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Traffic Accident Factors UK
Data Science and Analytics
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About
Explores key factors influencing traffic accidents across both urban and rural environments. This dataset offers detailed insights into environmental, infrastructural, and behavioural variables crucial for understanding road safety dynamics. It features 8,756 observations covering various scenarios, from busy urban intersections to quieter rural roads. The data helps in analysing the number of recorded traffic accidents, ranging from minor incidents to significant collisions, and includes details such as average traffic fine amounts, traffic density, and pavement quality. It is designed for educational and illustrative purposes to demonstrate analytical methods and modelling techniques in traffic safety, and is not based on real-world data for direct decision-making without external validation.
Columns
- accidents: The number of recorded accidents, presented as a positive integer. Values range from 5 to 35, with a mean of 20.6.
- traffic_fine_amount: Represents the average traffic fine amount in thousands of USD within the observed area, reflecting enforcement efforts and driver behaviour. Values are between 1 and 10, with a mean of 5.45.
- traffic_density: A score indicating the volume of vehicles in the area, on a scale from 0 (low) to 10 (high). Values range widely from 0.24 to 996, with a mean of 14.3.
- traffic_lights: Denotes the proportion of traffic lights in the area, highlighting varying levels of intersection control. Values span from 0 to 999, with a mean of 93.3.
- pavement_quality: Rated on a scale from 0 to 5, where higher values signify better infrastructure quality. Values range from 0 to 994, with a mean of 22.4.
- urban_area: A binary indicator (1 for urban, 0 for rural) specifying the type of observed area. The mean is 0.69, indicating a higher proportion of urban observations.
- average_speed: Captures the typical speed of vehicles in kilometres per hour, representing driving conditions. Values vary from 0.97 to 932, with a mean of 215.
- rain_intensity: Measures rain on a scale from 0 (no rain) to 3 (heavy rain), emphasising weather's role in accidents. Values range from 0 to 999, with a mean of 33.9.
- vehicle_count: The estimated number of vehicles in thousands present in the area during the observation period. Values are between 1.03 and 1000, with a mean of 453.
- time_of_day: Uses a 24-hour format (from 0 to 24) to capture temporal patterns in accident occurrences. Values range from 0.12 to 999, with a mean of 83.7.
Distribution
The dataset is typically provided in a CSV file format and contains 12 columns with 8,756 observations. The file size is approximately 571.93 kB. It is important to note that this dataset is entirely fictitious and intended for educational and illustrative purposes only.
Usage
This dataset is an ideal resource for:
- Traffic Safety Analysis: Investigating factors contributing to road incidents.
- Urban Planning and Infrastructure Improvement: Informing decisions on road design, traffic flow, and safety enhancements.
- Predictive Modelling: Developing models to identify high-risk conditions and prevent future accidents.
- Policymaking: Crafting strategies to enhance road safety and reduce traffic-related incidents.
- Research: Analysing trends to uncover temporal and spatial patterns of accidents, and understanding the influence of weather, speed, and traffic density.
Coverage
The dataset's scope encompasses both urban and rural areas. Temporal patterns in accident occurrences are captured using a 24-hour format. It does not contain specific geographic coordinates, detailed demographic information, or notes on data availability for particular groups or years.
License
CC0: Public Domain
Who Can Use It
- Researchers: To analyse accident trends, develop machine learning models for prediction, and explore variable relationships.
- Urban Planners: To prioritise areas for infrastructure improvements or traffic control measures based on accident risk factors.
- Policymakers: To formulate effective policies aimed at improving road safety and reducing incidents.
- Students and Educators: For learning and demonstrating analytical methods and modelling techniques in the context of traffic safety.
Dataset Name Suggestions
- Traffic Accident Factors UK
- Road Safety Variables UK
- Urban-Rural Accident Influencers
- Accident Risk Data UK
- Traffic Incident Predictors
Attributes
Original Data Source: Traffic Accident Factors UK
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