MATCHING GPS OBSERVATIONS TO LOCATIONS ON A DIGITAL MAP

By J. Greenfeld. 2002

524 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/61f7e5921151c7e6d574c07b3615377d36b8f6b1

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1. Yu Zheng
2. Jing Yuan
3. Haitao Yuan
4. Xing Xie
5. Guoliang Li
6. Timothy Hunter
7. Guangzhong Sun
8. Chengyang Zhang

Citations in the corpus

• Trajectory Data Mining (2015, methodology): “According to the additional information used, map matching algorithms can be categorized into four groups: geometric [36], topological [22][118], probabilistic [75] [83] [86] and other advanced techniques [63][73][126].”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “In particular, some methods [30, 31] just consider matching a GPS point on the nearest road by computing the Euclidean distance, and some work [32–34] focus on a trajectory by sequently matching each point of the trajectory onto a road with some greedy strategies.”
• Map-matching for low-sampling-rate GPS trajectories (2009, unknown): “The incremental matching algorithm in [8][7] pursues the local matching of a small portion of the trajectory.”
• A general map matching algorithm for transport telematics applications (2003, methodology): “A formal definition of MM can be found in Bernstein and Kornhauser (1996), White et al. (2000) and Greenfeld (2002).”
• The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data (2011, methodology): “New deterministic algorithms emerged to directly match partial trajectories to the road by using the topology of the network [6], and were soon expanded into probabilistic frameworks.”
• Hidden Markov map matching through noise and sparseness (2009, background): “Another purely geometric approach comes from Greenfeld [ 5 ], whose algorithm builds up a topologically feasible path through the road network.”
• On Map-Matching Vehicle Tracking Data (2005, methodology): “Greenfeld [9] introduces a map-matching strategy based on distance and orientation that does not assert any further knowledge about the movement besides the position samples.”
• An Interactive-Voting Based Map Matching Algorithm (2010, methodology): “According to the information of input GPS tracking data used, existing methods can be categorized into four groups: geometric [12], topological [13, 14], probabilistic [15] and other advanced techniques [16, 17, 19].”