Crashes and weather in boulder
Car crashes have been and continue to be a significant public safety concern across the United States, particularly in areas with challenging and rapidly changing weather conditions. Boulder County, Colorado, is one such area known for its unpredictable weather patterns during winter months, sometimes experiencing blizzards one day and clear skies the next. This extended op-ed aims to meticulously analyze the relationship between weather conditions and car crash incidents in Boulder County region using various data visualization methods. By understanding the impact of weather on car crashes, we can identify intricate patterns and trends that might help us mitigate the risks associated with driving in adverse conditions and develop better strategies for driver education and infrastructure improvements.
The first visualization provides a comprehensive overview of the 38 different car crash types in the dataset and their sum counts from January 1st, 2015 to December 31st, 2020 in Boulder County, Colorado. The data revealed that rear-end collisions were the most prevalent type of crash, with a staggering 315,304 crashes. Following this, at 109,697 crashes, is broadside, which is defined by the Colorado Department of Transportation (CDOT) as, “A crash type that involves two vehicles approaching from non-opposing angular directions (i.e., T-bone)”(1). The third most common crash type is sideswipe (same-direction) at 87,452. A sideswipe is defined by CDOT as, “A crash type that involves two vehicles moving alongside each other and colliding, with at least one of the vehicles being struck on the side. This type would include a collision resulting from one of the vehicles making an improper turn, such as a left from the right lane or vice versa, or turning right from the appropriate outside lane and striking a vehicle passing on the right shoulder”(1). The fourth most common was unknown at 86,143, which was initially confusing but is shorthand for a hit-and-run or unknown vehicle, defined by CDOT as, “Unknown vehicle type, a vehicle that left the scene of a crash.”(1) At number five were parked motor vehicle accidents at 78,463, as defined by CDOT, “A crash type in which a vehicle in motion collides with a parked motor vehicle whether occupied or not.” This kind of information is critical to understand which types of crashes are most common and, by extension, what to focus on when creating prevention and protection strategies. These types of crashes can be addressed in various ways, both top-down and bottom-up. For example, rear-end collisions could be reduced through grassroots efforts, such as promoting safe following distances, especially in younger drivers who are just learning to drive. On the other hand, broadside collisions could be addressed through top-down approaches, such as better intersection design and traffic signal timing.
After exploring and understanding the most common types of crashes, we can delve deeper into the relationship between weather conditions and car crashes. For this, a scatter plot with regression lines was created for the top five most common crash types. For the plot, average temperature was used for the x-axis, as it provided a better sense of what the day was like as a whole. The plot revealed, somewhat surprisingly, that for the top three most common types of crashes and accidents, there is a positive correlation with temperature, showing that the number of crashes increases as the temperature rises. This was surprising, as going into the analysis, one would expect colder conditions to have a stronger correlation, as roads have a higher chance of being frozen or covered in snow. One possible explanation for this surprising result could be related to the number of drivers, especially new ones. During warmer months, when drivers do not have to worry about icy conditions, it is most likely less stressful to learn how to drive a car. Either way, after obtaining these results, I decided to see if there were other ways to support or contradict these findings.
After the surprising results of the scatter and linear regression plots, I decided to create a series of time-series line graphs to examine the seasonality variation and trends of car crashes in Boulder over time. What is initially obvious from the plots is that there is rather high variation throughout most of the year; however, in the first two and final two months of each year, you will notice more extreme spikes, which happen to coincide with the winter months of the year. The most likely reason for these spikes, rather than a general increase, is likely due to the somewhat bipolar nature of Boulder’s weather, where some days are bright and sunny and others are cold and freezing. Moreover, it’s likely that the days that see the highest peaks were days with especially cold or snowy weather. There are some years that are exceptions to this general seasonality, with a notable drop in 2020 coinciding with the COVID-19 lock downs. An anomaly that actually supports this seasonality is in the first 11 days from the 3rd to 14th of January 2018. During this period the number of accidents saw a significant drop. During this time there was a significant rise in average temperature, reaching up into the 60s Fahrenheit and rarely below 20, most likely only dropping to freezing during the night. This gathered data suggests that weather conditions, such as snow and ice, which only form during the colder, wetter months of winter, likely contribute to the increased crash rates during these periods. Consequently, improving road maintenance and promoting safe driving practices during winter months could help reduce the number of accidents and crashes.
Another variable worth exploring is wind speed, which was analyzed using a bar chart with average wind speed on the x-axis and the count of crashes on the y-axis. The graph indicates that the highest number of crashes occurs at wind speeds of approximately 2.5 to 4 km/h. Crashes decrease on either side of this peak and see a small spike between 5 and 6 km/h. Beyond 10 km/h, however, the number of crashes remains low. This could suggest that mild wind speeds have a subtle impact on driving conditions, such as by affecting visibility due to dust or debris. However, I feel that what is more likely is that the graph is more a chart of average wind speed than anything. The reasoning behind this pertains mostly to the fact that while extreme wind conditions do cause crashes, they are just that, extreme. Most crashes occur due to reasons outside wind speed, and as a result, the plot is somewhat irrelevant.
The final visualization I created was more out of curiosity than anything. The heat map was created by using longitudinal and latitudinal coordinates provided by the data to identify locations with high crash frequencies around the University of Colorado Boulder. This visualization could help city planners and local law enforcement pinpoint areas where additional traffic control measures, signage, or road improvements may be needed to enhance safety for drivers, cyclists, and pedestrians alike. That being said, however, the heat map only really showed what most would intuitively guess. Most crashes were centered around intersections or high-traffic areas leading in, through, and out of Boulder. This finding aligns with general expectations, as intersections and high-traffic areas typically have a higher likelihood of crashes due to the increased concentration of vehicles, pedestrians, and cyclists interacting in close proximity.
This data-driven op-ed has hopefully shed some light on the complex relationship between weather conditions and car crash incidents in Boulder County, Colorado. By examining different crash types, temperature correlations, seasonal trends, wind speed, and crash hotspots in Boulder, we can better understand how to prevent and prepare for weather conditions, their influences, and areas of potential heightened risk. According to CDOT, in the year 2022, approximately 745 people died due to motor vehicle accidents. It is crucial to ensure that we do everything we can to prevent such deaths and develop targeted prevention strategies. The findings presented in this op-ed aimed to underscore the importance of raising awareness about the impact of weather on driving safety and the need for implementing appropriate measures to mitigate the risks associated with adverse conditions. Ultimately, a data-driven approach to understanding and addressing car crashes in Boulder can help reduce the number of accidents, save lives, and contribute to a more resilient and secure community. By adopting a multifaceted strategy that includes improvements to driver education, infrastructure enhancements, and targeted interventions in high-risk areas, we can create an environment where the risk of accidents is minimized, even under the most challenging weather conditions. In conclusion, the insights gleaned from this analysis should serve as a starting point for further research and policy discussions, helping Boulder and other communities with similar challenges to make informed decisions that will promote the safety and well-being of all road users.
(1) CDOT. “CDOT 2012 Statewide Crash Book.” Colorado Department of Transportation, 2012.
