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Activity Number:
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111
- Application and Development of Statistical Methods for Spatio-Temporal Data
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #323432
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Title:
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Using Dirichlet Processes and Machine Learning to Estimate Crash Risk on Roadways
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Author(s):
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Benjamin K. Dahl* and Matthew Heaton and Richard Warr and Philip White and Grant G. Schultz and Caleb Dayley
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Companies:
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Brigham Young University and BYU and Brigham Young University and BYU and Brigham Young University and Brigham Young University
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Keywords:
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Point pattern;
Poisson process;
Log-Gaussian Cox process;
Bayesian nonparametrics;
Dirichlet process;
Traffic
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Abstract:
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Historically, specifying models for point pattern data has had to balance flexibility with interpretability. On the one hand, mixture model specifications for Poisson process intensity surfaces can flexibly capture the non-linear nature of the intensity surface, but do not yield interpretable regression parameters. On the other hand, log-Gaussian Cox processes can give interpretable regression coefficients for the intensity surface but can be computationally costly to implement. In this project we provide a partial solution to this balancing act by using Dirichlet processes to flexibly model an intensity surface for a Poisson process. We then project the resulting Dirichlet process fit onto a set of basis functions using penalized regression to obtain an estimate of a corresponding log-Gaussian Cox process fit. We demonstrate this process by estimating the intensity surface and associated effects of roadway characteristics on the frequency of crashes along I-15 in Utah from 2019-2020.
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Authors who are presenting talks have a * after their name.
