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80:(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. However, the defects on low accuracy can be reduced due to integration of spatio-temporal proximity and improved weighted circle algorithms.
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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
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Probably the most common use of map-matching is where a traveller has some mobile computer giving him or her directions across a street network. In order to give accurate directions, the device must know exactly where in the street network the user is. A GPS location has positional error though, so
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Topological map matching aligns GPS points with a road network by considering the connectivity and relationships between road segments. It accounts for the structure of the network, path constraints, and the sequence of GPS points to provide accurate and realistic route matching, especially in
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Advanced map-matching algorithms, including those based on Fuzzy Logic, Hidden Markov Models (HMM), and Kalman filters, significantly enhance the accuracy of GPS point location estimation. However, achieving this level of precision often requires substantial processing time.
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Uses for map-matching algorithms range from the immediate and practical, such as applications designed for guiding travellers, to the analytical, such as generating detailed inputs for traffic analysis models and the like.
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picking the nearest street segment and routing from there will likely not work. Instead, the history of locations reported by the GPS can be used to guess a plausible route and infer the current location more accurately.
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where emission probability is a confidence of a point to belong a single segment, and the transition probability is presented as possibility of a point to move from one segment to another within a given time.
60:(network), usually in a sorted list representing the travel of a user or vehicle. Matching observations to a logical model in this way has applications in
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is the problem of how to match recorded geographic coordinates to a logical model of the real world, typically using some form of
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Jafarlou, Minoo; Naderi, Hassan (2022). "Improving Fuzzy-logic based map-matching method with trajectory stay-point detection".
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The earliest approached to solve the map matching problem based on similarity between points' curve and the road curve.
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I17th ACM SIGSPATIAL International
Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2009)
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361:"Map-matching of GPS traces on high-resolution navigation networks using the Multiple Hypothesis Technique (MHT)"
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257:"Real-Time Map Matching: A New Algorithm Integrating Spatio-Temporal Proximity and Improved Weighted Circle"
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routing engines. It is also included in a variety of proprietary programs and mapping/routing applications.
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343:"Efficient map-matching of large GPS data sets - Tests on a speed monitoring experiment in Zurich"
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There are other examples and this subject is still undergoing active research and development.
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Bernstein, David; Kornhauser, Alain (1996-08-01). New Jersey
Institute of Technology (ed.).
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automatically infer turn restrictions based on an analysis of multiple GPS tracks
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Pereira, Francisco Câmara; Costa, Hugo; Pereira, Nuno
Martinho (2009-09-11).
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400:"An Introduction to Map Matching for Personal Navigation Assistants"
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Map matching is implemented in a variety of programs, including the
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Willard (October 2013). "Real-time On and Off Road GPS Tracking".
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192:"An off-line map-matching algorithm for incompletemap databases"
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extracting traffic flow information from vehicle GPS tracks
457:"Hidden Markov Map Matching Through Noise and Sparseness"
324:"Map-Matching for Low-Sampling-Rate GPS Trajectories"
474:Luo, An; Chen, Shenghua; Xv, Bin (November 2017).
103:associating user-reported attributes with a street
96:Other uses, more analytical in nature, include:
322:; Wei Wang & Yan Huang (November 4, 2009).
480:ISPRS International Journal of Geo-Information
27:Matching of coordinates to physical locations
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455:Newson, Paul; Krumm, John (November 2009).
300:& Salas, Randall (September 2, 2005).
255:Teng, Wenxin; Wang, Yanhui (8 July 2019).
75:Map matching algorithms can be divided in
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341:Marchal; Hackney; Axhausen (July 2004).
302:"On Map-Matching Vehicle Tracking Data"
296:Brakatsoulas, Sotiris; Pfoser, Dieter;
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565:"Map Matching Implementation in Java"
359:Schuessler; Axhausen (October 2009).
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318:Yin Lou; Chengyang Zhang; Yu Zheng;
196:European Transport Research Review
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149:Map matching is described as a
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70:transportation engineering
34:Map matching example with
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66:GPS tracking of freight
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84:Examples and use cases
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596:Computing terminology
545:"open-tracking-tools"
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62:satellites navigation
52:) and relate them to
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145:Hidden Markov Models
127:Topological approach
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492:2017IJGI....6..327L
273:2019OGeo...11...23T
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151:hidden Markov model
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