A general map matching algorithm for transport telematics applications

By Mohammed A. Quddus, Washington Yotto Ochieng, Lin Zhao, Robert B. Noland. 2003

414 citations recorded by the source corpus.

This paper describes a map-matching algorithm designed to support the navigational functions of a real-time vehicle performance and emissions monitoring system currently under development, and other transport telematics applications. The algorithm is used together with the outputs of an extended Kalman filter formulation for the integration of GPS and dead reckoning data, and a spatial digital database of the road network, to provide continuous, accurate and reliable vehicle location on a given road segment. This is irrespective of the constraints of the operational environment, thus alleviating outage and accuracy problems associated with the use of stand-alone location sensors. The map-matching algorithm has been tested using real field data and has been found to be superior to existing algorithms, particularly in how it performs at road intersections.

Subfields: Building and Construction, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cbfa6585ef426c6f9a3cbcf3cae2a146adb246ed

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In Building and Construction

In Computer Networks and Communications

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Citations in the corpus

On Map-Matching Vehicle Tracking Data (2005, methodology): “Work in the area of map-matching vehicle positions exists towards augmenting GPS positioning with other methods such as dead reckoning to reduce the measurement error and to achieve better map-matching results (cf. [15]).”
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data (2011, background): “Researchers started systematic studies after the introduction of the GPS system to civilian applications in the 1990s [33], [42].”
In-Car Positioning and Navigation Technologies—A Survey (2009, background): “See [19], [74], and [89]–[94] for various weighting and combining approaches such as the belief theory, fuzzy network, and state machines.”