UK Traffic Accident Analysis (2015-2018)

Road safety remains one of the most critical public health challenges in the United Kingdom, affecting thousands of lives annually through accidents, injuries, and fatalities. This comprehensive analysis examines traffic accident data from 2015 to 2018, seeking to understand the complex interplay of factors that contribute to road incidents and their severity. By analyzing over 529,294 accidents, this study provides crucial insights into patterns, risk factors, and potential intervention points for improving road safety.

The significance of this analysis extends beyond mere statistics, touching upon fundamental aspects of public safety, urban planning, and social responsibility. Through detailed examination of temporal patterns, environmental conditions, and demographic factors, we uncover the multifaceted nature of traffic accidents. The findings reveal how various elements - from weather conditions and road types to driver demographics and vehicle characteristics - interact to influence both the likelihood and severity of accidents. This understanding is crucial for developing targeted interventions and evidence-based policies to enhance road safety.

This study employs advanced statistical analysis and visualization techniques to decode complex patterns within the data, focusing particularly on identifying high-risk scenarios and vulnerable populations. By examining the relationship between factors such as time of day, weather conditions, road types, and accident severity, we aim to provide actionable insights for policymakers, urban planners, and road safety professionals. The analysis pays special attention to age-related patterns, environmental impacts, and vehicular factors, offering a comprehensive view of road safety challenges in the UK.

Research Objectives:

GitHub Icon Github Link for Project

ETL Process

A comprehensive ETL process was performed on road safety data spanning multiple years, focusing on three main datasets: Accidents, Casualties, and Vehicles. The process included data validation, standardization, and consolidation, with particular attention to date/time format standardization from UK to US formats.

Detailed ETL Process

1. Data Validation and Structure Assessment

  • Performed initial data comparison between redundant 2015 datasets to ensure data integrity
  • Validated matching data between Data\RoadSafetyData_2015 and organized folder structure
  • Confirmed 100% match for Accidents, Casualties, and Vehicles datasets
  • Decision made to remove redundant 2015 folder after validation

2. File Structure Standardization

Implemented consistent naming convention across all datasets:

  • Renamed 2017 accident files from "ACC" to "Accidents_2017"
  • Standardized Casualties files naming convention across years
  • Updated Vehicles files from "Veh.csv" to standardized naming format
  • Organized files into appropriate report type directories

3. Data Transformation

Date/Time Format Standardization

  • Converted all timestamps from UK to US format
  • Resolved secondary time format issues related to hours and minutes
  • Implemented additional validation checks for datetime consistency

4. Data Cleaning

Executed custom data cleaning script to handle:

  • Data standardization
  • Error correction
  • Format consistency
  • Generated comprehensive data cleaning report using custom Python code

5. Data Consolidation

Combined cleaned datasets into three main categories:

  • Accidents master file
  • Casualties master file
  • Vehicles master file

Technical Notes

Primary challenge areas addressed:

  • Date/time format standardization
  • File organization and naming conventions
  • Data validation and verification

Custom Python scripts were developed for:

  • Data comparison and validation
  • Cleaning operations
  • Report generation
  • Data consolidation
2015 Accidents Comparison Report

Accidents Comparison Report

2015 Casualties Comparison Report

Casualties Comparison Report

2015 Vehicles Comparison Report

Vehicles Comparison Report

Python Folder Reader and CSV Cleaning Program

Python Folder Reader and CSV Cleaning Program

Process Timeline

12:52
Initial date format standardization
14:11
Data validation of 2015 datasets
14:29
Folder structure optimization
14:41
File renaming and organization
15:18
UK to US time format conversion
15:43
Implementation of data cleaning
16:24
Data consolidation
16:49
Final datetime format resolution

Data Legend

Accident Severity

Code Label
1Fatal
2Serious
3Slight

Weather Conditions

Code Label
1Fine no high winds
2Raining no high winds
3Snowing no high winds
4Fine + high winds
5Raining + high winds
6Snowing + high winds
7Fog or mist
8Other
9Unknown
-1Data missing or out of range

Road Type

Code Label
1Roundabout
2One way street
3Dual carriageway
6Single carriageway
7Slip road
9Unknown
12One way street/Slip road
-1Data missing or out of range

Casualty Type

Code Label
0Pedestrian
1Cyclist
2Motorcycle 50cc and under rider or passenger
3Motorcycle 125cc and under rider or passenger
4Motorcycle over 125cc and up to 500cc rider or passenger
5Motorcycle over 500cc rider or passenger
8Taxi/Private hire car occupant
9Car occupant
10Minibus (8 - 16 passenger seats) occupant
11Bus or coach occupant (17 or more pass seats)
16Horse rider
17Agricultural vehicle occupant
18Tram occupant
19Van / Goods vehicle (3.5 tonnes mgw or under) occupant
20Goods vehicle (over 3.5t. and under 7.5t.) occupant
21Goods vehicle (7.5 tonnes mgw and over) occupant
22Mobility scooter rider
23Electric motorcycle rider or passenger
90Other vehicle occupant
97Motorcycle - unknown cc rider or passenger
98Goods vehicle (unknown weight) occupant

Vehicle Type

Code Label
1Pedal cycle
2Motorcycle 50cc and under
3Motorcycle 125cc and under
4Motorcycle over 125cc and up to 500cc
5Motorcycle over 500cc
8Taxi/Private hire car
9Car
10Minibus (8 - 16 passenger seats)
11Bus or coach (17 or more pass seats)
16Ridden horse
17Agricultural vehicle
18Tram
19Van / Goods 3.5 tonnes mgw or under
20Goods over 3.5t. and under 7.5t
21Goods 7.5 tonnes mgw and over
22Mobility scooter
23Electric motorcycle
90Other vehicle
97Motorcycle - unknown cc
98Goods vehicle - unknown weight
-1Data missing or out of range

Data Dictionary

Accidents Dataset

Field Name Description Data Type Values/Format
Accident_Index Unique identifier for each accident String Unique value linking to vehicle and casualty data
Location_Easting_OSGR Easting location in OSGR format Numeric Grid Reference (-1 for missing data)
Location_Northing_OSGR Northing location in OSGR format Numeric Grid Reference (-1 for missing data)
Longitude Longitude in WGS84 format Decimal WGS 1984 coordinate system
Latitude Latitude in WGS84 format Decimal WGS 1984 coordinate system
Accident_Severity Severity of the accident Integer 1: Fatal, 2: Serious, 3: Slight
Number_of_Vehicles Number of vehicles involved Integer Count of vehicles
Number_of_Casualties Number of casualties Integer Count of casualties
Date Date of accident Date DD/MM/YYYY format
Time Time of accident Time HH:MM 24-hour format

Casualties Dataset

Field Name Description Data Type Values/Format
Accident_Index Reference to accident record String Links to accident data
Vehicle_Reference Reference to vehicle involved Integer Links to vehicle data
Casualty_Class Type of casualty Integer 1: Driver/Rider, 2: Passenger, 3: Pedestrian
Sex_of_Casualty Gender of casualty Integer 1: Male, 2: Female, -1: Unknown
Age_of_Casualty Age of casualty Integer Age in years (-1 for unknown)

Vehicles Dataset

Field Name Description Data Type Values/Format
Accident_Index Reference to accident record String Links to accident data
Vehicle_Type Type of vehicle Integer Various codes for vehicle types
Age_of_Driver Age of driver Integer Age in years (-1 for unknown)
Age_of_Vehicle Age of vehicle Integer Age in years (-1 for unknown)

Methodology and Data Overview

529,294
Total Accidents
6,658
Fatal Accidents
87,462
Serious Accidents
435,174
Slight Accidents

This analysis utilizes comprehensive accident data from 2015 to 2018, incorporating multiple data sources to provide a holistic view of road safety incidents.

Detailed Analysis and Findings

1. Temporal Analysis

Time of Day Analysis

Key Observations:

  • Peak accident times coincide with rush hours (7-9 AM and 4-6 PM)
  • Highest severity rates occur during nighttime hours (11 PM - 4 AM)
  • Early morning hours (2-5 AM) show lowest frequency but higher severity
  • Weekend patterns differ significantly from weekday patterns

2. Weather Impact Analysis

Weather Analysis

Key Findings:

  • Adverse weather conditions significantly impact accident severity
  • Rain is associated with increased accident frequency but lower average severity
  • Snow and ice show lower frequency but higher severity rates
  • Clear weather accounts for majority of accidents due to higher traffic volume

3. Road and Speed Analysis

Road Type Analysis Speed Analysis

Critical Insights:

  • Single carriageways account for highest number of accidents
  • Higher speed limits correlate strongly with increased severity
  • Urban roads show higher frequency but lower severity patterns
  • Motorways show relatively low accident rates despite high speeds

4. Casualty Analysis

Casualty Analysis

Demographic Patterns:

  • Young adults (18-25) show higher representation in accidents
  • Elderly casualties (65+) show higher severity rates
  • Pedestrian casualties show distinct age distribution patterns
  • Cyclist casualties concentrate in urban areas and peak commuting times

5. Vehicle Type Analysis

Vehicle Analysis

Vehicle-Specific Patterns:

  • Cars represent the majority of vehicles involved
  • Motorcycles show disproportionately high severity rates
  • Heavy goods vehicles involved in fewer but more severe accidents
  • Public transport shows lower involvement rates

6. Age Distribution Analysis

Age Distribution Analysis

Age-Related Patterns:

  • Young adults (17-25) show highest accident involvement
  • Severity rates increase with age groups
  • Children under 16 show distinct accident patterns
  • Middle-aged groups show more moderate risk levels

Accident Visualizations

Hourly Distribution

Hourly Distribution

Monthly Severity Trends

Monthly Severity Trends

Severity Heatmap

Severity Heatmap

Time of Day

Time of Day

Tableau: Interactive Visualization

Conclusions and Recommendations

Statistical Overview

529,294
Total Accidents Analyzed
6,658
Fatal Accidents
87,462
Serious Accidents
435,174
Slight Accidents

Key Vulnerable Road User Groups

  • Pedestrians
    • Highest risk during urban rush hours
    • Children and elderly most vulnerable
    • Poor visibility in dark conditions increases risk
    • Crossing points are critical incident locations
  • Cyclists
    • Urban intersection conflicts predominate
    • Limited visibility during peak hours
    • Lack of dedicated infrastructure
    • Vehicle-cyclist awareness gaps
  • Young Drivers (17-25)
    • Inexperience in complex traffic situations
    • Night driving risks
    • Peer pressure and distraction
    • Speed-related incidents
  • Elderly Road Users
    • Higher severity rates in accidents
    • Reduced reaction times
    • Medical conditions affecting driving
    • Difficulty with complex junctions
  • Motorcyclists
    • High severity rates in accidents
    • Vulnerability in poor weather
    • Junction collision risks
    • Speed-related incidents

Recommended Solutions

  1. Smart Infrastructure Implementation (15-20% potential reduction in urban accidents)
    • AI-powered traffic management systems
    • Dynamic speed limits based on conditions
    • Connected vehicle infrastructure
  2. Enhanced Education Programs (10-15% potential reduction)
    • Continuous learning systems
    • Virtual reality hazard training
    • Vulnerable user awareness training
  3. Technology-Based Solutions (20-25% potential reduction)
    • Advanced driver assistance systems
    • Vehicle-to-vehicle communication
    • Automated emergency braking
  4. Time-Based Interventions (15-20% potential reduction)
    • Rush hour management
    • School zone programs
    • Night-time safety measures
  5. Road Design Improvements (25-30% potential reduction)
    • Segregated lanes
    • Junction redesigns
    • Enhanced surface materials

Expected Impact

Through this comprehensive approach, combining technology, education, and infrastructure improvements, we project a potential reduction in overall accident rates by 30-40% over a five-year period. Success will require substantial government investment, public-private partnerships, and continuous monitoring and adaptation of strategies.

Conclusion

The comprehensive analysis of UK traffic accidents from 2015-2018 reveals a complex interplay between temporal, environmental, and human factors in road safety. The data demonstrates both predictable patterns, such as rush hour accident frequency, and counterintuitive insights, like the lower frequency but higher severity of nighttime accidents. Each vulnerable road user group shows distinct risk patterns requiring targeted interventions, from enhanced pedestrian crossing systems to advanced rider training programs for motorcyclists. The analysis particularly highlights the need for age-specific approaches, with young adults being most frequently involved in accidents while elderly road users face higher severity rates.

The environmental and infrastructure findings underscore the critical role of road design and weather conditions in accident outcomes. The analysis supports the implementation of smart infrastructure solutions, including AI-powered traffic management systems, dynamic speed limits, and connected vehicle infrastructure. The data suggests that comprehensive solutions combining technology, education, and infrastructure improvements could potentially reduce overall accident rates by 30-40% over a five-year period. Key initiatives should include mandatory advanced driver assistance systems, enhanced driver education programs, and targeted time-based interventions for high-risk periods.

Looking forward, the study advocates for a multi-faceted approach to road safety improvement through five key areas: smart infrastructure implementation, enhanced driver education, technology-based safety systems, targeted time-based interventions, and comprehensive road design overhaul. Success requires substantial government investment, public-private partnerships, and community engagement, implemented through a phased approach over short-term (0-12 months), medium-term (1-3 years), and long-term (3-5 years) periods. This structured approach, combined with regular effectiveness monitoring and adaptation to new technologies, presents a viable pathway to significantly reducing both the frequency and severity of road accidents across the UK. The findings emphasize that while certain risk factors remain constant, the solutions must evolve with technology and changing traffic patterns to effectively address road safety challenges in the modern era.