From Trial to Deployment: A SEM Analysis of Traveler Adoptions to Fully Operational Autonomous Taxis

TL;DR

This study empirically investigates actual user adoption of autonomous taxis in Wuhan using survey data and SEM, revealing key psychological factors influencing real-world usage and informing deployment strategies.

Contribution

It provides the first empirical analysis of actual autonomous taxi adoption behavior using real operational data and SEM, highlighting key psychological predictors.

Findings

Cost Sensitivity strongly predicts adoption behavior.

Behavioral Intention significantly influences user adoption.

Model shows excellent fit with real-world survey data.

Abstract

Autonomous taxi services represent a transformative advancement in urban mobility, offering safety, efficiency, and round-the-clock operations. While existing literature has explored user acceptance of autonomous taxis through stated preference experiments and hypothetical scenarios, few studies have investigated actual user behavior based on operational AV services. This study addresses that gap by leveraging survey data from Wuhan, China, where Baidu's Apollo Robotaxi service operates at scale. We design a realistic survey incorporating actual service attributes and collect 336 valid responses from actual users. Using Structural Equation Modeling, we identify six latent psychological constructs, namely Trust \& Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. Their influences on adoption behavior, measured by the selection frequency of autonomous taxis in ten scenarios, are examined and interpreted. Results show that Cost Sensitivity and Behavioral Intention are the strongest positive predictors of adoption, while other latent constructs play more nuanced roles. The model demonstrates strong goodness-of-fit across multiple indices. Our findings offer empirical evidence to support policymaking, fare design, and public outreach strategies for scaling autonomous taxis deployments in real-world urban settings.