Data
London_Bus_Safety

London_Bus_Safety

active ARFF Attribution-ShareAlike (CC BY-SA) Visibility: public Uploaded 31-05-2024 by Iwo Godzwon
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Description: The dataset "tfl_bus_safety.csv" provides comprehensive details on bus-related safety incidents reported in London, covering the years 2015 to 2017. It meticulously records incidents by date, bus routes, operating companies, and specifics such as the garage location and the borough where each incident occurred. The diversity of operators and routes highlighted includes Tower Transit, Arriva London South, and several others, reflecting the citywide coverage of the data. Each entry features critical information on the incident, including the injury result, categorizing the severity and treatment following the incident, the type of event leading to the incident, and detailed victim information such as category, sex, and age group. The dataset serves as a valuable resource for analyzing the safety performance of London's bus network over the specified period, identifying potential patterns or areas requiring attention for policy makers, transport operators, and safety analysts alike. Attribute Description: - `year`: The year when the incident occurred (e.g., 2015, 2016, 2017). - `date_of_incident`: Specific date of the incident (e.g., 2015-06-01). - `route`: Bus route number involved in the incident (e.g., 297, 320). - `operator`: Name of the bus operator (e.g., Tower Transit, Arriva London South). - `group_name`: Group to which the operator belongs (e.g., London United, Arriva London). - `bus_garage`: Location of the bus garage (e.g., Putney, Waterside Way). - `borough`: London borough where the incident occurred (e.g., Westminster, Islington). - `injury_result_description`: Description of the injury result (e.g., Taken to Hospital - Reported Serious Injury). - `incident_event_type`: Type of incident (e.g., Personal Injury, Onboard Injuries). - `victim_category`: Category of the victim involved (e.g., Passenger, Cyclist). - `victims_sex`: Sex of the victim (e.g., Female, Unknown). - `victims_age`: Age category of the victim (e.g., Adult, Unknown). Use Case: This dataset is instrumental for stakeholders aiming to enhance bus safety across London. Urban planners and transport authorities can leverage the insights to identify high-risk routes or areas, evaluate the effectiveness of various bus operators in managing safety, and tailor interventions to reduce incident rates. Researchers in transportation safety can utilize the data for in-depth analyses of injury outcomes related to bus incidents, contributing to academic and practical knowledge on urban transport safety. Moreover, policy makers can use the dataset to guide regulatory or policy changes, ensuring a safer commuting environment for London's residents and visitors.

12 features

yearnominal4 unique values
0 missing
date_of_incidentstring45 unique values
0 missing
routenominal612 unique values
0 missing
operatornominal25 unique values
0 missing
group_namenominal14 unique values
0 missing
bus_garagenominal84 unique values
0 missing
boroughnominal35 unique values
0 missing
injury_result_descriptionstring4 unique values
0 missing
incident_event_typenominal10 unique values
0 missing
victim_categorynominal17 unique values
0 missing
victims_sexnominal3 unique values
0 missing
victims_agenominal5 unique values
0 missing

19 properties

23158
Number of instances (rows) of the dataset.
12
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
10
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
0
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
83.33
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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