Effect of taxis on emissions and fuel consumption in a city based on license plate recognition data: A case study in Nanning, China

Abstract

With the rapid increase in urban vehicle ownership, transportation systems have become a major source of air pollution in cities across the globe. Taxis in urban transportation systems contribute to air pollution in two ways: through direct emissions, and through the indirect emissions of other vehicles resulting from increased traffic flow and saturation of the road network caused by vacant taxis. To analyze the differences between emissions in urban road transportation systems with and without taxis quantitatively, this study estimates the on-road emissions and fuel consumption (FC) of vehicles before and during a taxi strike in Nanning, China using license plate recognition data. The spatiotemporal features of the emissions and FC before and during the taxi strike are analyzed, including temporal variations and spatial distributions. The emissions and FC in four typical road segments are also analyzed with and without taxis. The results demonstrate that in the event of a taxi strike, the total emissions and FC decrease to a larger extent compared to the decrease in total travel distance. However, the temporal and spatial patterns of pollutant emissions in the city exhibit no significant change. The amounts of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), particulate matter (PM), FC, and carbon dioxide (CO2) decrease by 12.54%, 12.98%, 8.01%, 7.80%, 11.33%, and 11.33%, respectively, between 7 a.m. and 1 p.m. during the taxi strike. During the morning peak period (8 a.m.–10 a.m.), the reductions in emissions and FC are significant, i.e., 14.74%, 15.05%, 11.70%, 10.75%, 14.17%, and 14.17% for CO, HC, NOx, PM, FC, and CO2, respectively. Finally, from the results based on a large dataset, informative insights for improving taxi management systems and reducing emissions and FC are discussed to promote the sustainable development of urban transportation systems.