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Ridesplitting, a shared mobility service, has the potential to reduce traffic-related air pollution. This study evaluates the impacts of ridesplitting on reducing different types of emissions and investigate how travel characteristics of ridesplitting affect emission reduction based on a ridesourcing dataset in Chengdu, China. First, this study quantifies the influence of ridesplitting on emissions reduction compared to single ride (i.e., non-ridesplitting) for each trip. The results indicate that a ridesplitting trip averagely reduce CO 2 , CO, NOx, and HC emissions by 34.52%, 5.98%, 33.10%, and 13.42%, respectively. Subsequently, using explainable machine learning, we quantitatively analyze how the travel characteristics of ridesplitting affect emission reduction at two levels. At the trip level, shared travel distance, shared travel time, delay, and detour are important factors for emission reduction. At the grid level, the number of orders that match co-riders within the same spatial community is more important than the total number of orders. [ABSTRACT FROM AUTHOR]

With rapid urbanization and industrialization, the number of light-duty gasoline vehicles (LDGVs) in China has continued to grow rapidly, leading to a significant increase in traffic pollution. Therefore, it is essential to accurately calculate the emission of LDGVs for air quality monitoring and management. Fortunately, Motor Vehicle Emission Simulator (MOVES) is a sophisticated model for estimating mobile source emissions with good prediction accuracy. However, the parameters of MOVES are based on the field tests in the US, which is worth exploring whether MOVES can be applied to other countries. Hence, in this paper, we used the portable emission measurement system (PEMS) to conduct real driving emission (RDE) tests of LDGVs, aiming to explore the possibility of the MOVES application in China. Based on the field tests, we modified basic parameters in the MOVES model, but unsatisfactory prediction performance was obtained. Existing research on improving MOVES performance mainly involved new binning of operating modes, but these methods had limited improvements. Though studies have also used machine learning methods for predicting LDGV emissions, they lacked comparison and integration with the MOVES model. To further improve the prediction accuracy, we proposed a novel road vehicle emission model that integrated the machine learning method and the MOVES to predict the road-level emission rates of NO and CO 2 emissions of LDGVs. In addition, we employed the Boruta algorithm to capture the key influencing factors and promote prediction performance. The enhanced model outperformed MOVES and achieved higher R2 values. On average, the improvement for CO 2 was 0.132, and for NO, it was 0.261. This work will provide references for MOVES improvements in practical scenarios and better predict pollutant emissions for LDGVs using limited resources of field tests in cities outside the US. • Integrating engine characteristics with the MOVES model can improve model performance. • Integrating machine learning with MOVES performs well in prediction of vehicle CO 2 and NO emission. • The proposed emission model provides a possibility for calculation rather than costly an experiment in countries outside the USA. [ABSTRACT FROM AUTHOR]