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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Speed limit reduction in urban areas: a before-after study using Bayesian generalized mixed linear models.

Shahram Heydari1, Luis F Miranda-Moreno2, Fu Liping1

  • 1Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue W., Waterloo, Ontario N2L 3G1, Canada.

Accident; Analysis and Prevention
|September 28, 2014
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Summary

Montreal

Keywords:
Bayesian generalized mixed linear modelsBefore–after studiesExcessive speedingSpeed limit reductionSpeeding

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Area of Science:

  • Traffic Safety
  • Urban Planning
  • Transportation Engineering

Background:

  • Montreal reduced local street speed limits from 50 km/h to 40 km/h in fall 2009.
  • Assessing the impact of speed limit reductions on driver behavior is crucial for urban safety.

Purpose of the Study:

  • To propose and apply a novel methodology for estimating the effect of speed limit reductions on speeding behaviors.
  • To analyze hourly speed data and the entire speed distribution, not just point estimates.

Main Methods:

  • Utilized a full Bayes before-after approach, overcoming empirical Bayes limitations.
  • Employed two Bayesian generalized mixed linear models to analyze operating speeds.
  • Considered site characteristics, temporal variables, and environmental factors.

Main Results:

  • Speed limit reduction was effective for 40 km/h and 50 km/h references but not for excessive speeding (>80 km/h).
  • Lane width and night hours increased speeding; roadside parking decreased it.
  • One-way streets and lane width increased excessive speeding; evening hours decreased it.

Conclusions:

  • Speed limit reductions can be effective but require careful evaluation, especially concerning high-risk excessive speeding.
  • The methodology accounts for the full speed distribution and daily profile, offering a more comprehensive analysis.
  • Using a comparison group is vital for isolating treatment effects from confounding factors.