Data Visualization of Bus Breakdowns in NYC
Using data and analytics to recommend process improvements.
Project Overview
This group was tasked with discovering and analyzing a dataset in order to create visual representations and suggest recommendations for improvement. The data reviewed consisted of records of NYC school bus breakdowns based on such information as location, route, reason, and date and time.
Project Goals
Improve the NYC bus system and schedule for schools, students and parents through:
Improved tracking of bus breakdowns and delays
Improved vendor performance
Improved school and parent communications
My Role
I conducted comparative analyses of multiple aspects in an effort to find correlations, causation and patterns. Using information from these analyses, my team and I proposed recommendations for process improvement.
Methodology
The dataset consisted of over 345,000 records of school bus delays across 5 years. Information was entered manually and so cleansing was necessary. After cleansing the data, key data points were focused on for analysis:
Date, Run Type, Reason, Boro, How Long Delayed, Has Contractor Notified Schools/Parents/OPT.
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Analyses were conducted using Tableau in order to create visualizations of the data.
Findings
 Students V.S. Reason V.S. BoroThe majority of students are delayed due to heavy traffic, specifically in the Bronx, Brooklyn, Manhattan and Queens areas. |  Delays Per Month Per ReasonNo significant patterns detected for delay reasons by month. Winter months did find higher amounts of delays due to weather. |  Rate of notification by bus companyPoor compliance of bus companies to notify both schools and parents of delays. |
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 Proportioned Breakdowns for Top DelayedOnly one bus company was found to have a high proportion of breakdowns to running late for delay reason. |
Recommended Changes
A centralized bus management system, supported with GPS trackers and even face-recognition enabled cameras
Rearrangement of the school start time
Strengthening bus inspection
Redesigning routes
More and smaller buses that are closer to students
Lessons Learned
While suggestions were made based on the findings in the data, many assumptions were made to account for unknown information. Our group made recommendations first based on the present data, but allowed for understanding that not all recommendations may be feasible.
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Being able to make suggestions based on the data is easy, however, understanding there may be limitations is an important factor in presenting the legitimacy of the analysis.
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Working with the data to dig deeper than descriptive features helped me to see the value in data, but also learn to be aware of how the presentation can affect interpretation.