A generalized diffusion model for preference and response time: Application to ordering mobility-on-demand services. (December 2020) - Yu, Jiangbo Gabe
Highlights: Considers the impact of information update, framing effect, dynamic decisions, and time pressure in ordering a mobility-on-demand service. Jointly utilizes multi-attribute prospect theory, diffusion model, decision field theory, and random utility model. Demonstrates the importance of modeling and analyzing a decision of ordering on-demand mobility service as a sequence of sub-decisions. The outputs of the behavioral model are the probability of choosing each option and the corresponding response time distribution. Abstract: The goal of this research study is to model user preferences and response times (RTs) jointly in the context of Mobility-on-Demand (MOD) services under different MOD operator pricing schemes, information frames, and pressure levels. MOD operators' information provision, delay, and vehicle allocation strategies influence users' preferences and RTs, which, in turn, affect which operational and information provision strategies are optimal for MOD operators. Evidence shows that preferences and RTs are sensitive to precedent decisions, information frames, risk, and time pressure. These dynamic interplaying factors are challenging to capture using a traditional discrete choice modeling framework. Hence, this study proposes a generalized diffusion model based on Decision Field Theory (DFT), multi-attribute Prospect Theory (PT), and Random Utility Theory to model these various interplaying factors. This study applies the proposed modeling approach in the context of ordering Shared-use Automated Vehicle Mobility Services (SAMS). Sensitivity analyses explore the impacts of various inputs and model parameters such as initial waiting time estimate, updated waiting time estimate, time pressure, loss aversion, and value-of-time on preferences and RTs. The proposed model can provide value to MOD operators in terms of information provision and pricing strategies. Moreover, the proposed model can assist policymakers and planners interested in the system impacts of MOD services and regulating MOD information provision and pricing strategies. The modeling framework can extend to other applications where multiple sub-decisions are necessary to make a single (travel) choice under information update, framing, risk, and time pressure.