Research On Incentivizing Strategies For Ridesplitting Services

As a pooled ride-hailing service,ridesplitting can reduce the urban vehicle kilometers traveled(VKT),alleviate traffic congestion,and lower carbon emissions.However,compared to the ride-hailing service,such services have not been effectively promoted,with a market share of less than 20%.How to enhance commuters’ willingness to use ridesplitting through incentive strategy design,and avoid the failure of incentive strategies,is a key and difficult issue in promoting the development of shared transportation.This article aims to thoroughly understand the impact mechanism of incentive strategies on commuters’ decision-making.A survey was conducted on Shanghai commuters to obtain relevant data on socio-economic characteristics,commuting characteristics,psychological perception characteristics,and the willingness to use ridesplitting under different incentive strategy combinations.Based on these data,starting from the psychological aspect of decision-makers,individual cognition,and behavioral preferences were considered to construct a model of the intention to use ridesplitting,in order to explore commuters’ decision preferences and construct a set of incentive strategies for ridesplitting.Considering the heterogeneity of commuters,the model analyzed the similarities and differences in the impact mechanisms of different incentive strategies and influencing factors on the willingness to use ridesplitting for different types of commuters,and evaluated the impact effectiveness of each incentive strategy.Constrained by costs and regulatory boundaries,an incentive strategy combination optimization model was established based on different optimization decisions as objectives.A multi-agent simulation system and heuristic algorithm were integrated to solve the problem,and the applicability of incentive strategy combinations in different supply and demand environments was analyzed through case studies.The main research focus of this article can be divided into three parts: exploring the commuters’ preference of decision-making based on the modeling of ridesplitting intention,understanding the impact mechanism of willingness to use ridesplitting considering heterogeneity,and the incentive strategy combination optimization model and integration of optimization algorithm with multi-agent simulation.(1)Exploring the commuters’ preference of decision-making based on the modeling of ridesplitting intention.Intentions,as the intrinsic drivers of commuters’ decision-making behavior,are crucial intermediate variables for willingness to use(i.e.,how choices are made).Starting from the psychological perception of decision-makers,the intermediate psychological process of external incentive strategies,public transportation competition,and intention to use were analyzed.Building on the Theory of Planned Behavior(TPB)and the Technology Acceptance Model(TAM),a model of the intention to use ridesplitting services under external incentives and public transportation competition was constructed.Structural equation modeling was used to estimate model parameters and dissect the impact of the external environment on consumer intention and decision preferences,providing a theoretical basis for the design of ridesplitting incentive strategies.(2)Understanding the impact mechanism of willingness to use ridesplitting considering heterogeneity.Deep consideration was given to inter-group and intra-group heterogeneity among commuters,grouping them based on commuting distance.Building on inter-group heterogeneity,the intra-group heterogeneity of various commuter groups was further considered,coupling data on various types of commuters under various incentive strategies to their respective random parameter models.A willingness model was established,incorporating socio-economic characteristics,commuting features,psychological perception characteristics,built environment features,and incentive strategies.Based on the results of the random parameter model,the impact mechanisms of various factors,intra-group heterogeneity of various types of commuters,and differences in the willingness to use ridesplitting under various incentive strategies were studied.The discussion focused on the application value of model customization based on incentive strategy differences.(3)Design of incentive strategy combination optimization model and integration of optimization algorithm with multi-agent simulation.Constrained by costs and regulatory boundaries,incentive strategy combination optimization models were established separately,with the optimal goals of supply-side and dual-side benefits.This type of optimization problem falls under offline dynamic system optimization,and a heuristic algorithm was integrated with a multi-agent simulation system to solve the problem.Simultaneously,to verify the applicability of the model and algorithm,cases with different supply and demand environments were introduced.The incentive strategy combination optimization model and the optimization algorithm with multiagent simulation were used to find the optimal decisions in different supply and demand environments,capturing the optimal incentive strategy combination results for the supply side and dual side in different supply and demand environments.The strategy effects in different supply and demand environments were analyzed through corresponding indicators.