Scholarlink

<p>This study investigates heterogeneous preferences for in-vehicle activities in autonomous vehicles (AVs). Using a rich survey dataset (N = 3,376) collected across four regions in the southern United States between June 2019 and March 2020, and weighted to represent the study population, latent-class cluster analysis (LCCA) was conducted. This analysis identifies latent classes with distinct combinations of preferred in-vehicle activities, separately for respondents asked about hypothetical alone trips (N = 1995) and those asked about family trips (N = 1381). For alone trips, the analysis reveals four classes: <i>Active use of time</i> (37.6%), <i>Passive use of time</i> (19.9%), <i>Alert</i> (23.8%), and <i>No-ride</i> (18.7%). For family trips, four corresponding classes are identified: <i>Solo and immerse</i> (23.5%), <i>Relax and interact</i> (33.5%), <i>Alert and interact</i> (29.1%), and <i>No-ride</i> (13.9%). The key factors influencing class membership are travel contexts, attitudes (e.g., tech-savviness, trust in AV technology, appreciation of AV benefits), and employment status (in the alone-trip model only). Further, the classes differ in their willingness to purchase AVs and their anticipated changes in travel behavior and location choices when AVs become available, in a way that aligns well with their preferences and profiles. This study contributes to the growing body of AV research by offering valuable insights into (a) the heterogeneity in preferences for in-vehicle activities, (b) underlying reasons for the heterogeneity and its behavioral implications based on class profiles, and (c) planning and policy strategies that may facilitate the deployment of AVs with greater societal benefits.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

How will people spend travel time in autonomous vehicles? A four-region study focusing on heterogeneous preferences

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月21日

查看全文
  • Ilsu Kim,
  • Yongsung Lee,
  • Patricia L. Mokhtarian,
  • Giovanni Circella

摘要

This study investigates heterogeneous preferences for in-vehicle activities in autonomous vehicles (AVs). Using a rich survey dataset (N = 3,376) collected across four regions in the southern United States between June 2019 and March 2020, and weighted to represent the study population, latent-class cluster analysis (LCCA) was conducted. This analysis identifies latent classes with distinct combinations of preferred in-vehicle activities, separately for respondents asked about hypothetical alone trips (N = 1995) and those asked about family trips (N = 1381). For alone trips, the analysis reveals four classes: Active use of time (37.6%), Passive use of time (19.9%), Alert (23.8%), and No-ride (18.7%). For family trips, four corresponding classes are identified: Solo and immerse (23.5%), Relax and interact (33.5%), Alert and interact (29.1%), and No-ride (13.9%). The key factors influencing class membership are travel contexts, attitudes (e.g., tech-savviness, trust in AV technology, appreciation of AV benefits), and employment status (in the alone-trip model only). Further, the classes differ in their willingness to purchase AVs and their anticipated changes in travel behavior and location choices when AVs become available, in a way that aligns well with their preferences and profiles. This study contributes to the growing body of AV research by offering valuable insights into (a) the heterogeneity in preferences for in-vehicle activities, (b) underlying reasons for the heterogeneity and its behavioral implications based on class profiles, and (c) planning and policy strategies that may facilitate the deployment of AVs with greater societal benefits.