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A Study of Pre-Processing Technique for Map-Matching Schemes of GPS-Enabled Vehicles

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A Study of Pre-Processing Technique for Map-Matching Schemes of GPS-Enabled Vehicles International Journal of Computing and Digital Systems ISSN (2210-142X) Int. J. Com. Dig. Sys. 6, No.1 (Jan-2017) http://dx.doi.org/10.12785/ijcds/060104 A Study of Pre-Processing Technique for Map-Matching Schemes of GPS-Enabled Vehicles Aftab Ahmed Chandio1, 2, Fan Zhang2, Zubair Ahmed Kalhoro1, Imtiaz Ali Korejo1 and Muhammad Saleem Chandio1 1 Institute of Mathematics & Computer Science, University of Sindh, Jamshoro 70680, Pakistan 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China Received 23 Sep. 2016, Revised 15 Nov. 2016, Accepted 3 Dec. 2016, Published 1 Jan. 2017 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. Keywords: Map-Matching, GPS, splitting a road network, Pre-processing, SSSP, Bulk synchronize parallel,BSP i.e., more than 120 seconds time interval between GPS 1. INTRODUCTION points. The methods in the third category performs with A fundamental pre-process and useful service in the statistical methods such as Bayesian classifies, Location-Based Services (LBS) is a map-matching Hidden Markov model [6], Kalman filter and cubic process [1]. Specifically, the map-matching process is spline interpolation [7] to handle GPS measurement required in LBS when aligning the locations of Global errors. In aspect to the fast response time, the algorithms Positioning System (GPS) with the road network graph follow local/incremental method are very fast, while the on a given digital map. Several applications for GPS- algorithms follow global method are slow. More enabled vehicles such as moving objects management concisely, in aspect to the accuracy, the algorithms and driving directions use map-matching process as a follow local/incremental method produce low accurate fundamental step. More specifically, the map-matching results; while the algorithms follow global method process is categorized into three categories: (a) local or provides high accurate results. incremental method [2, 3], (b) global method [1] [4, 5] and (c) statistical method [6, 7]. In the first category, the Several LBS applications are deployed online that algorithms effort to discover the local match of geometry need a fast result. A map-matching technique therefore is wherein the small portion of the space close to the GPS needed which must be deal the low-sampling GPS data point is considered. This approach provides the better and produce fast result. In practice, we found in the well- results in aspect to accuracy with high sampling known map-matching algorithms that the shortest path frequency of the trajectory i.e., 2-5 seconds time interval queries are most frequently used process and most time between GPS points. In the second category, the consuming part of the algorithms. algorithms consider the complete trajectory for map- In this paper, we propose a pre-processing technique matching with a digital road network. The global for map-matching process, which is approached as methods provide the better results in aspect to the follows: (a) The pre-processing technique in the accuracy with low-sampling frequency of the trajectory developed model is based on splitting the road network E-mail: [email protected], [email protected], [email protected], [email protected], [email protected] http://journals.uob.edu.bh 38 Aftab A. Chandio, et. al.: A Study of Pre-Processing Technique for Map-Matching … graph for pre-processing the Single Source Shortest Path Main difference between current state-of-the-art and (SSSP) in Bulk Synchronize Parallel (BSP) processing in in our work is (a) we pre-compute shortest path queries the Hadoop environment, i.e., clouds. (b) In the between all nodes while previous work pre-compute the experimental work, the proposed pre-processing query between nodes and objects specifically. technique is incorporated with the map-matching Furthermore, (b) our system pre-compute the results can schemes (i.e., Spatial and Temporal MapMatching, ST- be used in different applications which need to perform MM [1] and Location-Based MapMatching, LB-MM [8] shortest path query rather in one application they are found in the literature review) providing better built for. Common types of queries can also use these performance in aspect to overall response time with results to accelerate the query processing time. slightly effects on the accuracy. Moreover, (c) we eliminate storage overheads as much as possible through splitting road network to small grids The rest of the paper is organized as follows. Section and finding redundant data in pre-computed results. (d) 2 addresses the related work and Section 3 presents the We use more efficient technique to pre-compute results, proposed pre-processing model. In the sequel, in Section which perform on parallel computing. This technique is 4, we provide the experimental results and discussions, not only beneficial for pre-compute results at once but while in Section 5, we show the conclusion of the paper. also it is more helpful when road network need to modify 2. RELATED WORK then only particular region of the road network will be computed by parallel computers. Processing of Shortest Path Queries (SPQs) on spatial road network database has been focused recently in the 3. THE PROPOSED PRE-PROCESSING MODEL state-of-the-art in order to reduce memory and In our proposed model, we split a road network graph computation overheads. Wherein spatial road network into small regions/partitions. The purpose of splitting database, roads are modeled in the graph, where road junctions and road segments are nodes and edges, road network graph into small partitions is to store the respectively. To propose new model of road network is results into files in such a way that the required partition fundamental problem in Geographic Information System can be loaded into the memory. This approach does not (GIS) databases for LBS applications [9-13]. The best only provide an advantage of low-cost storage and map-matching approaches are highly depended on the efficient query processing but also road network could be accuracy and correctness of underlying road networks well-managed in case of new roads updated by [11]. For instance, Hu et. al. [9] proposed an efficient transportation department in the road network database. index to discretize the distances between objects and In the case of updating road network database, only network nodes into categories, called distance signature specific partition of the graph would be computed the for distance computation and query processing over long shortest path distances and spatial information. distances. Kolahdouzan and Shahabi [10] proposed: “a novel approach to evaluate k-Nearest Neighbor (kNN) A pseudo-code of the proposed technique for queries in spatial network databases using first order partitioning a given road network graph is depicted in Voronoi diagram based on partitioning a large network to Algorithm 1. In this paper, we use the important small Voronoi regions, and then pre-computing distances notations and descriptions which are shown in the Table both within and across the regions”. Authors claimed that II. the process of pre-computing distances and partitioning a TABLE II. NOMENCLATURE large network to small regions can save storage and Notation Description computation cost. Author in [12] proposed quick map- SPQ Shortest Path Query matching algorithm based on structure of road network, BSP Bulk Synchronous Parallel on which road network is divided into two levels and 퐺(푉, 퐸) A digital road network graph partitions into small grids (mesh). Author claimed that 푉 Vertexes of a graph old road network model is not sufficient need to be 퐸 Edges of a graph adapted to the requirement for the efficient map- 퐾 A number of sub-graphs (퐺퐾) matching. 푘 A given number required for partitions 푚푛퐶 and Smallest (minimum) and largest (maximum) value Since SPQs (i.e., Dijkstra’s algorithm) is only 푚푎푥퐶 of graph’s coordination (i.e., longitude and latitude) efficient for shortest distances [9], and to minimize the 푣 Total number of vertexes in a graph searching and storage cost, the optimal category 푝푎푟푡푎푙퐶 Partial coordination of a partition partitions of spatial road network is needed. By our 푇퐻 Threshold value to adjust the partition coordination practical approach and comparative analysis study, we 푎푝푝푟표푥푉 Approximate number of vertexes in a partition show that the proposed technique of partitions in road Total number of vertexes counted into the space network is efficient and robust for SPQs in map- 푡표푡푎푙푉 covered 푚푛퐶 and 푚푎푥퐶 coordination of the partition matching. 푙표푤퐶and Coordinate values used for extra space covered in ℎℎ퐶 order to connect the neighbor partition. http://journals.uob.edu.bh Int. J. Com. Dig. Sys. 6, No.1, 37-43 (Jan-2017) 39 Specifically, this algorithm takes an input of a road 6: set 푝푎푟푡푡표푛 = 1 푝푎푟푡푡표푛 ≤ 푘 network graph 퐺(푉, 퐸) and a number 푘 which is given 7: while do 8: set 푡표푡푎푙푉 = 푉 vertexes between left_border for producing 퐾 number of partitions formulated in sub- and right_border graphs.
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