One of the things I love about Vancouver is its public transportation system, Translink. I grew up loving trains, and so it only seemed natural that riding the Skytrain be one of the funnest things I have experienced when I first came to Vancouver. It has been around two years since I have moved here and I still use it to go everywhere in and out of the Vancouver area. A cool feature of the Translink system is the Compass Card, a re-loadable fare pass in which frequent riders will use to tap themselves on and off the transit system through fare gates. Part of the reason why I love the idea of tapping on and off the fare gates or on bus rides is because of how the system records data of where and when you have tapped.
The thought of Translink’s ability to easily conduct commuter analysis using the millions of data recorded everyday for strategic pricing and vehicle allocation is intriguing. As such, this is what motivates riders like me to analyze my personal rider behavior. Conveniently, the Compass Card website allows you to download your own personal .csv file. The file contains lines of transactions representing every single time you have tapped on and off the system.
The motivation behind this post is to showcase some data analysis. I would love to present what I have learned about my transit behavior between September 2016 and August 2017 using Tableau Public. For any Pokemon fans out there, visualizations take on a Charizard colour palette.
On average, I began my travels with the bus 2.7 times more than the train each month. Equivalently, the bus began 73% of my trips.
This makes sense as the bus begins my commute to almost everywhere I go when I begin at home. It is interesting to see that my ridership has consistently increased up until the second quarter in 2017. The slight kink in the graph is due to the fact that I spent most of the month of May 2017 travelling (I went to Japan for the first time!)
I used the transit system in 289 out of 365 days and most days, I took 2-3 trips.
Here, I defined a trip as one where I would be required to make a new full fare payment. It is possible that multiple forms of transit may be used within an hour and a half time interval before having to pay again. These potential ways of transferring between types of transit (ie. bus to a train) are not considered as trips.
I tried avoiding the morning transit rush. I am more likely to use transit during evening rush hour. Weekend usage often starts in the late morning.
This huge morning spread in my transit usage behavior reflects my choice to go to the gym before I go to work, especially during the spring and summer seasons when it gets brighter outside earlier in the day. Therefore, I can begin using transit as early as 5:00am! Also, being given a flexible work schedule, sometimes I choose to head to work as late as anywhere between 8:00am and 9:00am.
I often go out Friday and Saturday evenings and as much as I love taking transit, there is no clear increase in transit usage behavior during this time because depending on the activity, I may already be in walking distance of what I want to do, or transit may not be my ideal form of transportation.
I saved money getting a Zone 1 Monthly Pass at $91.00 with my behavior! I would have spent on average, $103.00 a month on individual fares.
Often times, I don’t think about how many times I tap on and off the system and overlook what I would be paying if I did not have a monthly pass. This is full proof that getting a zone 1 monthly pass is worth it as a frequent transit user and I do not have to worry about other financial alternatives.
If I was more nit-picky, I could definitely save more money by not getting the monthly pass during the months where it would not be worth it. For example, every December, I fly out to Toronto for two weeks to visit family for the holidays. One might think that this could signal a behavioral change to pay closer attention to my budget allocation towards public transit. In reality, I actually prefer not having to worry about loading my compass card every month. Hence, I have it set to auto-load where the system automatically charges my credit card and loads a monthly pass to my compass card.
This analysis was a great introductory way for me to explore Tableau as an analytical tool. I will definitely be using it more often to create vibrant visualizations and hone in on insights from interesting data. Some future considerations I have for these kinds of analysis is to utilize maps and locations to enhance the visualization and stories behind transit data. In this particular case, almost all of my trips began in Vancouver and rarely in any other surrounding city so geographic visuals may have not been much use.
Another future consideration is to augment the existing transit data with other data sources such as the distances traveled using transit possibly obtained from Google Maps for example. Some analysis on how much it would cost per kilometer traveled or personal summary statistics on distances traveled also sounds interesting.
Amidst the world of available data in everyday life, one last future consideration is that the next time you tap off the transit system, think about how that is one more data point for your next analysis!