Day to Day Data: The Morning Commute

The Background

In the last installment of Day-to-Day Data, we discussed that we are all swimming through oceans of data and information every day. While every decision and action can seem virtually meaningless, they can also be extremely important. The trick is to have the ability to find the data that is meaningful and use it to your advantage. Keeping this idea in mind, I wanted to attempt to optimize one of my favorite periods of the day: The Morning Commute.

The Research

Without a doubt, this frustration is something that a great deal of people share. While many, including myself, enjoy what we do for a living, the morning commute can do wonders to counteract that passion. It is because of this that I wanted to streamline this process. Think of it as an operations management ploy, how many bottlenecks can I avoid in my routine? This process may seem pretty straightforward:

1. Open Web Browser
2. Navigate to maps.google.com
3. Type in start and end location
4. Select fastest route
5. Voila!

Fortunately, this is not the only step that I took to analyze this issue. It is essential to identify key data points that can be used to analyze the experience. Here are some of the key data points that I ultimately used.

1. Route
2. Start Time
3. End Time
4. Date

In order to make this data meaningful it’s important to identify why these data points are important. To most, it would seem that route and the total time spent driving are the only pieces of information that matter. However, by inserting Start Time, I will have greater visibility at the depth of my data. With the start time, I can see how the total drive time fluctuates if starting at a different period. If I wanted to dive deeper, I could compare the average speeds with the drive times that would show if I was hitting any traffic.

Below is a basic dashboard created using Tableau. Here I took a sample and tracked this data each work day for 2 work weeks. The first week of the experiment, I followed Route 1, changing my start time each day, and then repeated the same for Route 2 in Week 2.

From this dashboard, with this very small sample of data I am able to see a variety of facts concerning my commute. Immediately I have a better view of:

• How Route 1 compares to Route 2
• Average time of travel
• Shortest times of travel
• How Start Time impacted total drive time.

The Analysis

It is important to note that this sample size is indeed very small. Given weeks, months, or years of information will produce more accurate analysis overall.  In the end, the data accurately represented what I had thought to be true, with a few added surprises.

Looking at my data I can visually come to a variety of conclusions. First, Route 1 is generally faster than Route 2. Only at a 7:15 a.m. “Start Time” was Route 2 faster than Route 1. The average travel time of Route 1 was also just under 3 minutes faster than that of 2 as well.

My data also shows average times for each “Start Time”. The data clearly shows that beginning my morning journey at 7:45 a.m. will in turn present me with the longest drive time. It also tells me that the most efficient time for departure is 7:30 a.m. for both routes. Given that Route 1 is faster, and that 7:30 a.m. is the optimal departure time, strictly for efficiency it is best for me to take Route 1 at 7:30 a.m.

That conclusion, as I mentioned before, fell in line with what I had predicted. However, what I did not expect to see was the higher commute time for the 7:15 a.m. departure time. For both Route 1 and Route 2, commute times were higher. One assumption I made was that an earlier departure time could potentially lead to less traffic on the highways, as displayed by the 7:30 a.m. time. What I did not account for is the local K-12 students traveling to school in the morning. With this information I was able to find a “sweet spot” in my morning commute, where I can attempt to avoid any delays.

The Conclusion

This extremely small data sample provided me with enough information to improve my efficiency each day. With this information I can now save anywhere from 7 to 22 minutes of travel time to work each day. Meaning at minimum, I would save over 30 hours a year in travel time.

The data is always there. Data can be used to learn new things about your business, or even your personal life and decisions. The trick as always is the ability to identify what is important to you or your business and above all… How to make your data meaningful.