01042537

Component

Map matching algorithms integrate positioning data with spatial road network data to identify the correct link on which a vehicle is traveling and to determine the location of a vehicle on a link. A map matching algorithm could be used as a key component to improve the navigation functions of intelligent transport systems (ITS) that require locational data of a relatively high quality. The required horizontal positioning accuracy of such ITS applications is in the range of 1m to 40m (95%). A number of map matching algorithms have been developed by researchers around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, Kalman filter, fuzzy logic, and belief theory. The performance of these algorithms has improved over the years due to the application of advanced techniques in the map matching processes and to the improvements of the quality of both positioning data and spatial road network data over time. However, these algorithms are not always capable of supporting the navigation functions that require a high positioning accuracy, specifically within dense urban areas. This suggests that further improvements to map matching techniques are essential. Therefore, it is very important that the constraints and limitations of existing map matching algorithms are identified to facilitate further enhancement of the algorithms. The objectives of this paper are then to uncover the constraints and limitations of current map matching algorithms by an in-depth literature review and to suggest possible solutions where feasible for further improvements. This paper also reports the potential impacts of the forthcoming European Galileo system and the EGNOS (European Geostationary Overlay Service) on the performance of map matching algorithms.

01042056

07-2216

English

24p

2007

Transportation Research Board 86th Annual Meeting

CD-ROM

Figures (2) ; References; Tables (1)

Algorithms; Applications; Automatic vehicle identification; Digital maps; Fuzzy logic; Intelligent transportation systems; Kalman filtering; Maps; Networks; Research; State of the art; Urban areas

Highways; Operations and Traffic Management; Planning and Forecasting; Research; I72: Traffic and Transport Planning

Transportation Research Board Annual Meeting 2007 Paper #07-2216

TRIS, TRB

Feb 8 2007 6:54PM