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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| Published in: | IEEE transactions on intelligent transportation systems Vol. 15; no. 2; pp. 507 - 529 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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New York
IEEE
01.04.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1524-9050, 1558-0016 |
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| Abstract | 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 variety of tradeoffs and scenarios and with a high throughput. Numerous prior approaches in map matching can be shown to be special cases of the PIF presented in this paper. We present an efficient procedure for automatically training the filter on new data, with or without ground-truth observations. The framework is evaluated on a large San Francisco taxi data set and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The PIF has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco and Sacramento, CA, USA; Stockholm, Sweden; and Porto, Portugal. |
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| AbstractList | 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 variety of tradeoffs and scenarios and with a high throughput. Numerous prior approaches in map matching can be shown to be special cases of the PIF presented in this paper. We present an efficient procedure for automatically training the filter on new data, with or without ground-truth observations. The framework is evaluated on a large San Francisco taxi data set and is shown to improve upon the current state of the art. This filter also provides insights about driving patterns of drivers. The PIF has been deployed at an industrial scale inside the Mobile Millennium traffic information system, and is used to map fleets of data in San Francisco and Sacramento, CA, USA; Stockholm, Sweden; and Porto, Portugal. |
| Author | Hunter, Timothy Abbeel, Pieter Bayen, Alexandre |
| Author_xml | – sequence: 1 givenname: Timothy surname: Hunter fullname: Hunter, Timothy email: tjhunter@eecs.berkeley.edu organization: Dept. of Electr. Eng. & Comput. Sci., Univ. of California at Berkeley, Berkeley, CA, USA – sequence: 2 givenname: Pieter surname: Abbeel fullname: Abbeel, Pieter email: pabbeel@cs.berkeley.edu organization: Dept. of Electr. Eng. & Comput. Sci., Univ. of California at Berkeley, Berkeley, CA, USA – sequence: 3 givenname: Alexandre surname: Bayen fullname: Bayen, Alexandre email: bayen@berkeley.edu organization: Dept. of Electr. Eng. & Comput. Sci., Univ. of California at Berkeley, Berkeley, CA, USA |
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| SubjectTerms | 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 |
| Title | The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data |
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