On the Fourth of July, while fireworks lit up the night sky, we were interested in a different kind of boom – the surge of cyclists on America’s bike paths. Our mission was to answer a question: What impact would the holiday have on bicycle traffic? Our hypothesis: More people would use recreational bike paths because of the holiday, while fewer would use commuter bike paths since they didn’t need to go to work.

Photo by frank mckenna on Unsplash

With a mix of curiosity and excitement, we watched, as we’ve always loved to do, the data. With a bit of analysis and the consistent capturing of the flow of cyclists from our counters, we could paint a picture of how Independence Day caused a single-day change in biking habits in 2024. Dive into our findings to discover how this holiday temporarily altered the pattern of pedal-powered movement in the US. 

SETUP

Before diving into our study, we had to answer two main questions. Namely: 

1. How would we define the difference between a commuter and a recreational path? 
2. Which counters, out of the 1000s we have across the US, should we choose for this study? 

First up, question one. What makes a path commuter or recreational? Well, in a nutshell, it depends on their hourly peaks.

As seen on the graphs above, commuter and recreational paths have different shapes when graphed by their average counts per hour of the day. A commuter path has very pronounced peaks in the morning (~9 am) and evening (~5 pm). This happens since most people go to work, school, etc. and head home at those times. On the other hand, a recreational path has less articulated peaks and a smoother shape. This is because as people who cycle for fun go at various hours of the day. 

Next, question two. To answer this question, we decided to narrow down our field of focus. So, we selected 163 counters in urban environments to create a more homogenous sample. This meant counters in cities or towns – with a total of 70 commuter sites and 93 recreational sites. Another critical but often overlooked criterion was the completeness and quality of the data. We chose each of the 163 counters after seeing that their historical data was of high quality and consistency.

One last important piece of the setup: these trends for commuter and recreational paths are true for an average weekday, not necessarily a weekend. Since the Fourth of July occurred on a Thursday this year, we focused on weekday counts for our analyses. 

METHOD

With everything set up, it was time to look at the data. To conduct our study, we looked at bike counts on July 4th, 2024, and compared them with counts from the two preceding and two following Thursdays. This way, we could compare Independence Day’s counts to an average Thursday in June or July. 

Treating July 4th as one group and the four surrounding Thursdays as another, we created average hourly profiles at recreational and commuter sites for both groups and graphed them. 

OBSERVATIONS

Commuter paths had more volume than recreational paths, but both followed a similar trend

• Commuter paths had an average daily bike count of 626, while recreational paths had an average daily bike count of 421. Therefore, there was more bike traffic on commuter paths compared to recreational paths by almost 50%. This fact was surprising since it wasn’t a workday. That said, it might be because Americans in urban areas used commuter paths to bike to their Fourth of July festivities. 
• Bike volumes on both commuter and recreational sites peaked at 11 am. This trend was very different from the usual peaks in the morning and evening, reflecting that people likely decided to sleep in and start their day later. 
• Finally, both commuter and recreational sites looked very different from the ebbs and flows of a typical Thursday. The Fourth of July completely turned the pattern of both on their heads, so much that they resembled each other a lot instead! 

People continued biking on commuter paths during the holiday

• On the commuter paths we analyzed, there was a slight 6% decrease in bike volumes on July 4th compared to the surrounding four Thursdays. This figure was probably due to people not traveling to and from work, school, and other places like a usual weekday. Nevertheless, it’s notable that ridership remained more or less steady.
• A typical Thursday’s hourly profile has peaks in the morning and late afternoon. However, the average hourly profile on July 4th resembled an hourly profile for a recreational site. As seen in the graph above, this meant less drastic peaks and an overall rounder shape. 
• Interestingly, there’s an unusual spike in counts at 10 pm on July 4th. Our theory? People were rushing to see the fireworks! 

Recreational paths saw a huge increase in ridership

• Overall, there was a whopping 48% increase in bike volumes on recreational paths on July 4th compared to the surrounding Thursdays. Many people took the opportunity to go on a fun bike ride for the Fourth of July! 
• If we narrow it down, it gets even more drastic. From 8 am to 5 pm, there was an 85% increase in bike volumes compared to a typical Thursday. 
• A typical Thursday for recreational sites has a slight morning and late afternoon bump. In contrast, the average hourly profile on July 4th showed a peak at 11 am, followed by a steady decrease in volumes throughout the day. 

CONCLUSION 

Our analysis of bicycle traffic on July 4th confirmed our hypothesis: recreational bike paths saw a significant uptick in use, while commuter paths experienced a slight decrease. What we didn’t expect though, was that commuter paths would have almost 50% more volume than recreational paths. These findings led us to even more conclusions:

• Commuter paths are used for more than just commutes. They also appear to serve as a means to access all sorts of activities, even fun ones on holidays.
• We can see a demand for a more connected networks. It seems that recreational riders use commuter paths to get to their desired destinations and back.
• Since commuter facilities are used much like recreational facilities during holidays, these paths should be designed for all ages and abilities much like their recreational counterparts.

The one lesson to take away from this study is that data tells stories. With the correct analysis and interpretation, count data can prove what you see or discover behavior you would never expect. With that, clean and complete datasets are vital if you want to draw conclusions that you can be confident in sharing. As we look forward to future holidays and events, we can’t wait to explore how people’s biking habits change! 

Interested in knowing more about this study? Reach out to our data team via our contact us page.

If you’re interested in going father with count data, explore our data analysis software, Eco-Visio.