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Map matching for low-sampling-rate GPS trajectories by exploring real-time moving directions

https://doi.org/10.1016/j.ins.2017.12.031

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Implementation of generic algorithm in map-matching model

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Path inference from sparse floating car data for urban networks

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Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

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Some map matching algorithms for personal navigation assistants

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On‐line map‐matching framework for floating car data with low sampling rate in urban road networks

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An HMM-based map matching method with cumulative proximity-weight formulation

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Online map matching by indexing approximate road segments

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Online map-matching based on Hidden Markov model for real-time traffic sensing applications

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Accurate Real-time Map Matching for Challenging Environments

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A ST-CRF Map-Matching Method for Low-Frequency Floating Car Data

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Online Map-Matching of Noisy and Sparse Location Data With Hidden Markov and Route Choice Models

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AntMapper: An Ant Colony-Based Map Matching Approach for Trajectory-Based Applications

inferredcites →https://doi.org/10.1109/tkde.2016.2617326openalex-snapshot

IF-Matching: Towards Accurate Map-Matching with Information Fusion

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Hidden Markov map matching through noise and sparseness

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Map-matching for low-sampling-rate GPS trajectories

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Eddy

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Accurate and efficient map matching for challenging environments

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Computing the shortest path: A search meets graph theory

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Real time map matching for low frequency GPS data based on machine learning technology

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