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Full bayesian models to assess the impacts of mobile automated enforcement on road safety and crime

University of British Columbia

The success of road safety programs is highly dependent on using accurate and precise safety models. Traditionally, these safety models were developed at a micro-level and lack understanding of how safety is prioritized at a planning-level. This dissertation bridges this gap by developing macro-level models to enhance the decision-making processes by providing opportunities for planners and designers to become better informed on issues related to road safety and criminology. The contributions of this dissertation were to develop Full Bayesian models to explore new applications for macro-level modeling, which focused on mobile automated enforcement (MAE). This type of enforcement is one of the tools that agencies use when manned enforcement is too costly or not feasible. It consists of units that are installed in vehicles that rotate between sites to improve compliance to the speed limit and to enhance safety. The first application showed that increasing the number of tickets issued for vehicles exceeding the speed limit resulted in a decrease in all collision severities. The results also showed that collision reductions were associated with an extended time enforcing a site. Decision support tools were also created to help agencies make informed decisions regarding how to optimize their enforcement strategy. The second application explored the impact of MAE on both collisions and crime. Previous work suggested that collision and crime hotspots overlapped. It was, therefore, crucial to quantify the degree of correlation between both events. The results of the models confirmed this relationship and showed that increased MAE presence resulted in reductions in both events. This demonstrates how a single deployment can achieve multiple objectives, and allows agencies to optimize their deployment strategy to achieve more with less. Understanding how changing the deployment strategy at a macro-level affects safety provides enforcement agencies with the opportunity to maximize the efficiency of their existing resources. Future work would include using the results in this dissertation to build an optimization tool which accounts for the safety impacts, constraints surrounding a deployment, and the cost of a deployment to allow agencies to maximize the use of their resources to achieve the highest safety benefit.

Text

2020-11-20

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/76568

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/