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Effects of autonomous vehicles on intercity public transport within urban agglomerations: Exploring multi-layered heterogeneity and distance-based variations

Author

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  • YUAN, Yali
  • YANG, Xiaobao
  • LI, Sixuan
  • CUI, Pengfei
  • YANG, Manli

Abstract

Autonomous vehicles (AVs) offer distinct advantages in intercity travel compared to urban travel, including less interference from complex urban traffic environments, reduced driver fatigue during longer trips, and enhanced travel comfort. The widespread adoption of AVs is expected to reshape intercity travelers' mode choice behavior. This study develops a hybrid random parameter logit model with heterogeneity in means and variances (HRPLHMV) to assess the impact of AVs on intercity public transport. Using data from 803 respondents in the Beijing-Tianjin-Hebei urban agglomeration, the study examines how attitudes, travel characteristics, and socio-demographic attributes influence mode choices among AVs, trains, and buses across varying distances. Results indicate that incorporating attitudes and multi-layered heterogeneity improves model fit. Effort expectancy, monthly income, and age exhibit significant mean and variance heterogeneity as random parameters variables. Influential factors and their impacts vary with travel distance. A higher perceived risk of AVs leads intercity travelers to prefer buses for shorter trips and trains for longer journeys. For longer distances, leisure travelers show a growing preference for AVs over trains. This study provides deep insights into intercity travelers' mode choice behavior within urban agglomerations post-AV introduction, helping policymakers formulate refined and differentiated strategies.

Suggested Citation

  • YUAN, Yali & YANG, Xiaobao & LI, Sixuan & CUI, Pengfei & YANG, Manli, 2026. "Effects of autonomous vehicles on intercity public transport within urban agglomerations: Exploring multi-layered heterogeneity and distance-based variations," Technological Forecasting and Social Change, Elsevier, vol. 224(C).
    • Handle: RePEc:eee:tefoso:v:224:y:2026:i:c:s0040162525005384
    DOI: 10.1016/j.techfore.2025.124507
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