SAFETY EFFECTS OF SHOULDER AND CENTERLINE RUMBLE STRIPS: A BAYESIAN PROPENSITY SCORE MATCHING FRAMEWORK
Open Access
- Author:
- Li, Lingyu
- Graduate Program:
- Civil Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- December 12, 2018
- Committee Members:
- Eric T. Donnell, Dissertation Advisor/Co-Advisor
Eric T. Donnell, Committee Chair/Co-Chair
Vikash V. Gayah, Committee Member
S. Ilgin Guler, Committee Member
Kari Lock Morgan, Outside Member - Keywords:
- Propensity score matching
crash modification factor
shoulder and centerline rumble strips
safety effect estimates
causal inference
Bayesian method
covariate balancing - Abstract:
- To develop a reliable and accurate safety effect estimate is one of the fundamental objectives in the traffic safety study. Transportation engineering practitioners use the safety effect estimates to identify and deploy a variety of safety countermeasures to reduce the frequency and severity of traffic accidents. Researchers have developed several methodologies to estimate the safety treatment effect in the observational study. The current state-of-the-practice is the empirical Bayes before-after study, which utilized the before and after treatment data to evaluate the treatment effect. The advantage of this methodology is that it corrects the regression-to-the-mean. Another prevalent method is the cross-sectional regression models. It can be used to manage cross-sectional data and estimate the effect of multi-level treatments. In recent years, the propensity score matching-potential outcomes framework has been introduced into the traffic safety study. This method is capable to balance the covariates between treatment and control (or reference) groups, and thus, reveal the causal relationship between the treatment and outcome. Recent traffic safety studies showed the strength of this method in estimating countermeasure safety effectiveness. However, the issue in the traffic safety data such as the unobserved heterogeneity may still affect the effectiveness and robustness of this propensity score matching method. This study explores an alternative way to conduct propensity score matching by incorporating Bayesian methods into the propensity score estimation. In order to explore the potential benefits of the Bayesian propensity score matching, a no-treatment analysis and a simulation-based analysis were conducted in the research. The data used in the analysis is the Pennsylvania two-lane rural highway crash database with the treatment of the duel application of shoulder and centerline rumble strips. The no-treatment analysis focused on investigating the effectiveness of the Bayesian propensity score matching framework. The regional-level samples such as statewide, district-level and county-level data were used respectively in the no-treatment analysis. The simulation-based analysis focused on investigating the robustness of this framework when there is unobserved heterogeneity. In each analysis, the Bayesian propensity score matching was conducted with different Bayesian prior settings, matching ratios and caliper widths. In addition, the ability to balance covariates and yield unbiased safety effect estimates was compared between the proposed methodology and the traditional propensity score matching method. The findings indicated that: 1. The Bayesian propensity score matching method is superior to the traditional propensity score matching method in reducing bias of safety effect estimates when there are sufficient samples in the analysis. When the sample size is limited, incorporating Bayesian method would still improve the matching performance in balancing the covariates. 2. The Bayesian propensity score matching method has the potential to yield unbiased estimates under the influence of unobserved heterogeneity. 3. The estimates from Bayesian propensity score matching method is sensitive to the choice of Bayesian prior and matching configuration such as ratio and caliper width. Decisions need to be made when applying this framework to the real data according to the sample size and number of measured covariates. The research also provides the guideline for the application of Bayesian propensity score matching method. A step-by-step flowchart is developed to help the researchers and practitioners to implement this framework.