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Application of hidden Markov models on residuals: an example using Canadian traffic accident data
W H Laverty1, M J Miket, I W Kelly
1Department of Educational Psychology and Special Education, University of Saskatchewan, Saskatoon, Canada.
Perceptual and Motor Skills
|August 21, 2002
Summary
Hidden Markov models revealed two distinct accident patterns in Saskatchewan automobile data. A
Area of Science:
- Social Science
- Statistics
- Transportation Safety
Background:
- Road safety analysis often involves identifying trends and seasonal variations in accident data.
- Previous analyses of Saskatchewan automobile accidents (1992) identified linear trends, seasonal, holiday, and day-of-week effects.
Purpose of the Study:
- To explore underlying patterns in automobile accident data beyond traditional regression analysis.
- To apply hidden Markov models (HMMs) to residual data from a regression analysis of traffic accidents.
Main Methods:
- Conducted a regression analysis on 9 years of Saskatchewan automobile accident data (200,545 accidents).
- Applied a hidden Markov model to the residuals of the regression analysis.
- Identified and characterized distinct states within the residual data.
Main Results:
- Uncovered two hidden states in the accident data, labeled 'low volatility' and 'high volatility'.
- The 'high volatility' state, characterized by greater variability, was associated with colder months and a higher number of accidents.
- The 'low volatility' state exhibited less variability.
Conclusions:
- Hidden Markov models are effective for detecting unobserved states in complex datasets.
- The identified states likely relate to weather conditions influencing accident frequency and severity.
- HMMs offer a valuable tool for uncovering hidden dynamics in social science and health-related research.
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