A weight-based map matching algorithm using minimum input variables for urban road networks

Abstract

Global Positioning System (GPS) has found its application in the field of Intelligent Transportation Systems (ITS) in fleet management, prediction of arrival of public transport vehicles, route identification, route navigation, and many other location based services. Map matching algorithms integrate the positioning data received from the GPS, with digital road networks on existing maps. This includes minimizing the error in locating the vehicle on a route, identifying the correct road segment and locating the vehicle's correct position on the road network. This paper presents a weight based map matching algorithm, which can be applied in real-time, for complex urban road networks. The parameters considered in the evaluation of the candidate segments are distance, direction difference and connectivity with the previously identified segment. A comparison between these parameters for the two best road segments was analyzed to identify probable cases of ambiguity in selection of the best segment and a solution to overcome wrong assignments was included in the algorithm. The most noticeable feature about this algorithm is the high accuracy of 96.55% for segment assignment using minimum input variables of latitude and longitude of the vehicles.