About ClearLane
Learn about our mission to protect student study time through evidence-based bus enforcement strategies.
Our Mission
To protect the vital study time of students by using data science to identify and solve bus enforcement failures that disrupt their daily commutes.
Student-Centered
Every analysis prioritizes the academic success of working students who depend on reliable transit.
Data-Driven
Evidence-based solutions using comprehensive analysis of 3.7 million violation records.
Transparent
Open methodology and findings to build trust and enable replication.
Actionable
Ready-to-implement solutions with clear ROI and measurable impact.
The Story Behind ClearLane
ClearLane started with something we all experience: that frustrating moment when you're trying to get somewhere important and everything just goes wrong. For us, it was those long BxM10 rides from the Bronx to Baruch College, where we actually got our best studying done. There's something about being on a bus that just works for focusing, you know? But here's the thing that really got us thinking. When cars would block the bus lanes and our bus got delayed, we weren't just losing travel time. We were losing our study time, that precious window where we could actually concentrate and get work done. And we started noticing this wasn't just happening to us. Other students were dealing with the same problem, day after day. So we decided to dig deeper. We had been learning data science in our classes, and we thought, why not use those skills to figure out what's really going on with these bus delays? We got our hands on 3.7 million MTA violation records and started looking for patterns. What we found was pretty eye-opening. Turns out, these enforcement failures aren't random at all. They happen in predictable places, at predictable times, and they hit students hardest when we need reliable transportation the most. It's like the system was failing us right when we needed it to work. That's how ClearLane came together. We took something that was just a daily annoyance and turned it into something bigger. We combined our personal experience with the research skills we were learning in school, and we ended up with insights that could actually help make transit policy better for everyone.
Our Research Approach
Rigorous data science methodology ensures our findings are accurate, reproducible, and actionable.
Data Collection
Comprehensive analysis of 3.7M MTA violation records, campus locations, and bus route data
Multiple data sources including NYC Open Data, MTA GTFS feeds, and institutional research
Spatial Analysis
Geographic clustering using DBSCAN to identify violation hotspots
Haversine distance calculations and buffer zone analysis around college campuses
Temporal Analysis
Time-series analysis of violation patterns by hour, day, and academic calendar
Peak violation identification during 7-10 AM student commute hours
Predictive Modeling
Machine learning models to predict high-impact enforcement opportunities
Random Forest regression with 85% accuracy in hotspot prediction
Impact Assessment
ROI analysis and speed improvement projections for targeted enforcement
12% speed improvement potential with 40% violation reduction
Meet the Team
This project was primarily created by Basir Abdul Samad. Teammates are listed with college and major.
Basir Abdul Samad
Statistics & Quantitative Modeling
Baruch College
Primary creator; led the complete research pipeline end-to-end
Albert Bagdasarov
Statistics & Quantitative Modeling
Hunter College
Built the interactive Streamlit dashboard
Caitlin Reyes
Statistics & Quantitative Modeling
Hunter College
Supported development of the Streamlit dashboard
Tenzin Namdol
Statistics & Quantitative Modeling
Baruch College
Assisted with coding tasks
Expected Impact
ClearLane's targeted enforcement approach can deliver measurable improvements for NYC transit.
What's Next
ClearLane is ready for implementation and scaling across NYC.
Pilot Program
Deploy targeted enforcement at top 10 student-impacting hotspots
Citywide Rollout
Scale to all 90 identified high-priority locations
Policy Integration
Integrate findings into MTA enforcement strategy
Research Expansion
Extend analysis to other student populations and transit modes
Ready to Make a Difference?
Join us in protecting student study time through data-driven transit enforcement.