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Application of New Modelling Techniques to Perform Observational Before-After Safety Evaluations Open Access


Other title
Before-After Safety Evaluation
Full Bayesian Evaluation
Spatial Crash Model
Traffic Speed Model
Type of item
Degree grantor
University of Alberta
Author or creator
Islam, Md Tazul
Supervisor and department
El-Basyouny, Karim (Civil and Environmental Engineering)
Qiu, Zhi-Jun (Civil and Environmental Engineering)
Examining committee member and department
Qiu, Zhi-Jun (Civil and Environmental Engineering)
Bouferguene, Ahmed (Campus Saint-Jean)
El-Basyouny, Karim (Civil and Environmental Engineering)
Miranda-Moreno, Luis (Civil Engineering, McGill University)
Tjandra, Stevanus (Civil and Environmental Engineering)
Department of Civil and Environmental Engineering
Transportation Engineering
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Speeding is the number one road safety problem in many countries around the world. Speeding contributes to as many as one third of all fatal crashes, and is considered an aggravating factor in crash severity. Because of the adverse consequences of speeding, speed management is considered to be the key strategy to reduce traffic fatalities and injuries. Any speed management strategy has an immediate effect on drivers speed choice and a long-term effect on crash occurrence; these effects can be referred to impact and outcome, respectively. A comprehensive evaluation process of any speed management strategy therefore should include impact evaluation based on speed data and outcome evaluation based on crash data. This evaluation is an important step in the road safety management process because the evaluation results can be used not only for economic justification of the strategy but also for future decision-making activities related to the allocation of funds and selection of appropriate remedial strategies. While the methodologies associated with before-after evaluation of speed and crash data have improved substantially in last two decades, there are several areas for improving the before-after evaluation methodologies in order to provide more reliable estimates of the safety effect of any speed management strategy. Therefore, the research in this thesis focuses on addressing key issues related to the modelling and application of before-after evaluation of i) speed data and ii) crash data. Vehicle speed data are collected from different sites over a period of time; hence, the speed data exhibit within-site and between-site variation. The conventional ordinary least-square regression model fails to capture these two variations of the speed data into the modelling framework. Similarly, crash data exhibits several specific features, such as correlation among severity levels and spatial correlation that need to be addressed into the modelling framework for the unbiased estimation of the model parameters. This thesis addressed several key issues by 1) developing appropriate statistical test method to address and account for confounding factors and time trend in non-model based before-after speed data evaluation, 2) developing a mixed-effect intervention modelling approach for modelling and evaluating before-after speed characteristics that incorporate the clustering nature of speed data, 3) exploring multilevel heterogeneous model to address the heterogeneous site variances of speed data, 4) developing multivariate full Bayesian (FB) methodology for before-after evaluation of crash data that can take account for the correlation of crash data of different severity levels and comparing the results with univariate counterpart, 5) developing FB macroscopic spatial modelling approach for before-after evaluation of crash data that can address the limitations of the microscopic evaluation as well as incorporate spatial correlation of the crash data and comparing the results with non-spatial models, and 6) developing an alternative modelling methodology to address spatial correlation into the modelling of before-after evaluation of crash data and compare the results with other spatial models. Several advanced statistical models were developed for both speed and crash data and the models were compared for their goodness of fits. The applications of the various developed models have been demonstrated using both microscopic and macroscopic datasets collected for an urban residential posted speed limit reduction pilot program. The results provide strong evidence for (i) addressing the effect of confounding factors in non-model based speed data evaluation for more reliable estimate of the effect of a safety intervention, ii) considering the clustered nature of speed data into models used to conduct before-after evaluation, iii) incorporating heterogeneous site variances into multilevel modelling and evaluation of mean free-flow speed, iv) developing multivariate models for modelling and evaluation of crash by severity, v) incorporating spatial correlation in modelling of before-after crash data, and vi) using alternative spatial models to better capture the spatial correlation of crash data. Finally, the multilevel model with heterogeneous variance provided significant improvement in the goodness-of-fit over other models for speed data analysis. For crash data, multivariate spatial models provided significant improvement in the goodness-of-fit over other univariate or non-spatial models. Therefore, it is recommended to employ multilevel model with heterogeneous variance and multivariate spatial models for more reliable and unbiased estimate of the effect of a safety intervention on vehicle speed and crash data, respectively.  
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Islam, M. T., and El-Basyouny, K. (2015). Full Bayesian mixed intervention model for before-after speed data analysis. Transportation Research Record: Journal of the Transportation Research Board. In press.Islam, M. T., and El-Basyouny, K. (2015). Full Bayesian before-after safety evaluation of the posted speed limit reduction on urban residential area. Accident Analysis and Prevention, 80, 18-25.Islam, M. T., El-Basyouny, K., and Ibrahim, S. (2014). The impact of lowered speed limit in the City of Edmonton. Safety Science, 62, 483–494.Barua, S., El-Basyouny, K., and Islam, M. T. (2014). A multivariate count data model of collision severity with spatial correlation. Analytic Methods in Accident Research, 3–4, 28–43.

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