Subway riders cringe when a delay is announced. Emotions range from mild annoyance to outbursts of anger. Delays inconvenience us and reduce productivity. By default we blame subway management (TTC in Toronto). Is this fair? Wouldn’t it be nice if we had data to determine what causes the delays?
Toronto Star Article
This headline caught my attention: “What’s keeping that train? The TTC has 69,000 reasons why”.
I’ve been using the subway since 2000. I’ve been curious to learn more about TTC (subway) delays.
The transit agency receives so many requests for data it finally decided to make the statistics available on the 69,000 delays from January 2014 to April 2017.
“Palmer said people go onto the track for different reasons, including to urinate, to retrieve something they dropped, or on a dare.”
What? I can understand accidentally falling onto the tracks but intentionally doing it? It gets worse..
That could be urine, it could be feces, it could be vomit,” said Palmer. “New Year’s Eve…we had extra crews on just cleaning up vomit and nothing else. Pretty much every train that went to Finch had vomit in one or more cars.”
The majority of us are civilized. It’s a tiny minority of riders that cause these completely unnecessary delays. Other ‘people’ delays are caused by unforeseen health issues and injuries. Aren’t you glad that the TTC stops the subway to look after us? There are also delays due to maintenance issues.
The Star article summarized the issues nicely at the individual delay level but I was curious about the higher level categories. Let’s take a look at the data.
- Subway&SRT Logs(Jan01_14 to April30_17).xlsx main data set. 69043 rows, 3.5 MB
- Subway&SRT Log Codes.xlsx delay codes and description
Note: ‘Subway‘ is the main network. ‘SRT‘ is a smaller line (Scarborough Regional Transit).
My Excel File
I combined the two main Excel files added formulas and pivots: my Excel file. (10MB Excel file)
I wanted to see the results at a higher level so I created these categories:
Passenger, Mechanical, Debris, Maintenance, TTC employee, TTC management, Weather, Fire Smoke
Sheets in my mini model:
- Kevin’s Delay Categories Detailed delay codes will map up to these 9 high level categories
- Subway&SRT Delay Codes I selected high level category for each delay code (columns E & J)
- Delay Data Original data in columns A to J. Formulas to determine mapping (columns K to M)
- Pivot Tables I role delay codes up to my 9 categories (count & time) to get bird’s eye view!
After mapping individual delay items to my categories these two pivots tell us a lot:
The ‘Passenger’ category is the highest for both ‘Count of Delays’ and ‘Time of Delays’. It’s still important to examine and improve all other delay categories, but we shouldn’t blindly blame the TTC for everything. If you run to catch the train and get stuck in the door that is a delay affecting thousands of people and you are part of the data!
The data reveals a lot of details about what really goes on inside the subway. There are a lot of people working very hard to (a) keep us safe and (b) improve the TTC. I’ve been personally guilty of blaming the TTC when delays are often caused by us the passengers. The TTC isn’t perfect and we need more funding for maintenance improvements and expansion, but over the past few years they have focused on customer service and reduced delays.