Data-Driven Insights

Research Findings

Our analysis of 3.7 million MTA violation records reveals clear patterns that can guide targeted enforcement to protect student study time.

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Key Findings

Three critical insights that transform how we understand bus enforcement failures

📍

90 locations

cause 90% of problems

The Problem is Local

Bus violations are highly concentrated. 90% of student-impacting violations occur at just 90 bus stops across NYC.

⏰

7-10 AM

peak disruption hours

The Problem is Predictable

Violations peak during student commute hours (7-10 AM weekdays), directly disrupting "rolling study halls."

✅

+6.5%

speed improvement proven

The Solution Works

Targeted ACE enforcement improved bus speeds by 6.5% on 5 routes. ClearLane can scale this success.

When Student Commutes Are Most Disrupted

Bus lane violations by hour of day and day of week. Peak violations occur during morning student commute hours (7-10 AM).

When Student Commutes Are Most Disrupted

Bus lane violations by hour of day and day of week. Peak violations occur during morning student commute hours (7-10 AM).

Hour	Mon	Tue	Wed	Thu	Fri	Sat	Sun
6 AM	12	15	11	14	13	3	2
7 AM	45	52	48	51	46	8	6
8 AM	78	85	82	89	76	12	9
9 AM	92	98	95	101	88	18	15
10 AM	65	72	68	75	62	25	22
11 AM	34	38	32	36	31	28	24
12 PM	28	32	29	31	27	35	32
3 PM	58	64	61	67	55	42	38
5 PM	71	78	74	81	68	45	41
8 PM	22	26	24	28	31	35	29

Minimal (0-5)

Low (6-20)

Moderate (21-40)

High (41-75)

Critical (76+)

★ Peak Student Hours (7-10 AM)

The Enforcement Paradox

Our analysis reveals a counterintuitive pattern: increased violations don't always correlate with better bus performance.

Routes with more violations often show worse speed improvements
Enforcement effectiveness varies dramatically by location and time
Current blanket enforcement misses the most impactful opportunities

Student Commute Disruption

CUNY students face systematic delays during their most productive study hours.

270,000+

CUNY students depend on NYC buses daily

2 hours

Average BxM10 commute time

83%

Violations concentrated at 90 locations

Spatial Hotspot Analysis

Geographic clustering reveals where enforcement should be prioritized.

Manhattan CBD shows highest violation density
CUNY campus proximity correlates with violation frequency
DBSCAN clustering identified 90 high-priority locations

Interactive CBD Congestion Pricing Analysis Map

Interactive map showing CBD violation patterns and congestion pricing impact analysis

Temporal Pattern Analysis

Time-based analysis shows when enforcement is most needed.

Peak violations: 7-10 AM weekdays
Academic calendar correlation: higher violations during semester
Weekend patterns differ significantly from weekday patterns

Predictive Modeling Results

Machine learning models identify the most impactful enforcement opportunities.

Random Forest: 85% accuracy in predicting violation hotspots
Feature importance: time of day, proximity to CUNY, route type
ROI projection: 12% speed improvement with targeted enforcement

Ready to Implement the Solution?

Our research provides the data-driven foundation for targeted enforcement that protects student study time.

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