An Analysis of Map Matching Algorithm for Recent Intelligent Transport System
Keywords:
Map-matching, Intelligent Transportation System, Vehicle Navigation System, Global Positioning System.Abstract
Map matching is a technique combining electronic map with locating information to obtain the real position of vehicles in a road network. Map matching algorithms can be divided in real-time and offline algorithms. Real-time algorithms associate the position during the recording process to the road network. Offline algorithms are used after the data is recorded and are then matched to the road network. Real-time applications can only calculate based upon the points prior to a given time (as opposed to those of a whole journey), but are intended to be used in 'live' environments. This brings a compromise of performance over accuracy. Offline applications can consider all points and so can tolerate slower performance in favour of accuracy. The MM algorithms integrate positioning data with spatial road network data to identify the correct link on which a vehicle is travelling 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 performance of systems that support the navigation function of intelligent transport systems. A number of map-matching algorithms have been developed by around the world using different techniques such as topological analysis of spatial road network data, probabilistic theory, fuzzy logic, and belief theory. The performances of these algorithms have improved over the years due to the application of advanced techniques in the map matching processes and improvements in the quality of both positioning and spatial road network data. However, these algorithms are not always capable of supporting intelligent transport system applications with high required navigation performance, especially in difficult and complex environments such as dense urban areas. The main objectives of this paper are thus to uncover the constraints and limitations by an in-depth literature review and to recommend ideas to address them. This paper also presents some ideas for monitoring the integrity of map matching algorithms. The map-matching algorithms considered in this paper are generic and do not assume knowledge of ‘future’ information (i.e. based on either cost or time). Clearly, such data would result in relatively simple map-matching algorithms.
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