Work

A Mixed-Methods Study of Wait Time Perception and Discrepancy in Technology-Mediated Mobility Systems

Year: 2025

Type: article

Abstract: Mobility services are becoming increasingly reliant on new technologies and mobile apps to manage and enable rides. In recent years, we have witnessed a rapid growth of technology-mediated mobility services (e.g., ridesharing and carsharing) with the ubiquity of smartphones. As an important technology-mediated mobility service involving interactions between passengers, drivers, and platforms, ridesharing has attracted great interest from the research community. Even though many existing studies have focused on the ridesharing experience of passengers, few of them have conducted a comprehensive study of passenger wait times in ridesharing systems. Prior research has shown that wait time is highly related to user experience. Understanding wait times in technology-mediated mobility systems and identifying factors that may impact them is of great importance for better user experience and the design of next-generation interactive mobility systems. Hence, in this paper, we adopt a mixed-methods approach to comprehensively examine two wait times in one of the largest technology-mediated mobility systems-DiDi, i.e., (i) the promised wait time shown on its mobile app after entering the origin and destination and (ii) the actual wait time. We first interviewed 102 individuals (including 52 passengers and 50 drivers) to understand people's perceptions of the two wait times in the DiDi ridesharing system. Our findings reveal that wait time discrepancy causes problems and negative emotions for passengers, and there are multiple potential factors that impact the discrepancy. To further verify some of these findings from a quantitative perspective, we performed a data-driven analysis based on large-scale ridesharing log data from over 36.6 million rides. Based on these findings, we share some design implications for ridesharing systems including those on minimizing expectation mismatch, supporting algorithmic transparency & fairness, and contextual factors consideration.

Source: Proceedings of the ACM on Human-Computer Interaction

DOI: 10.1145/3710921

Authors: Guang Wang, Vivek K Singh, Desheng Zhang

Institutions: Florida State University, Rutgers, The State University of New Jersey

Language: English

Topic: Transportation and Mobility Innovations

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: bronze

Funders: National Science Foundation, Universitas Brawijaya