| dc.description.abstract | For cities aiming to create a useful surveillance system for bicycle injuries, a common challenge is that city crash reporting is scattered, faulty, or non-existent. In chapter 1, I document some of the lessons learned in helping the City of Boston to: 1) create a prototype for a comprehensive police crash database, 2) produce the city’s first Cyclist Safety report, 3) make crash data available to the public, and 4) generate policy recommendations for both specific roadside improvements and for sustainable changes to the police department’s crash reporting database. Some of the lessons include finding and using committed champions, prioritizing the use of existing data, creating opportunities to bridge divisions between stakeholders, partnering with local universities for assistance with advanced analytics, and using deliverables, such as a Cyclist Safety Report, to advocate for sustainability.
In chapter 2, given that the first step in the public health approach to injury prevention is to identify the problem (Krug et al, 2002), I examine whether police narrative reports cover the information that end-users need to do their part in preventing bicycle injuries. For example, civil engineers can use crash data to identify road conditions that need fixing, such as pavement defects and potholes. Urban planners can use reports to inform their design of the built environment, such as protected bicycle lanes and road diets. Health educators can use the data to plan campaigns. Lastly, police can use the data to determine where and when to focus their enforcement of traffic laws. I used a sample of narrative reports and filled in the fields in a government-recommended bicycle crash form aimed at understanding multiple factors about the crash. I used the percent of missing data across various domains, such as bicyclist information, environmental conditions, road conditions, and others, and found that that the reports did well in crash typing. Examples of “crash types” are: motorist failed to yield, bicyclist lost control, and bicyclist ran a red light. The percent missingness in the crash-typing domain was, in general, lower and had more variation than percent missingness in other domains. Percent missingness for the crash-typing domain, for example, ranged from just over 40% to 75%. Other domains had little variation, such that missingness was generally over 75%. Police officers generally do not have professional training in road engineering or urban planning or public health and healthcare (which relate to the other domains in the recommended bicycle crash form). In addition, they are not compensated to collect that level of detail. Our results also show that there is less information (more missingness) when police officers take a statement from an involved party either in person or by phone versus when they are onsite. Given that there is a fair amount of missingness in narrative reports, I recommend adopting the Pedestrian Bicycle Crash Analysis Tool (PBCAT) and training officers to use it. The PBCAT software, endorsed by the US Government, is freely available to anyone and any police department for direct download.
In chapter 3, I identify factors related to a hit-and-run after a vehicle-bicycle collision. Understanding bicycle-vehicle collisions that result in hit-and-run (HAR) behavior is an important concern for law enforcement, public health, and affected individuals. If bicyclists are injured, this issue has implications for expedient access to medical care and for protection from the financial burden of associated medical costs. This study aimed to identify significant predictors of vehicle-bicycle HARs, the results of which can potentially inform preventive interventions for this type of injury and crime. I collected the data from Boston Police Department bicycle crash reports for 2009-2012. The data identified whether a crash was a hit-and-run and other predictor variables including road and bicyclist characteristics. The probability of a HAR was fit to selected variables through logistic regression models. Effects of the predictors were reported as odds ratios. I found that the probability of a hit-and-run partially depends on time, day of the week, and whether the vehicle type was a taxi. I discuss implications for policies and interventions aimed at preventing this type of collision and crime. | en_US |
