Sensitivity Analysis of Passenger Behavioral Model for Dynamic Pricing of Shared Mobility on Demand

TL;DR

This paper develops a framework to analyze how sensitive passenger behavioral models based on CPT are when used for dynamic pricing in shared mobility services, considering complex nonlinearities and passenger heterogeneity.

Contribution

It introduces a sensitivity analysis framework for CPT-based behavioral models in dynamic pricing, including analytical and numerical methods, to assess robustness and variability.

Findings

Model is robust for small to moderate parameter changes.

Sensitivity varies significantly with travel scenarios and modes.

Complex nonlinearities and passenger heterogeneity influence sensitivity outcomes.

Abstract

This paper provides a framework to quantify the sensitivity associated with behavioral models based on Cumulative Prospect Theory (CPT). These are used to design dynamic pricing strategies aimed at maximizing performance metrics of the Shared Mobility On-Demand Service (SMoDS), as solutions to a constrained nonlinear optimization problem. We analyze the sensitivity of both the optimal tariff as well as the optimal objective function with respect to CPT model parameters. In addition to deriving analytical solutions under certain assumptions, more general numerical results are obtained via computational experiments and simulations to analyze the sensitivity. We find that the model is relatively robust for small to moderate parameter perturbations. Although some of the trends in sensitivity are fairly general, the exact nature of variations in many cases depends heavily on the specific travel scenarios and modes being considered. This is primarily due to the complex nonlinearities in the problem, as well as the significant heterogeneity in passenger preferences across different types of trips.