Open Access
The Path Inference Filter: Model-Based Low-Latency Map Matching of Probe Vehicle Data.
Timothy Hunter,Pieter Abbeel,Alexandre M. Bayen +2 more
- 01 Jan 2012
pp 591-607
138
TL;DR: 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, is introduced.
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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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Citations
A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models
TL;DR: A self-adaptive parameter selection algorithm called HMTP * is proposed, which captures the parameters necessary for real-world scenarios in terms of objects with dynamically changing speed and has higher positioning precision than HMTP due to its capability of self-adjustment.
281
Predicting travel time reliability using mobile phone GPS data
TL;DR: TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks, using GPS data from mobile phones or other probe vehicles.
Online Map-Matching of Noisy and Sparse Location Data With Hidden Markov and Route Choice Models
TL;DR: This paper presents a novel map-matching solution that combines the widely used approach based on a hidden Markov model (HMM) with the concept of drivers’ route choice, which uses an HMM tailored for noisy and sparse data to generate partial map-matched paths in an online manner.
116
Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data
TL;DR: A simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network that helps to understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.
99
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