About This Dashboard

This dashboard presents an analysis of traffic crashes near bus stops in New York City. The data focuses on transportation safety, an important area of research with direct implications for public safety, urban planning, and transit design.

The visualizations in this dashboard explore:

• Spatial distribution of crashes across NYC boroughs
• Temporal patterns of crash occurrences
• Primary contributing factors to crashes
• Types of vehicles involved in crashes
• Relationship between location, time, and crash severity

Each visualization includes observations about what the data reveals and why these insights are important for transportation safety planning.

This analysis is part of research conducted in collaboration with the AI & Mobility Research Lab.

Academic Context: This dashboard was created as part of the midterm project for ECO B2100: Foundations of Empirical Research at The City College of New York (CCNY), taught by Professor John Schmitz.

Dataset Overview

NYC Bus Stop Crashes: High-Contrast Categorical Map

Analysis & Insights

What this map shows: This map displays the geographic distribution of crashes near bus stops across New York City, with colors indicating the severity of each crash based on the number of persons injured.

Why this is interesting:

• The map reveals clear spatial patterns of crash severity, with certain neighborhoods showing clusters of higher-severity crashes
• Areas with higher density of severe crashes indicate locations where safety interventions should be prioritized
• The correlation between bus stop locations and crash severity provides valuable information for transit safety planning

Key insights:

• Crashes concentrate along major transportation corridors
• Some areas show distinct patterns of high-severity crashes, which warrant further investigation
• The spatial distribution helps identify potentially dangerous bus stop locations that need redesign or additional safety measures

Severity Categories:

• Light Blue: No injuries (50.7%)
• Green: 1-2 injuries (47.4%)
• Orange: 3-5 injuries (1.6%)
• Red: 6+ injuries (0.3%)

Crashes by Borough

Observations

What this map shows: This visualization displays the spatial distribution of traffic crashes across different boroughs in New York City, with each borough represented by a distinct color.

Why this is interesting:

• Comparing crash distributions between boroughs helps identify areas with higher crash densities that might need targeted interventions
• The visualization highlights differences in crash patterns across the city’s diverse neighborhoods
• Borough-level analysis is useful for allocating safety resources and developing borough-specific safety strategies

Key insights:

• Some boroughs show greater concentration of crashes than others
• The pattern of crashes varies significantly between boroughs, suggesting different underlying factors affecting safety
• The distribution can help transit authorities prioritize which boroughs need more immediate attention for safety improvements around bus stops

Crashes Over Time

Observations

What this chart shows: This line graph shows the temporal trends of crash frequency across the dataset period, helping identify seasonal patterns or overall increases/decreases in crash rates.

Why this is interesting:

• Temporal trends reveal whether crashes are becoming more or less frequent over time
• Seasonal patterns indicate times of year when additional safety measures are needed
• Spikes in crash frequency correlate with external factors like weather events, holidays, or special events

Key insights:

• The chart reveals cyclical patterns in crash frequency
• Understanding temporal trends allows for targeted safety campaigns during high-risk periods
• Long-term trends help evaluate the effectiveness of safety measures implemented over time

Crashes by Time of Day and Borough

Observations

What this chart shows: This faceted plot demonstrates how crash patterns shift throughout the day (night, morning, afternoon, evening) across different boroughs, highlighting temporal risk patterns by location.

Why this is interesting:

• The time-of-day analysis reveals when crashes are most likely to occur in each borough
• Comparing boroughs helps identify if certain areas have different temporal risk profiles
• This information is crucial for scheduling traffic enforcement and planning transit service adjustments

Key insights:

• Crash patterns vary significantly by time of day, with some periods showing notably higher crash rates
• Each borough shows a distinct temporal pattern, suggesting different underlying factors
• Afternoons and evenings typically show higher crash frequencies, related to rush hour congestion
• Understanding these patterns helps prioritize when and where to implement safety measures to maximize their impact

Top 10 Contributing Factors to Crashes

Observations

What this chart shows: This horizontal bar chart ranks the most common reasons for crashes, revealing that factors like driver inattention and failure to yield right-of-way are major contributors to traffic incidents.

Why this is interesting:

• Understanding the primary causes of crashes helps target safety education and enforcement
• The ranking of factors helps prioritize which issues to address first
• This information guides the design of bus stops and surrounding infrastructure to mitigate specific risk factors

Key insights:

• Driver behavior is a dominant factor in crashes near bus stops
• Distracted driving and failure to yield are the leading causes
• The distribution of contributing factors informs targeted safety campaigns and driver education initiatives
• Infrastructure improvements should address the most common contributing factors (e.g., improved visibility at locations with high “failure to yield” crashes)

Contributing Factors by Borough

Observations

What this chart shows: This faceted bar chart reveals how different contributing factors to crashes vary across the five boroughs of New York City, showing borough-specific patterns in crash causes.

Why this is interesting:

• The chart exposes whether certain boroughs have unique risk profiles
• It helps determine if safety interventions should be tailored to specific areas
• The variation across boroughs reveals underlying differences in road design, traffic patterns, or enforcement

Key insights:

• The relative importance of each contributing factor varies significantly by borough
• Some boroughs have disproportionately high injury rates for specific factors
• These differences indicate the need for borough-specific safety strategies rather than one-size-fits-all approaches
• The visualization identifies which contributing factors are most dangerous (causing more injuries) in each area

Top 10 Vehicle Types Involved in Crashes

Observations

What this chart shows: This horizontal bar chart identifies which types of vehicles are most frequently involved in crashes near bus stops in NYC, with passenger vehicles dominating the distribution.

Why this is interesting:

• Understanding which vehicles are most commonly involved in crashes helps target safety messages to specific driver groups
• The distribution reveals particular vehicle types that have higher crash risks near bus stops
• This information informs bus stop design to account for interactions with the most common vehicle types

Key insights:

• Passenger vehicles and SUVs are the most common vehicles involved in crashes
• Commercial vehicles (taxis, trucks) also show significant involvement in crashes near bus stops
• The vehicle type distribution identifies which driver populations should be targeted for safety education
• Bus stop design should consider the characteristics of frequently-involved vehicle types (e.g., visibility lines for taller vehicles)

About This Project

Purpose: This dashboard explores patterns and factors related to traffic crashes near bus stops in New York City. The analysis aims to identify key safety concerns and potential areas for intervention to improve transportation safety.

Data Source: The data used in this analysis comes from crash records in New York City, specifically filtered to show crashes occurring near bus stops. This provides valuable insights into the safety challenges at these important transit connection points.

Academic Context: This dashboard was developed as a midterm project for ECO B2100: Foundations of Empirical Research at The City College of New York (CCNY) under the guidance of Professor John Schmitz. The project demonstrates the application of data visualization techniques to transportation safety research.

Research Significance: Transportation safety is a critical public health and urban planning concern. By analyzing crash patterns near bus stops, we can better understand:

• How transit infrastructure influences safety outcomes
• When and where crashes are most likely to occur
• What factors contribute most frequently to crashes
• Which vehicle types are commonly involved

Lab Affiliation: This work is associated with the AI & Mobility Research Lab, which focuses on transportation safety research and urban mobility analysis.

Next Steps: Further research will include:

• Detailed analysis of high-crash locations
• Evaluation of bus stop design features and their relationship to crash rates
• Development of targeted safety interventions based on the identified patterns
• Comparative analysis with crashes not near bus stops to isolate bus stop-specific factors

Analysis Methods

This dashboard was created using R and various packages for data visualization and spatial analysis. The methods included:

1. Spatial analysis to examine geographic patterns in crash locations
2. Temporal analysis to identify trends and patterns over time
3. Categorical analysis to understand contributing factors and vehicle types
4. Comparative analysis across boroughs to identify local patterns

The analysis workflow involved: - Loading and preprocessing the spatial data - Creating visualizations to explore different aspects of the data - Identifying patterns and relationships - Documenting observations and insights

All code for this analysis is available in the R Markdown file used to generate this dashboard.