Introduction: Crash Reporting in Montgomery County, Maryland (2015-2023)

• The analysis of traffic collision data is crucial for understanding the dynamics of road safety and implementing effective measures to reduce accidents and their associated impacts. In this report, I use the “Crash Report Drivers Data of Montgomery County, Maryland” data set. This report try to reveal the key factors driving on these accidents and proposing actionable recommendations to mitigate their occurrence and impacts.

• The “Crash Report Drivers Data of Montgomery County, Maryland” data set provides a detailed overview of traffic collisions involving motor vehicle operators throughout the county. The data has been collected by the Automated Crash Reporting System (ACRS) managed by the Maryland State Police and reported by multiple law enforcement agencies. Available for download at https://catalog.data.gov/dataset/crash-reporting-drivers-data

Exploring Data set

• Important features:
  • ACRS.Report.Type : Fatal Crash, Injury Crash, Property Damage Crash
  • Crash.Date.Time & Light : date time and light condition
  • Route.Type, Cross.Street.Type, Traffic Control : specific location and traffic control
  • Weather & Surface Condition : Clear, Cloudy, Raining, Snow…
  • Collision Type : Angle, Head-on, Rear-end, Sideswipe…
  • Driver Substance Abuse : Alcohol, Medication, Illegal Drug
  • Injury Severity :Fatal Injury, No Injury, Possible Injury, Suspected Minor Injury, Suspected Serious Injury
  • Circumstance (140746/172105 fields has N/A data)
  • Driver Distracted By : phone activity or other distract (many categories, have to grouping or aggregating categories, or using Top-N Analysis)
  • Driver License State

What’s interesting in that data set:

• This data set has a lot of information, When looking at this, we can uncover the trends and relationships between various factors that contribute to collisions. The exploration can provide crucial insights for improving road safety within Montgomery County

Challenges

• Some features have a lot of categories, some features have a lot of N/A information, also these collision reports are based on preliminary information, so information may include verified and unverified collision data, information may include mechanical or human error.

• Feature has a lot of category, some category has the same meaning. Some category data is free input by law enforcement agencies so they are different name but same meaning. It need to consolidate.

Question to answer

Identify Trends:

• Seasonal Variations: Look for patterns related to the date. Are there more crashes during specific times of the year (e.g., winter, night times)?
• Weather Conditions: Correlate weather data with crashes. Does rain, snow, or fog increase the frequency of accidents?
• Collision Type and Location: Analyze the distribution of collision types (rear-end, head-on, etc.) across different locations. Are certain intersections or road types more prone to specific types of crashes?
• Traffic Control and Violations: See if disregarding traffic signals or signage plays a role in crashes.
• Vehicle Movement and Speed: Investigate if speeding or unsafe maneuvers (like higher speed limit) are contributing factors.
• Surface Condition: Identify if poor road conditions (wet, icy, potholes) are linked to crashes.

Driver Behavior:

• Distraction Analysis: Understand how different types of distractions (phone use, eating, etc.) affect crash risk.
• Impaired Driving: See if the data suggests a correlation between crashes and factors like driving under the influence of alcohol or drugs.

Outcome: Focus on Safety Improvements:

• Use the analysis to identify areas for improvement in infrastructure, traffic management, and driver education programs.
• Target interventions (e.g., public awareness campaigns) towards specific types of driver distractions or risky behaviors.

1. Identify Trend

• When are the most dangerous times to be driving

• According to the graph, the most dangerous time to drive is on Monday - Friday between 3:00 PM and 5:00 PM, when the number of car accidents is highest. There is a secondary peak in car accidents around 8:00 AM. It is generally safer to drive early in the morning or later at night.

• An alternative perspective on examining the frequency of traffic accidents by hour on weekdays is by observing a heatmap, which yields comparable findings to the previously analyzed chart.

• Drawing from everyday life observations, individuals typically go to work or school around 8:00 a.m. and begin go back home between 3:00 p.m. to 5:00 p.m. Consequently, more vehicle are on road, which increased collision rates.

• However, this analysis extends beyond time patterns, recognizing various additional factors influencing traffic accidents, including weather conditions, instances of intoxicated driving, and distracted drivers.

2. Distribution Of Accident 2015 - 2023

• The next thing I want to look for is seasonal pattern. I then examined whether there was a seasonal trend. When I organized the incidents by date and plotted them (see graph below), I noticed that although there’s a bit more variation during winter, the overall rate remains fairly consistent throughout the year.

• One point of interest is in late December. These are the three days of the year with the lowest number of crashes: Christmas Day, Boxing Day, and New Year’s Eve.

3. Accident Severity by Year

• The total number of traffic accidents declined during the COVID-19 period. Subsequently, post-COVID, there was an uptick in accidents, accompanied by a steady rise in injury rates.

4. Weather Condition

• The majority of accidents occurred under clear weather condition, while rainy and cloudy condition contribute almost the same proportion. A minimal accident proportion were record during snow condition. In each type of weather the proportion of accident severity is the same which is 36% to 39% of accident involved in injury.

5. Surface Conditions

• The majority 78.7% of accidents happened on dry surfaces while Wet surfaces accounted for totaling around 19.5% of accidents. 38% of accident involved injury in each surface condition.

6. Light Condition

• The pie chart outlines accident distribution by light conditions. Around 68.46% of incidents occurred during daylight, while dark conditions contributed to 27.17% of accidents. Weak light conditions accounted for a smaller proportion, approximately 4.37%, of incidents. This breakdown highlights the dominance of accidents during daylight hours, with a significant portion also happening in the dark, and a lesser occurrence under weak light conditions.

7. Traffic Control

• The pie chart illustrates accident distribution concerning traffic control measures. The majority, comprising 47.54% of incidents, occurred in areas with no traffic control. Accidents at traffic signals accounted for 40.79% of incidents, while those at stop signs constituted 8.65%. A smaller proportion, approximately 1.48%, of accidents occurred at locations with flashing traffic signals. This breakdown emphasizes the prevalence of accidents in areas lacking traffic control, followed by those regulated by traffic signals and stop signs, with fewer incidents occurring at locations with flashing traffic signals.

Injury Severity by Traffic Control

• There are more injury when accidents happened at Traffic Signal

8. Speed Limit Zone

• There was more accidents happened at Medium speed zone but the proportion of driver consume Alcohol or using Drug in low speed zone is higher. Do they think that driving at low speed zone is safer when use Alcohol or Drug ?

9. Route Type

• Driver tend to drive to County Road than State Road when consume Alcohol or Drug.

10. Distribution of Collision Types

Injury Severity by Collision Types

• Head-on collisions cause more and more serious injuries. As a consequence, head-on collisions are much more likely to result in severe injuries or death than other types of car accidents. Common causes of head-on collisions include distracted driving, driving while impaired, driver fatigue, speeding, and reckless driving.

• Similarly, sideswipe accidents also pose significant risks, often resulting in injuries. Sideswipe accidents occur when two or more vehicles are traveling parallel to one another, and one vehicle strikes the side of the other car. Drivers failing to turn their heads to check their blind spots before merging lanes or changing lanes frequently cause side-impact collisions.

11. Driver Behaviours

• Distraction Analyst

Mapping

12. Driving Under The Influence

Injury Severity by Driver Abuse

Location of Fatal Accidents

Conclusions

• Most dangerous time to drive: most accidents occur during “rush hour,” between 3 p.m. and 5 p.m
• There are more accident happened at Traffic Signal.
• Major accident happened at Daylight, Clear Weather, Dry Surface Road and in Medium Speed Limit Zone.
• 2/3 of confessed that they were doing something that lead to accident
• 86% of driving under the influence is Alcohol.

Future work

• Build a model to predict high-risk situations based on trend, factors like weather, date time, location and traffic volume.
• External Data Integration: Consider incorporating external data sources like traffic volume or road closure information to enrich the analysis.

Q/A