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Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

机译:开发用于智能交通系统的增强的基于权重的拓扑图匹配算法

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摘要

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services. © 2009 Elsevier Ltd. All rights reserved.
机译:地图匹配(MM)算法将来自全球定位系统(或许多其他定位传感器)的定位数据与空间路线图集成在一起,以识别用户(或车辆)行驶的路段以及该段上的位置。在由几何,拓扑,概率和高级组成的MM算法家族中,拓扑MM(tMM)算法相对简单,便捷,快速,可以实时实现。因此,tMM算法被用于许多工业制造的导航设备中。但是,相对于高级MM算法,现有的tMM算法具有许多局限性,影响其性能。本文证明,通过解决这些问题,可以显着提高tMM算法的性能。本文介绍了一种基于权重的增强型tMM算法的开发,其中使用优化技术从现实世界的现场数据确定权重。引入了两个新的权重来限制交界处的转弯限制和链路连接,以改善匹配的性能,尤其是在交界处。为初始地图匹配过程开发了一个新程序。引入了两个一致性检查以最大程度地减少不匹配。使用来自密集市区和郊区的现场数据测试了增强的地图匹配算法。该算法分别正确识别了96.8%和95.93%的链接,分别用于定位在伦敦市中心和华盛顿特区市区收集的数据。以伦敦西部的郊区为例,该算法以96.71%的正确链路识别成功,其水平精度为9.81 m(2σ)。这优于大多数现有的拓扑MM算法,并有可能支持许多智能运输系统(ITS)服务的导航模块。 ©2009 ElsevierLtd。保留所有权利。

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