While living in the Washington D.C. region the past two years, I’ve been fortunate enough to always live right next to Metrorail (U Street, Pentagon City, and now NoMA) while working from home half of the time and out of 1776 (near McPherson Square and Farragut North) the other half.
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This has allowed me to take advantage of the extensive Metrorail network but it has also greatly shaped which parts of town I’m likely to visit.
Large gaps around town seem daunting to get to – only accessible by transferring from Metrorail to a long bus ride, adding a lengthy walk on either end, or just taking an Uber.
A few months ago, I (finally) started biking and it’s startling how much it has changed my perception about the best mode of transportation to get to certain parts of the city. One of the reasons why I had not biked in the past was a lack of a clear comparison to other forms of transportation that I was currently using.
A Commute Profile
My organization Conveyal has been working with Arlington County Commuter Services to build tools that pinpoint and enhance the best commuting options for the county’s residents and workers. “What’s the fastest way for me to get from A to B right now?” is what journey-planning tools solve with smartphone applications and integration with real-time transportation information. The commute planner we are building takes a more holistic approach – using factors like cost, calories burned, and number of transfers needed to make the trip.
To determine these factors and weights, we began by taking a look at multiple sets of journey profiles that are generated by OpenTripPlanner. Each profile contains the fastest biking, driving, and walking directions along with three to four public transportation options. (Bike-to-transit and drive-to-transit coming soon!) These profiles are based on transit data given a specific window of time. This allows us to include frequency and average wait times in the transit options.
Analyzing a route
The first profile that I analyzed was my current (short) daily commute. I wanted to reverse engineer the factors and weights from these results because of my strong familiarity with the routes. I know that 1160 First Street NE to 1133 15th Street NW is an ideal biking route with westward lanes on K Street for commuting in to work and slow eastward traffic on M Street heading home. There is also heavy traffic during normal commute hours (we currently don’t have the data to model car traffic in D.C.). I can regularly beat cars riding along similar routes, taking between 10 to 15 minutes to ride each way.
This route is just under two miles long, which means, on a nice day, a walk with my headphones in listening to a podcast or making a few calls can be very pleasant and would take between 25 and 30 minutes. Biking and walking these distances have the added benefit of burning significant calories, but not being far enough to be considered tiring — right in the sweet spot.
The most direct Metrorail trip is to take the Red line from NoMA to Farragut North, which, along with walking and waiting, takes 26 minutes on average. This also costs $2.10 each way during peak hours.
As a driver, I would be leaving from a garage and parking in a garage, which adds 2-3 minutes of time on each end and significant monthly or daily costs for parking. This parking cost data is available, but incomplete, so at Conveyal we use estimates based on location to determine the parking cost, in this case $10 per day at work. We can also add in the IRS mileage reimbursement as an estimate of average cost of car ownership – $1.07 each way.
Scoring the options
After using our commute planner and my current knowledge of the commute, I knew what I’d want the rankings for this route to come out to be. By default, they were ranked by just the time, similar to real-time journey planning results:
- Drive – 7 minutes
- Bike – 13 minutes
- Red Line – 26 minutes
- Walk – 29 minutes
Ideally, these options would be ranked by a score that encompasses all of the factors noted above.
To score, we start with how many minutes the route takes. This allows us to easily filter out routes that are way too roundabout or improper for the area. We can now convert all the other factors into minutes to add or subtract from the commute. To start, for each dollar we spend, let’s add five minutes to the commute.
Five minutes? If we think of a commute as a trip that’s taken 500 times per year (two times per day for 250 work days), then $1 more per trip is $500 more per year! Five minutes might not be the final number when we compare commutes across the region, but it’s a good start. This will add 55.35(!) minutes to the driving route ($1.07 cost of the drive and $10 for parking) and 10.5 minutes to the transit route ($2.10 fare).
Next, let’s factor in calories. Assuming a sedentary lifestyle in D.C. isn’t absurd. A large population of residents in the area work in office jobs and sit six-plus hours per day. There’s a level of calorie burning that is going to have a positive effect on our lives that does not adversely affect our day-to-day energy levels and will actually improve health over time. For now, we’ll use a simple factor, but in the future, we’ll use a scaling function to show the diminishing return and eventual negative value (for the average commuter) of too many calories burned on a commute.
I found a simple estimate that we burn 4.4 calories per minute of brisk walking and 10 calories per minute of easy biking. These lead to 130 calories burned on the bike route and 157 calories burned while walking. The yearly totals come out to be 65,000 calories burned biking and 78,500 calories burned walking! That’s the equivalent of running almost 20 marathons each year. (Calculations using my body weight and a five-hour marathon pace.)
For this commute, I’m going to value every 100 calories at three minutes of time. Biking would take off 3.9 minutes, and walking would take off 4.71 minutes.
- Bike – 13 minutes, 9.1 score
- Walk – 29 minutes, 24.29 score
- Red Line – 26 minutes, 36.5 score
- Drive – 7 minutes, 62.35 score
Our commute planner also factors in CO2 emissions or offsets, pain of a transfer, time to park a bike, and time to park a car. Some are cut-and-dry factors like the ones demonstrated above and others are functions that vary depending on the input.
This detailed analysis and comparison is what we are trying to convey in a simple and quick manner with our commute planner. We want to make it easy to come to the same conclusion that I did without ever having gone to or from a destination before, or to supplement your current knowledge of routes with new information and simple analysis that could easily enhance your commutes in the future.
The next step is to analyze thousands of more commutes across the Washington D.C. region and aggregate statistics about how each factor affects a given commute.