The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data

We consider the problem of reconstructing vehicle trajectories from sparse sequences of GPS points, for which the sampling interval is between 1 s and 2 min. We introduce a new class of algorithms, which are altogether called the path inference filter (PIF), that maps GPS data in real time, for a va...

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Bibliographic Details
Published in:	IEEE transactions on intelligent transportation systems Vol. 15; no. 2; pp. 507 - 529
Main Authors:	Hunter, Timothy, Abbeel, Pieter, Bayen, Alexandre
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2014 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Expectation-maximization algorithms filtering Global Positioning System GPS data Inference Intelligent transportation systems Markov random fields Matching maximum likelihood estimation Mobile communication network-free data path observation generation probabilistic map matching Probability distribution Probes Real time Roads route choice modeling Sampling State of the art Traffic information Trajectory Vehicles San Francisco California route choice modeling GPS data path observation generation network-free data probabilistic map matching Expectation-maximization algorithms filtering maximum likelihood estimation Markov random fields
ISSN:	1524-9050, 1558-0016
Online Access:	Get full text
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