T-ventures: Personal Adventures on the T

By Aneesh Agrawal, Jane Coffrin, and Catherine Caruso

The data say that T riders have a lot of strong, different ideas, questions and feelings about the future of transportation in Boston, but there are many T riders who still are not making their voices heard. We want to tell this story because we love the city of Boston and want to make sure everyone has a say during the policy planning process. For example, the dataset GoBoston2030 collected last January during record snowfall has over 200 questions related to snow and inclement weather, making it clear that Boston transit needs modernization to handle the needs of citizens who weren’t able to make it to work. One respondent asked:

“If I don’t get to work, I don’t get paid. I don’t have a car and rely on the T. Is it possible to build/redesign a public transportation model that does NOT need to be shut completely down because of snow? When will the MBTA see management and operations that plan and prepare for the most common obstacles?”

Our audience is the citizens of Boston, both those who currently use public transit as well as those who will use public transportation in the future. Last January, the city of Boston launched the GoBoston2030 campaign, which aims to use citizen feedback to guide the planning process for the next 15 years of Boston’s transit system. Gathering continuous input is essential to making this process a success. Our goal is to engage our audience in conversation by getting them to tweet @GoBoston2030.

We started our data exploration with a list of over 5000 questions from Boston area residents compiled by GoBoston 2030. The questions have been categorized and broken down by station, making it easy to perform an initial analysis of the data.

Here’s a sampling of the question set:

  • What is the plan to update the T in the next 10 years?
  • Why can’t the T work like a ski lift? Constant flow of cars that slow to near stop on platforms for loading, keep moving to end of the platform, and immediately take off again for next stop. Then, a new car arrives just as the last one is leaving.
  • How can we make the T more affordable?
  • Will there be massage chairs on the bus and train?
  • How can we make the T more convenient for parents with strollers/young children?

Some of these are whimsical, while others are more pragmatic. The question database is a great starting point, but it’s fairly large and impersonal, so we wanted to put faces to the feedback. Although GoBoston2030’s initial data collection period is over, with their Action Plan scheduled to be released this summer we wanted to highlight how Boston citizens can continue to engage in the process in a simple way, and make their voices heard.

We gathered personal stories from local T riders via short interviews and compiled them into a short video, with the goal of building momentum for aggregating feedback that can extend to viewers. We used interview clips of feedback from real T riders to make our message more personable, and show our viewers that T riders are already making their voices heard, and they should do the same . A video is also a highly appropriate media format for GoBoston2030 to share directly on Twitter, which will help drive the call to action for riders to tweet back @GoBoston2030. Keeping the video short makes it more likely viewers will watch to the end of the video, and including humorous clips such as the doors closing on an unlucky rider helps build empathy and a connection with the audience.

This video could be a prototype for a series of short videos released periodically by GoBoston 2030 throughout the planning process. Follow up videos could include more interviews, longer interviews, and more parts of Boston’s public transportation system.

Activity Log for Monday, February 8th, 2016

  • Entering buildings tracks usage of my MIT ID to tap or swipe in
  • Ate lunch at Maseeh, swiped my ID to keep track of my meal plan
  • GPS tracking was enabled on my phone in the morning (I turned it off after remembering about this assignment)
  • Called my family after dinner
  • Various websites tracked me online as I surfed (I use a bevy of add-ons to block as many as I can)
  • Wrote a bunch of code: this involves making commits and pushing (uploading) them to an online repository (GitHub.com and GitLab.com)
  • Sent text messages to friends and family via Signal (which performs opportunistic encryption)
  • Watched a TV show in my floor lounge
  • Sent a variety of emails, archived and deleted other emails

Divergence between Human State Assumption and Actual Aircraft System State

One of my Graduate Resident Tutors (GRTs), Sathya Silva, gave her thesis defense yesterday on the topic of divergence between human assumptions and actual state in aircraft. In short, the divergence framework she built examines how differences between pilots’ mental models of the state of the aircraft they are flying (i.e. throttle setting, landing gear position, etc.) and the actual state contribute to accidents and postulates mitigations for these divergence events. Her presentation (slides here) drew on a variety of sources, including previous research and aircraft incident reports. I’d like to focus on slide 33 in particular:


This slide comes after more detailed analysis of a number of incidents during flight and shows visually timing information for each incident in the divergence framework. The use of lines that split and come together again, with visual markers for recovery time, loss of control time, and impact times, highlight the extent of divergence in each case, any subsequent re-convergence, and the relative timing in events. One of the main takeaways from this aggregate view is that in many cases the difference between fatal and non-fatal incidents was not that convergence did not happen, but that it it happened without enough time left for a full recovery, proving insight into a possible mitigation strategy.

The audience for this defense was her thesis committee, as well as friends and family attending the defense. In addition to Sathya’s main goal of passing the defense, the presentation was meant to lay out the concepts behind divergence to provide evidence for the thesis; slide 33 in particular highlights patterns in these incidents in the context of the divergence framework. The presentation overall was effective in achieving its goals; the novel divergence visualizations provide at-a-glance comprehension in the divergence framework and demonstrate its effectiveness in highlight patterns. The thesis committee agreed that this was a great presentation – congratulations Dr. Silva on passing your thesis defense!