Quantifying and analyzing traffic emission reductions from ridesharing: A case study of Shanghai

Ridesharing has potential to mitigate traffic emissions. To better support policymaking, this paper endeavors to estimate and analyze emission reductions by large-scale ridesharing combining the Shareability-Network approach, the COPERT III emission model, and a speed-density traffic-flow model. Using Shanghai as a case, we show that ridesharing per se can reduce fuel-consumption (FC) by 22.88% and 15.09% in optimal and realistic scenarios, respectively, with corresponding emissions reductions. Ridesharing's spontaneous first-order speed effect further reduces FC by 0.34-0.96%. Additionally, spatial analyses show that ridesharing reduces more emissions on severely polluted roads, leading to two spatial patterns; temporal analyses demonstrate patterns shifted from disorganized to organized. Both the phenomena can be explained by the aggregation of trips and the grading and topology of the roads. Moreover, ridesharing may also increase emissions on some branch roads, creating a new environmental injustice, which, however, is estimated to be less significant than expected.