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

• Author / Creator

  Islam, Md Tazul

• 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.  

• Subjects / Keywords

  • Spatial Crash Model
  • Traffic Speed Model
  • Before-After Safety Evaluation
  • Full Bayesian Evaluation

• Graduation date

  Fall 2015

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3DB7W23S

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.