New perspective of environmental impact research: predicting bus exhaust emissions using the ETSformer based on collaborative perception

Abstract

Accurate prediction of urban traffic exhaust emissions plays a crucial role in controlling motor vehicle exhaust pollution. Urban public transport vehicles often operate in stop-and-go traffic conditions, emitting significant exhaust pollution. Existing approaches primarily focus on assessing vehicle emissions in the past or present, which may not adequately address long-term planning needs. To enhance the accuracy of bus exhaust emissions prediction, we introduce a novel time series transformer architecture named ETSformer for forecasting future vehicle emissions. This framework integrates exponential smoothing principles to refine the transformer-based time series prediction model, utilizing historical pollutant emissions data to forecast future exhaust emissions. The experiment collects emissions data and driving status information from public transport vehicles in Zhenjiang City, Jiangsu Province, considering factors such as vehicle runtime, route, and operational state to standardize response variables and enhance model precision and consistency. Experimental results demonstrate a determination coefficient (R2) of 70.7% and a mean square error (MSE) of 41.1% for exhaust gas prediction, signifying improved accuracy and efficiency compared to other models. Applying the exhaust gas prediction model to bus operations enables advanced forecasting of future exhaust emissions, addressing current challenges and potentially reducing Carbon Dioxide (CO2) emissions by over 3% during bus operations.