In an effort led by Green Building Alliance, Envision Downtown and other Pittsburgh stakeholders partnered to deliver an extensive commuter survey asking over 20,000 people who work in Pittsburgh detailed questions about their commuting habits. We can use the final results from the Make My Trip Count Survey to unpack the nature of commuting trends affecting the Downtown area.
From the data we can see a higher percentage of respondents take public transit than any other mode to get Downtown each morning (44.8%). Driving alone is still a popular option, but an impressive 5% of respondents choose people-powered modes with biking and walking coming in at 2.5% each.
NOTE: for the graphic above, we combined bus, light rail, and park & ride into one Public Transit category but left the car-centric modes separate.
These maps show the five best performing zip codes for each mode of transit for people who just work within the Golden Triangle. By utilizing a geographic approach, we can form spatial connections that correlate to what we already know about the region. The data shows that the combined Downtown and Strip District zip code, 15222, is a top performer for walking and biking to work, while the East End of Pittsburgh (where the East Busway runs) outpaces all other zip codes in commuting by bus.
What can we learn?
Greater Downtown may have almost 50,000 off street parking spaces, but it also serves as the hub for our regional public transit. This information is impactful because it shows that a good number of Pittsburghers are already using the existing transit infrastructure daily, which reinforces the importance of making the commuter experience better for everyone.
The maps that show clusters of heavy use are especially helpful in the process of reverse engineering the reasons why certain neighborhoods outperform others. By narrowing down our focus in this way we can note trends, understand the areas where improvements can be made, and form educated guesses about the habits of people who work Downtown. As we continue to work with each part of this large and useful dataset, the better informed our decision making will become. The results will tell us a lot about the way people come in and out of the city and further analysis of these daily patterns will guide our future efforts to improve mobility networks in Downtown Pittsburgh.