Related Experiment Video
Updated: Jul 18, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
Published on: February 25, 2013
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.
Abstract:
Laverty, Kelly, Rotton, and Flynn conducted a regression analysis in 1992 on 9 years of automobile accidents in Saskatchewan (a total of 200,545 accidents) to find a small linear trend, season effects, holiday, and day of the week effects. The application of a hidden Markov model to the residuals of this analysis uncovered two states which are likely to be related to the weather. These states can be described as low volatility' and 'high volatility'. The 'low volatility' state involves low variability compared to the 'high volatility' state (occurring during the colder months) during which the largest numbers of accidents occur. It is suggested that hidden Markov models are a useful method for uncovering hidden, underlying states in social science and health-related data.
Related Concept Videos
Probability Histograms
Hypothesis Test for Test of Independence
H0: The two variables (factors)...
Determination of Expected Frequency
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Residual Plots
When the residual values are plotted against the variable x, it is called a residual...
Mechanistic Models: Compartment Models in Individual and Population Analysis
