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

By Timothy Hunter, Pieter Abbeel, Alexandre M. Bayen. IEEE transactions on intelligent transportation systems (Print), 2011

486 citations recorded by the source corpus.

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.

Subfields: Building and Construction, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/2c1921eae702e835b24f95b83c3613d5c79e229b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

1. Haitao Yuan
2. Guoliang Li
3. Junbo Zhang
4. Yu Zheng
5. Wei Cao
6. Jian Li
7. Philip S. Yu
8. Senzhang Wang

In Control and Systems Engineering

1. Yu Zheng
2. Haitao Yuan
3. Guoliang Li
4. Junbo Zhang

In Ocean Engineering

1. Haitao Yuan
2. Guoliang Li
3. Junbo Zhang
4. Yu Zheng
5. Philip S. Yu

Citations in the corpus

• When Will You Arrive? Estimating Travel Time Based on Deep Neural Networks (2018, background): “(Jenelius and Koutsopoulos 2013)) are able to capture the aforementioned traffic conditions implicitly.”
• A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation (2021, methodology): “To avoid the selection bias problem, Hunter et al. [51] leverage the model CRF.”