A novel road network change detection algorithm based on floating car tracking data
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A novel road network change detection algorithm based on floating car tracking data Weidong Fang1 · Rong Hu1
· Xiang Xu1 · Ye Xia2 · Mao-Hsiung Hung1
© Springer Science+Business Media New York 2016
Abstract Road network changes constantly and rapidly. Traditional road network change detection methods cannot meet the needs of modern applications. This paper proposes a novel detection algorithm that uses the floating car tracking data to detect the changes of road network in real-time. With this novel algorithm, an experiment was carried out by adapting the trajectories derived from floating car data with actual date, to generate a part of the current road network of Fuzhou City. The experiment results show that the algorithm has well feasibility and achieves the better quality road network. Keywords Road network detection · Floating car tracking · Car driving trajectory
1 Introduction With the rapid economic development of todays China, the entire citys road network changes constantly everyday, including road construction, road broadening, and road reconstruction. Such changes lead to significant differences of road network between the real world and the electronic map. The problem often lowers the quality of several related applications such as GIS, transportation systems, locationbased services and web mapping. Therefore, how to keep
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Rong Hu [email protected]; [email protected]
1
School of Information Science and Engineering, Fujian University of Technology, No.3 Xueyuan Road, University Town, Fuzhou 350118, Minhou, China
2
College of Ecological Environment and Urban Construction, Fujian University of Technology, No.3, Xueyuan Road, University Town, Fuzhou 350118, Minhou, China
road data of electronic map up-to-date become one of the most important research issues about real-time road network. Currently, such datasets are updated mainly through two kinds of methods: one is map data downsizing update and the other one is network update based on remote sensing images. For example, Kilpelainen updated small scale maps by downsizing the changed information of the large scale maps [1,2]. Ai et al. [3] directly updated by downsizing large-scale ones. Liu Ying proposed a method of using space photography measurement techniques to update electronic maps [4]. Xiao proposed a method of updating electronic maps by extracting road lines based on the use of edge parallel lines [5]. Jia proposed a method of automatically extracting road in SAR (Synthetic Aperture Radar) images to update the maps [6]. However, the two widely used methods have been intense discussed because of low efficiency, long duration, or expensive cost. Automatic map construction and road network refinement have been applied to provide the improved navigation services. Various approaches exist to solve the problem of road network reconstruction. Road networks can be derived from satellite or aerial images by means of image processing techniques [7–10]. For example, Tavakoli et al. group edges by an edge detector into shapes, and these shapes ar
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