ITSs) and location-based services (LBSs) have become an interesting research area over the last years. Due to the rapid increase of data volume within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A large part of data within the transportation domain is produced in the form of Global Positioning System (GPS) data. Such a kind of data is usually infrequent and noisy and achieving the quality of real-time transport applications based on GPS is a difficult task. The map-matching process, which is responsible for the accurate alignment of observed GPS positions onto a road network, plays a pivotal role in many ITS applications. Regarding accuracy, the performance of a map-matching strategy is based on the shortest path between two consecutive observed GPS positions. On the other extreme, processing shortest path queries (SPQs) incurs high computational cost. Current map-matching techniques are approached with a fixed number of parameters, i.e., the number of candidate points (N CP) and error circle radius (ECR), which may lead to uncertainty when identifying road segments and either low-accurate results or a large number of SPQs. Moreover, due to the sampling error, GPS data with a high-sampling period (i.e., less than 10 s) typically contains extraneous datum, which also incurs an extra number of SPQs. Due to the high computation cost incurred by SPQs, current map-matching strategies are not suitable for real-time processing. In this paper, we propose real-time map-matching (called RT-MM), which is a fully adaptive map-matching strategy based on cloud to address the key challenge of SPQs in a map-matching process for real-time GPS trajectories. The evaluation of our approach against state-of-the-art approaches is performed through simulations based on both synthetic and real-world datasets.

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Topics: Map matching (58%), Global Positioning System (58%), Intelligent transportation system (54%) ...read more

6 Citations


Open accessPosted Content
Abstract: Recently, with the advancement of the GPS-enabled cellular technologies, the location-based services (LBS) have gained in popularity. Nowadays, an increasingly larger number of map-based applications enable users to ask a wider variety of queries. Researchers have studied the ride-sharing, the carpooling, the vehicle routing, and the collective travel planning problems extensively in recent years. Collective traveling has the benefit of being environment-friendly by reducing the global travel cost, the greenhouse gas emission, and the energy consumption. In this paper, we introduce several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively. The goal of each problem is to minimize the aggregate cost of the individual travelers' paths and the shared route under various constraints. First, we formulate the problem of determining the optimal pair of end-stops, given a set of queries that originate and terminate near the two prospective end regions. We outline a baseline polynomial-time algorithm and propose a new faster solution - both calculating an exact answer. In our approach, we utilize the path-coherence property of road networks to develop an efficient algorithm. Second, we define the problem of calculating the optimal route and intermediate stops of a vehicle that picks up and drops off passengers en-route, given its start and end stoppages, and a set of path queries from users. We outline an exact solution of both time and space complexities exponential in the number of queries. Then, we propose a novel polynomial-time-and-space heuristic algorithm that performs reasonably well in practice. We also analyze several variants of this problem under different constraints. Last, we perform extensive experiments that demonstrate the efficiency and accuracy of our algorithms.

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4 Citations


Open accessProceedings ArticleDOI: 10.1145/3139958.3140061
07 Nov 2017-
Abstract: The rise of innovative transportation services and the recent breakthrough in the development of autonomous vehicles have stimulated the research on collective travel planning problems such as ride-sharing, carpooling, and on-demand vehicle routing in recent years. In this paper, we introduce several optimization problems to recommend a suitable route and stops of a vehicle, in a road network, for a group of users intending to travel collectively. The goal of each problem is to minimize the aggregate cost of the individual travelers' paths and the shared route under various constraints. First, we introduce the optimal end-stops (OES) query that finds a pair of pick-up-and-drop-off locations such that the sum of the distance between these locations and the total distance traveled by the travelers from their start locations to the pick-up location and from the drop-off location to their end locations is minimized. We propose a polynomial-time fast algorithm for the OES query by utilizing the path-coherence property of road networks. Second, we formulate the optimal route and intermediate stops (ORIS) query to find a set of intermediate stops for the vehicle such that the sum of the total distance traveled by the vehicle and the total distance traveled by the travelers from their start locations to one of the stops and to their end locations from one of the stops is minimized. We propose a novel near-optimal polynomial-time-and-space heuristic algorithm for the ORIS query that performs reasonably well in practice. We also analyze several variants of this problem. Finally, we perform extensive experiments to demonstrate the efficiency and efficacy of our algorithms.

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Topics: Vehicle routing problem (54%)

3 Citations


Open accessJournal ArticleDOI: 10.12785/IJCDS/060104
Abstract: This study towards the Map-Matching process that is useful to align a location of Global Positioning System (GPS) of vehicles on the digital road networks. Today’s GPS-enabled vehicles in developed countries generate a big volume of GPS data. On the other hand, the development of new roads in the city enables the road network very complex and difficult to match the vehicles’ location. So therefore, different techniques (i.e., pre-processing techniques) may be applied before the map-matching process is a recent concern of the Intelligent Transport System (ITS) research community. In this paper, we introduce the pre-processing technique; splitting the road network graph and processing the Single Source Shortest Path (SSSP) in synchronize parallel processing in the Hadoop environment. The proposed technique enables the map-matching schemes efficient to align the GPS points on the digital road networks. In the experimental work, the results of the map-matching schemes (i.e., found in the literature review) incorporated with our proposed pre-processing technique shows better performance in aspect to the response time.

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Topics: Map matching (66%), Global Positioning System (57%)

2 Citations


References
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Open access
01 Jan 1985-
Abstract: From the reviews: "This book offers a coherent treatment, at the graduate textbook level, of the field that has come to be known in the last decade or so as computational geometry...The book is well organized and lucidly written; a timely contribution by two founders of the field. It clearly demonstrates that computational geometry in the plane is now a fairly well-understood branch of computer science and mathematics. It also points the way to the solution of the more challenging problems in dimensions higher than two."

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Topics: Computational geometry (61%), Plane (geometry) (55%), Proximity problems (53%) ...read more

6,443 Citations


Open accessProceedings ArticleDOI: 10.1109/ICDE.2004.1320006
30 Mar 2004-
Abstract: Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q/sub 1/ q/sub 2/ and q/sub 3/ that want to find a meeting point (e.g., a restaurant); the corresponding query returns the data point p that minimizes the sum of Euclidean distances |pq/sub i/| for 1/spl les/i/spl les/3. Assuming that Q fits in memory and P is indexed by an R-tree, we propose several algorithms for finding the group nearest neighbors efficiently. As a second step, we extend our techniques for situations where Q cannot fit in memory, covering both indexed and nonindexed query points. An experimental evaluation identifies the best alternative based on the data and query properties.

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  • Figure 5.5: Cost vs. size of MBR of Q (k=8, P=PP, Q=TS)
    Figure 5.5: Cost vs. size of MBR of Q (k=8, P=PP, Q=TS)
  • Figure 5.7: Cost vs. overlap area (k=8, P=PP, Q=TS)
    Figure 5.7: Cost vs. overlap area (k=8, P=PP, Q=TS)
  • Figure 3.5: Example of heuristic 2
    Figure 3.5: Example of heuristic 2
  • Figure 5.6: Cost vs. overlap area (k=8, P=TS, Q=PP)
    Figure 5.6: Cost vs. overlap area (k=8, P=TS, Q=PP)
  • Figure 3.1: Example of a GNN query
    Figure 3.1: Example of a GNN query
  • + 12

Topics: Fixed-radius near neighbors (61%), Nearest neighbor graph (60%), Nearest neighbor search (57%) ...read more

265 Citations


Open accessProceedings ArticleDOI: 10.5555/1182635.1164183
Donghui Zhang1, Yang Du1, Tian Xia1, Yufei Tao2Institutions (2)
01 Sep 2006-
Abstract: This paper proposes and solves the min-dist optimal-location query in spatial databases. Given a set S of sites, a set O of weighted objects, and a spatial region Q, the min-dist optimal-location query returns a location in Q which, if a new site is built there, minimizes the average distance from each object to its closest site. This query can help a franchise (e.g. McDonald's) decide where to put a new store in order to maximize the benefit to its customers. To solve this problem is challenging, for there are theoretically infinite number of locations in Q, all of which could be candidates. This paper first provides a theorem that limits the number of candidate locations without losing the power to find exact answers. Then it provides a progressive algorithm that quickly suggests a location, tells the maximum error it may have, and keeps refining the result. When the algorithm finishes, the exact answer can be found. The intermediate result of early runs can be used to prune the search space for later runs. Crucial to the pruning technique are novel lower-bound estimators. The proposed algorithm, the effect of several optimizations, and the progressiveness are experimentally evaluated.

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Topics: Set (abstract data type) (51%)

115 Citations


Proceedings ArticleDOI: 10.1109/ICDE.2011.5767845
Xiaokui Xiao1, Bin Yao2, Feifei Li2Institutions (2)
11 Apr 2011-
Abstract: Optimal location (OL) queries are a type of spatial queries particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an L p space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their L p distance. Motivated by the deficiency of the existing techniques, this paper presents the first study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We demonstrate the efficiency of our solutions through extensive experiments with real data.

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Topics: Spatial query (64%)

102 Citations


Open accessJournal ArticleDOI: 10.14778/1921071.1921074
Michael N. Rice1, Vassilis J. Tsotras1Institutions (1)
01 Nov 2010-
Abstract: The current widespread use of location-based services and GPS technologies has revived interest in very fast and scalable shortest path queries. We introduce a new shortest path query type in which dynamic constraints may be placed on the allowable set of edges that can appear on a valid shortest path (e.g., dynamically restricting the type of roads or modes of travel which may be considered in a multimodal transportation network). We formalize this problem as a specific variant of formal language constrained shortest path problems, which we call the Kleene Language Constrained Shortest Paths problem. To efficiently support this type of dynamically constrained shortest path query for large-scale datasets, we extend the hierarchical graph indexing technique known as Contraction Hierarchies. Our experimental evaluation using the North American road network dataset (with over 50 million edges) shows an average query speed and search space improvement of over 3 orders of magnitude compared to the naive adaptation of the standard Dijkstra's algorithm to support this query type. We also show an improvement of over 2 orders of magnitude compared to the only previously-existing indexing technique which could solve this problem without additional preprocessing.

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  • Table 1: Graph Label Support for North America
    Table 1: Graph Label Support for North America
  • Table 2: Experiments on the North American Graph Dataset
    Table 2: Experiments on the North American Graph Dataset
  • Figure 5: Effects of Degree Explosion During Construction of the North American Graph Dataset
    Figure 5: Effects of Degree Explosion During Construction of the North American Graph Dataset
  • Table 3: Degree Limit Experiments on the North American Graph Dataset
    Table 3: Degree Limit Experiments on the North American Graph Dataset
  • Figure 7: Counter-example showing lack of minimality when edges are considered in arbitrary order.
    Figure 7: Counter-example showing lack of minimality when edges are considered in arbitrary order.
  • + 7

62 Citations