Trajectory based optimal segment computation in road network databases

Finding a location for a new facility s.t. the facility attracts the maximal number of customers is a challenging problem. Existing studies either model customers as static sites and thus do not consider customer movement, or they focus on theoretical aspects and do not provide solutions that are shown empirically to be scalable. Given a road network, a set of existing facilities, and a collection of customer route traversals, an optimal segment query returns the optimal road network segment(s) for a new facility. We propose a practical framework for computing this query, where each route traversal is assigned a score that is distributed among the road segments covered by the route according to a score distribution model. We propose two algorithms that adopt different approaches to computing the query. Empirical studies with real data sets demonstrate that the algorithms are capable of offering high performance in realistic settings.

[1]  Stefan Langerman,et al.  Facility location problems in the plane based on reverse nearest neighbor queries , 2010, Eur. J. Oper. Res..

[2]  Justo Puerto,et al.  Location Theory - A Unified Approach , 2005 .

[3]  M. J. Hodgson The location of public facilities intermediate to the journey to work , 1981 .

[4]  Yang Du,et al.  The Optimal-Location Query , 2005, SSTD.

[5]  Oded Berman,et al.  Efficient solution approaches for a discrete multi-facility competitive interaction model , 2009, Ann. Oper. Res..

[6]  Obed Berman The maximizing market size discretionary facility location problem with congestion , 1995 .

[7]  Gaston H. Gonnet,et al.  Analytic variations on quadtrees , 2005, Algorithmica.

[8]  Philip S. Yu,et al.  Efficient Method for Maximizing Bichromatic Reverse Nearest Neighbor , 2009, Proc. VLDB Endow..

[9]  Oded Berman,et al.  Locating flow-intercepting facilities: New approaches and results , 1995, Ann. Oper. Res..

[10]  O. Berman Deterministic flow-demand location problems , 1997 .

[11]  I. Averbakh,et al.  Locating flow-capturing units on a network with multi-counting and diminishing returns to scale , 1996 .

[12]  M. J. Hodgson A Flow-Capturing Location-Allocation Model , 2010 .

[13]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[14]  Gert W. Wolf,et al.  Facility location: concepts, models, algorithms and case studies. Series: Contributions to Management Science , 2011, Int. J. Geogr. Inf. Sci..

[15]  Yufei Tao,et al.  Progressive computation of the min-dist optimal-location query , 2006, VLDB.

[16]  Christian S. Jensen,et al.  Map Matching for Intelligent Speed Adaptation , 2007 .

[17]  W. Marsden I and J , 2012 .

[18]  Feifei Li,et al.  Optimal location queries in road network databases , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[19]  Adam Meyerson,et al.  Online facility location , 2001, Proceedings 2001 IEEE International Conference on Cluster Computing.

[20]  Dimitris Fotakis Incremental algorithms for Facility Location and k-Median , 2006, Theor. Comput. Sci..

[21]  Maged Dessouky,et al.  A modeling framework for facility location of medical services for large-scale emergencies , 2007 .

[22]  Oded Berman,et al.  Locating Discretionary Service Facilities, II: Maximizing Market Size, Minimizing Inconvenience , 1995, Oper. Res..

[23]  Maged M. Dessouky,et al.  Solution approaches for facility location of medical supplies for large-scale emergencies , 2007, Comput. Ind. Eng..

[24]  Harry Lahrmann,et al.  Spar paa farten: an Intelligent Speed Adaption project in Denmark based on Pay as you Drive Principles , 2007 .

[25]  Wei Wu,et al.  MaxFirst for MaxBRkNN , 2011, 2011 IEEE 27th International Conference on Data Engineering.

[26]  Stefan Langerman,et al.  Reverse facility location problems , 2005, CCCG.