Modelling Animal-Vehicle Collision Counts across Large Networks Using a Bayesian Hierarchical Model with Time-Varying Parameters Article Swipe
Krishna Murthy Gurumurthy
,
Zili Li
,
Kara M. Kockelman
,
Prateek Bansal
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.02289
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2104.02289
Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify locations of high risk are valuable to AVC prevention, yielding economic, social and environmental cost savings. However, many challenges exist in the study of AVC datasets. These include seasonality of animal activity, unknown exposure (i.e., the number of animal crossings), very low AVC counts across most sections of extensive roadway networks, and computational burdens that come with discrete response analysis using large datasets. To overcome these challenges, a Bayesian hierarchical model is proposed where the exposure is modeled with nonparametric Dirichlet process, and the number of segment-level AVCs is assumed to follow a Binomial distribution. A Pólya-Gamma augmented Gibbs sampler is derived to estimate the proposed model. By using the AVC data of multiple years across about 100,000 segments of state-controlled highways in Texas, U.S., it is demonstrated that the model is scalable to large datasets, with a preponderance of zeros and clear monthly seasonality in counts, while identifying high-risk locations (for application of design treatments, like separated animal crossings with fencing) and key explanatory factors based on segment-specific factors (such as changes in speed limit) can be done within the modelling framework, which provide useful information for policy-making purposes.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2104.02289
- https://arxiv.org/pdf/2104.02289
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4287238736Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2104.02289Digital Object Identifier
- Title
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Modelling Animal-Vehicle Collision Counts across Large Networks Using a Bayesian Hierarchical Model with Time-Varying ParametersWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
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2021Year of publication
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2021-04-06Full publication date if available
- Authors
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Krishna Murthy Gurumurthy, Zili Li, Kara M. Kockelman, Prateek BansalList of authors in order
- Landing page
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https://arxiv.org/abs/2104.02289Publisher landing page
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https://arxiv.org/pdf/2104.02289Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2104.02289Direct OA link when available
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Computer science, Bayesian probability, Gibbs sampling, Dirichlet process, Dirichlet distribution, Count data, Econometrics, Statistics, Mathematics, Artificial intelligence, Poisson distribution, Mathematical analysis, Boundary value problemTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.locations | 42, 192 |
| abstract_inverted_index.modelling | 223 |
| abstract_inverted_index.networks, | 92 |
| abstract_inverted_index.purposes. | 231 |
| abstract_inverted_index.separated | 199 |
| abstract_inverted_index.challenges | 60 |
| abstract_inverted_index.collisions | 1 |
| abstract_inverted_index.framework, | 224 |
| abstract_inverted_index.occurrence | 30 |
| abstract_inverted_index.application | 194 |
| abstract_inverted_index.challenges, | 108 |
| abstract_inverted_index.crossings), | 81 |
| abstract_inverted_index.explanatory | 206 |
| abstract_inverted_index.identifying | 190 |
| abstract_inverted_index.information | 228 |
| abstract_inverted_index.prevention, | 50 |
| abstract_inverted_index.seasonality | 70, 186 |
| abstract_inverted_index.significant | 21 |
| abstract_inverted_index.treatments, | 197 |
| abstract_inverted_index.Pólya-Gamma | 138 |
| abstract_inverted_index.considerable | 11 |
| abstract_inverted_index.contributing | 35 |
| abstract_inverted_index.demonstrated | 169 |
| abstract_inverted_index.hierarchical | 111 |
| abstract_inverted_index.Understanding | 28 |
| abstract_inverted_index.computational | 94 |
| abstract_inverted_index.distribution. | 136 |
| abstract_inverted_index.environmental | 55 |
| abstract_inverted_index.nonparametric | 121 |
| abstract_inverted_index.policy-making | 230 |
| abstract_inverted_index.preponderance | 180 |
| abstract_inverted_index.segment-level | 128 |
| abstract_inverted_index.Animal-vehicle | 0 |
| abstract_inverted_index.segment-specific | 210 |
| abstract_inverted_index.state-controlled | 162 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |