Advancing incentive strategies in ridesplitting service: Insights from evolutionary game modeling

Ridesplitting, an on-demand travel mode facilitated by transportation network companies (TNCs), involves two passengers sharing a vehicle and splitting the fare. It emerges as a sustainable transportation mobility given its contribution to reducing greenhouse gas emissions, optimizing spatial-temporal use of road infrastructure, and enhancing passenger travel efficiency. However, passengers in TNC-based services frequently exhibit poor willingness to share (WTS) due to concerns over decreased privacy, comfort, identity recognition, and disease prevention in ridesplitting. This study aims to increase ridesplitting adoption rates among passengers by exploring specific price discount ranges that mitigate these concerns. Using the Criteria Importance Through Intercriteria Correlation (CRITIC) method and questionnaire surveys, we introduce the concept of passenger sensitivity to quantify the impact of reduced utilities on WTS. Subsequently, an evolutionary game model (EGM) is employed to develop price discount strategies for ridesplitting incentives tailored to varying levels of passenger sensitivity under two scenarios (scenario I: vehicle supply exceeds service demand; scenario II: service demand exceeds vehicle supply). Results indicate that passenger sensitivity ranges from 1.45 to 4.50, with an average of 3.18. Moreover, ridesplitting price discounts can effectively offset the negative impact of passenger sensitivity, with higher sensitivity requiring more substantial discounts. These findings offer an innovative framework for quantifying the negative factors affecting WTS and promoting the strategy formulation for ridesplitting incentives.