title: Speed limit reduction in urban areas: a before-after study using Bayesian generalized mixed linear models.
abstract: In fall 2009, a new speed limit of 40 km/h was introduced on local streets in Montreal (previous speed limit: 50 km/h). This paper proposes a methodology to efficiently estimate the effect of such reduction on speeding behaviors. We employ a full Bayes before-after approach, which overcomes the limitations of the empirical Bayes method. The proposed methodology allows for the analysis of speed data using hourly observations. Therefore, the entire daily profile of speed is considered. Furthermore, it accounts for the entire distribution of speed in contrast to the traditional approach of considering only a point estimate such as 85th percentile speed. Different reference speeds were used to examine variations in the treatment effectiveness in terms of speeding rate and frequency. In addition to comparing rates of vehicles exceeding reference speeds of 40 km/h and 50 km/h (speeding), we verified how the implemented treatment affected "excessive speeding" behaviors (exceeding 80 km/h). To model operating speeds, two Bayesian generalized mixed linear models were utilized. These models have the advantage of addressing the heterogeneity problem in observations and efficiently capturing potential intra-site correlations. A variety of site characteristics, temporal variables, and environmental factors were considered. The analyses indicated that variables such as lane width and night hour had an increasing effect on speeding. Conversely, roadside parking had a decreasing effect on speeding. One-way and lane width had an increasing effect on excessive speeding, whereas evening hour had a decreasing effect. This study concluded that although the treatment was effective with respect to speed references of 40 km/h and 50 km/h, its effectiveness was not significant with respect to excessive speeding-which carries a great risk to pedestrians and cyclists in urban areas. Therefore, caution must be taken in drawing conclusions about the effectiveness of speed limit reduction. This study also points out the importance of using a comparison group to capture underlying trends caused by unknown factors.
authors.affiliation: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue W., Waterloo, Ontario N2L 3G1, Canada. Electronic address: shahram.heydari@uwaterloo.ca.
authors.forename: Shahram
authors.initials: S
authors.lastname: Heydari
authors.affiliation: Department of Civil Engineering and Applied Mechanics, McGill University, 817 Sherbrooke St. W., Montreal, Quebec H3A 2K6, Canada. Electronic address: luis.miranda-moreno@mcgill.ca.
authors.forename: Luis F
authors.initials: LF
authors.lastname: Miranda-Moreno
authors.affiliation: Department of Civil and Environmental Engineering, University of Waterloo, 200 University Avenue W., Waterloo, Ontario N2L 3G1, Canada. Electronic address: lfu@uwaterloo.ca.
authors.forename: Fu
authors.initials: F
authors.lastname: Liping
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control*; D001334:Automobile Driving / Q000331:legislation & jurisprudence*; D001499:Bayes Theorem; D002947:Cities; D065187:Controlled Before-After Studies; D006801:Humans; D016014:Linear Models; D008962:Models, Theoretical; D012621:Seasons
keywords: Bayesian generalized mixed linear models; Before–after studies; Excessive speeding; Speed limit reduction; Speeding