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

Yutong Cai, Hua Wang

TL;DR

This study tackles the gap in external validity for autonomous taxi adoption by analyzing real-world user responses to Baidu's Apollo Robotaxi in Wuhan. It designs a realism-based stated-preference survey grounded in operational data and analyzes 336 valid responses with Exploratory Factor Analysis and Structural Equation Modeling to identify six latent drivers of adoption: Trust & Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. The results show Cost Sensitivity and Behavioral Intention as the dominant positive predictors, with other factors contributing more nuanced effects, and the SEM exhibits a robust fit (CFI 0.956, TLI 0.950, RMSEA 0.043, SRMR 0.046). Practically, the findings inform fare design, public outreach, and regulatory strategies to scale autonomous taxi deployments in urban environments.

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.

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

TL;DR

This study tackles the gap in external validity for autonomous taxi adoption by analyzing real-world user responses to Baidu's Apollo Robotaxi in Wuhan. It designs a realism-based stated-preference survey grounded in operational data and analyzes 336 valid responses with Exploratory Factor Analysis and Structural Equation Modeling to identify six latent drivers of adoption: Trust & Policy Support, Cost Sensitivity, Performance, Behavioral Intention, Lifestyle, and Education. The results show Cost Sensitivity and Behavioral Intention as the dominant positive predictors, with other factors contributing more nuanced effects, and the SEM exhibits a robust fit (CFI 0.956, TLI 0.950, RMSEA 0.043, SRMR 0.046). Practically, the findings inform fare design, public outreach, and regulatory strategies to scale autonomous taxi deployments in urban environments.

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.
Paper Structure (14 sections, 3 equations, 5 figures, 4 tables)

This paper contains 14 sections, 3 equations, 5 figures, 4 tables.

Table of Contents

  1. Abstract
  2. Introduction
  3. Literature Review
  4. Methodology
  5. Survey Questionnaire Design and Data Collection
  6. Survey Questionnaire Design
  7. Survey Execution and Data Collection
  8. Results
  9. Explanatory Factor Analysis
  10. Structural Equation Modeling
  11. Conclusions
  12. Conclusion
  13. Acknowledgements
  14. Appendix

Figures (5)

  • Figure 1: An overview of our research framework
  • Figure 2: Sample Travel Scenario involving AR in Wuhan, China
  • Figure 3: ECDF of AT choice in the 10 travel scenarios
  • Figure 4: The measurement model with loading factors
  • Figure 5: The structural model for AT adoption