Multivariate Vehicle Exhaust and Traffic Dataset

Columns

• Vehicle Type: Categorises the mode of transport into classes such as Car, Truck, Bus, or Motorcycle.
• Fuel Type: Indicates the propulsion energy source, including Petrol, Diesel, Electric, and Hybrid systems.
• Engine Size: The displacement volume of the engine measured in litres, indicative of power and potential emission volume.
• Age of Vehicle: The operational life of the vehicle in years, serving as a proxy for technology standards and mechanical wear.
• Mileage: The cumulative distance travelled by the vehicle, reflecting long-term usage and engine condition.
• Speed: The average velocity maintained during the measurement period (km/h), a critical factor in combustion efficiency.
• Acceleration: The rate of change in velocity (m/s²), highlighting driving behaviour that often correlates with emission spikes.
• Road Type: Classifies the driving infrastructure into settings such as Highway, City, or Rural roads.
• Traffic Conditions: Describes the congestion levels experienced, ranging from Free flow to Heavy traffic.
• Temperature: Ambient air temperature (°C) recorded during operation, which affects engine thermal efficiency.
• Humidity: The relative moisture content in the air (%), influencing the combustion process.
• Wind Speed: The velocity of prevailing winds (m/s), which impacts the immediate dispersion of exhaust gases.
• Air Pressure: Atmospheric pressure (hPa) data that correlates with oxygen availability for combustion.
• CO2 Emissions: The quantity of Carbon Dioxide emitted (g/km), the primary greenhouse gas tracked.
• NOx Emissions: Nitrogen Oxide output (g/km), a key contributor to smog and respiratory issues.
• PM2.5 Emissions: Fine particulate matter output (g/km), essential for health impact assessments.
• VOC Emissions: Volatile Organic Compounds released (g/km), involved in ozone formation.
• SO2 Emissions: Sulphur Dioxide emissions (g/km), associated with acid rain and air toxicity.
• Emission Level: A target variable categorising the overall vehicle footprint into Low, Medium, or High classes.

Distribution

Usage

• Predictive Modelling: develop machine learning algorithms to classify vehicles into emission risk categories (Low, Medium, High).
• Environmental Impact Assessment: Quantify the carbon and particulate footprint of different vehicle fleets under varying weather conditions.
• Urban Planning: Analyse the impact of road types and traffic congestion on total pollution loads to optimise traffic flow.
• Automotive Engineering: Study the correlation between engine size, vehicle age, and specific pollutants like NOx and SO2 to improve design.
• Health and Safety Analysis: Correlate PM2.5 and VOC levels with specific driving environments to assess public health risks in urban areas.

Coverage

• Geographic Scope: India.
• Demographic/Subject Scope: Covers a mix of private and commercial transport (Cars, Trucks, Buses, Motorcycles) with varied fuel technologies including Electric and Hybrid.
• Update Frequency: Expected to be updated annually.
• Conditions: Includes diverse operational environments such as city centres, highways, and rural settings under varying meteorological states.

License

Who Can Use It

• Data Scientists: For training multi-class classification models and regression analysis.
• Environmental Researchers: To study the variance of pollutants relative to atmospheric changes.
• Policy Makers: To formulate regulations based on emission trends associated with vehicle age and fuel type.
• Automotive Manufacturers: To benchmark performance and durability regarding emissions over a vehicle's lifespan.

Dataset Name Suggestions

• Indian Vehicle Emission Factors and Weather Data
• Vehicle Pollution Metrics by Operational Condition
• India Automotive Emissions and Environmental Impact
• Multivariate Vehicle Exhaust and Traffic Dataset

Attributes