Explaining Ridesharing: Selection of Explanations for Increasing User Satisfaction

TL;DR

This paper explores how providing tailored explanations about transportation options can increase user satisfaction in ridesharing services, using game theory and machine learning to optimize explanations.

Contribution

It introduces a signaling game model for explanation strategies and develops a machine learning agent that outperforms rational and random explanation methods.

Findings

Machine learning agent improves user satisfaction over rational agent.

Providing explanations increases willingness to use ridesharing.

Agent outperforms baseline explanation strategies.

Abstract

Transportation services play a crucial part in the development of modern smart cities. In particular, on-demand ridesharing services, which group together passengers with similar itineraries, are already operating in several metropolitan areas. These services can be of significant social and environmental benefit, by reducing travel costs, road congestion and CO2 emissions. Unfortunately, despite their advantages, not many people opt to use these ridesharing services. We believe that increasing the user satisfaction from the service will cause more people to utilize it, which, in turn, will improve the quality of the service, such as the waiting time, cost, travel time, and service availability. One possible way for increasing user satisfaction is by providing appropriate explanations comparing the alternative modes of transportation, such as a private taxi ride and public transportation. For example, a passenger may be more satisfied from a shared-ride if she is told that a private taxi ride would have cost her 50% more. Therefore, the problem is to develop an agent that provides explanations that will increase the user satisfaction. We model our environment as a signaling game and show that a rational agent, which follows the perfect Bayesian equilibrium, must reveal all of the information regarding the possible alternatives to the passenger. In addition, we develop a machine learning based agent that, when given a shared-ride along with its possible alternatives, selects the explanations that are most likely to increase user satisfaction. Using feedback from humans we show that our machine learning based agent outperforms the rational agent and an agent that randomly chooses explanations, in terms of user satisfaction.