This project examines motor vehicle collisions in New York City, providing insights into crash frequency, spatial hotspots, temporal patterns, contributing factors, and vehicle involvement. Using Tableau, the analysis explores relationships across time, location, severity, and human behavior to uncover key trends within the data. The purpose of this project is to identify factors influencing crash occurrence and severity, highlight high-risk areas and behaviors, and provide actionable insights that can support data-driven decisions aimed at improving road safety in NYC.
Data Source :
NYC Open Data – Motor Vehicle Collisions (Crashes)
Dataset Page: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95
This dashboard provides a citywide overview of traffic accidents in New York City, highlighting overall crash volume, trends over time, and the impact on different road users. The analysis shows that NYC experiences thousands of crashes each month, with clear temporal patterns emerging across years. Pedestrians and motorists account for a significant proportion of injuries and fatalities, emphasizing the broad public safety impact of traffic accidents.
This dashboard examines how crash frequency varies by hour of day, day of week, and month. The analysis reveals that crashes peak during rush hours, particularly on weekdays, reflecting high congestion and commuter activity. Seasonal variations further indicate periods of elevated risk, suggesting that time-based interventions could play a key role in reducing accidents.
This dashboard focuses on the geographic distribution of crashes across NYC. The analysis shows that collisions are heavily concentrated in Manhattan, Brooklyn, and the Bronx, with certain intersections and corridors experiencing repeated incidents. These spatial patterns help identify priority zones where targeted safety measures and infrastructure improvements may be most effective.
This dashboard analyzes the primary factors contributing to traffic accidents. The results indicate that driver inattention and distraction are the leading causes of crashes, followed by failure to yield the right-of-way. Human behavior consistently dominates crash causation across boroughs, highlighting the importance of enforcement, education, and behavioral interventions.
This dashboard examines the role of different vehicle types in crash frequency and severity. Passenger vehicles and taxis are involved in the highest number of crashes, while larger vehicles and motorcycles show a higher likelihood of severe injuries and fatalities. The findings demonstrate that vehicle type influences both the probability of crashes and their outcomes.
The analysis in this project sheds light on the critical factors contributing to motor vehicle collisions in New York City, including temporal patterns, spatial hotspots, driver behavior, and vehicle involvement. It highlights how human factors—such as distracted driving and failure to yield—along with high-risk locations and peak traffic periods, significantly influence crash frequency and severity. These insights provide a strong foundation for data-driven planning and targeted safety interventions, supporting efforts to improve road safety, reduce accident-related injuries and fatalities, and create safer streets across NYC.