7:00 a.m.
Good morning! I start off the day woken up by the cries of a very hungry tiny human. This tiny human is none other than my one-year-old son, who needs an early morning feeding after a long night of rest. After that, he will fall right back to sleep, and that is when I truly start my day.
7:20 a.m.
I take a quick shower and make myself breakfast as I catch up on some topline news for the day. Today’s breakfast consists of a bowl of Rice Krispies with some fruit and a morning cup of coffee.
8:00 a.m.
I head out to catch my personal favorite bus route, the SIM4. I hop on the bus, which I’ve noticed has been fuller during my commute to work since the launch of Congestion Pricing. That aligns with our data that shows a 6% increase in overall weekday express bus ridership, with a 10% increase on the SIM4 specifically, when compared to the same time last year. Despite the increase, there’s still room for me to grab a seat for my ride into the city. I get off at Bowling Green to head into the MTAHQ office to start the day.
9:00 a.m.
I always start the day off by checking emails and calendar so that I can plan out my work tasks for the day. Since my earliest meeting today is at 10:30 a.m., I start my day off with a data pull for the Revenue Finance team to get revenue data needed to close on Congestion Pricing for the month. This is when the Revenue team calculates the revenue collected for the previous month of this program. After the data pull, I spend some time putting together some visuals for a deck I am preparing to present for a new revenue dataset that I ingested into the MTA Data Lake.
10:30 a.m.
Time for our daily team stand-up where we go over some top level asks and what everyone’s work tasks are for the day. Today, our team manager, Matt Yarri, presented a couple of slides I’ve put together recently about the reliability improvements we’ve seen since the launch of Congestion Pricing for some Manhattan crosstown bus routes. Fun fact: In the first 2 months of 2024, 22% of M42 runs finished their route with more than 5 minutes of delay. Since the start Congestion Pricing, this has fallen to 13%. This is something that our team has tracked closely since the launch of this program, and we’re very excited that we can already measure how our customers are now experiencing better service due to this program.
11:00 a.m.
My next meeting won’t be until 2 p.m. so I begin to work on some other work tasks. I start by writing up a data pipeline to ingest from our vendor a detailed dataset of every trip that occurred in the Central Business District and the toll amount associated with the trip. This dataset enables core members to have a clear understanding of how the program is currently performing, and is working smoothly.
12:00 p.m.
It’s lunch time! I usually bring food from home but today I decided to grab some pizza instead. I went to the Grotto, which, in my opinion, has the best pizza in the neighborhood. This is also when I use my time to take a walk around the neighborhood to stretch my legs and be away from the computer screen for a little bit.
12:30 p.m.
After lunch, I hit the task to build reporting dashboards in Mode Analytics (the pictured visualization tool) to go over some high-level metrics on how the Congestion Pricing program is going. Did you know that since the program launched, there are 80,000 fewer vehicles entering the CBD per day, 560,000 fewer vehicles per week, and 6 million fewer vehicles since launch? These measures come from the methodology and data infrastructure our team built, which updates for the public each week. You can track these metrics yourself.
2:00 p.m.
Presentation time! Currently I am presenting the data I’ve put together for the Revenue Finance group and showing them how to use this data. I’ve also created some high-level visualizations to display the ability of this dataset. I emphasize to the team that the dataset will allow the team to better understand the current progress of revenue collection. It will enable the team to easily create focus points on specific initiatives and interventions that can potentially have the best rate of return.
3:00 p.m.
The presentation went well. The participants seemed eager to use the data that is now available and have already scheduled follow up meetings to take deeper dive into the SQL scripts I’ve put together for them. I continue to work on knocking out some ongoing tickets that I have open along with some code review for a couple of merge requests that are in my queue. Our team has a custom where any new code that is ready to be put into production must be reviewed by a Data Scientist and Data Engineer before it is released.
5:00 p.m.
Time to go home to start my second job of being a father to this child!
About the author
Jack Hui is a Senior Data Scientist of the MTA Data & Analytics team. As a born and raised New Yorker who spent a lot of time in the CBD, he is ecstatic to see Congestion Pricing driving congestion down and the benefits it brought to the city he grew up in.