Robotaxi services are expanding rapidly in China, yet many users remain hesitant to continue using them after their initial experience. Prior studies have mainly relied on technology acceptance and trust-based models, focusing on pre-adoption intentions and hypothetical scenarios. Limited attention has been paid to how users cognitively and emotionally evaluate robotaxi experiences in real-world settings. To address this gap, this study applies Cognitive Appraisal Theory to explain how users form continuance intention toward robotaxi services. Using survey data from 422 individuals with actual robotaxi experience in Wuhan, China, the study examines two cognitive appraisal domains: perceived user–system interaction and perceived AI driving performance. These appraisals are proposed to influence two emotional responses, fear and enjoyment, which subsequently shape intention to continue using robotaxi services. This study also investigates the moderating role of perceived AI warmth, reflecting the extent to which the robotaxi is perceived as friendly, empathetic, and socially responsive. Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that both user–system interaction and AI driving performance significantly influence users’ emotional responses, with the exception that perceived user–system interaction does not significantly affect fear. In turn, these emotional responses shape continuance intention: fear reduces intention to continue using robotaxi services, whereas enjoyment enhances it. Furthermore, perceived AI warmth significantly moderates the relationship between perceived AI driving performance and fear. This study extends robotaxi literature beyond traditional technology acceptance approaches and highlights the importance of emotional and relational factors in supporting long-term robotaxi use. The study also contributes to the transport literature by positioning robotaxis as an emerging form of autonomous, shared mobility, showing that user retention is critical to the long-term viability and sustainable scaling of these services. These insights offer practical guidance for operators and policymakers seeking to retain riders and accelerate the transition to autonomous mobility.
Sustainable transport has been adopted as a guiding principle for developing transport systems that balance environmental sustainability, social inclusion, economic viability, and the needs of future generations. Despite its widespread recognition, the implementation of sustainable transport policies has progressed more slowly than anticipated. A key challenge is the uncertainty surrounding sustainable transport policymaking, including contested objectives and trade-offs, system complexity, diverse stakeholder perspectives, institutional path dependencies, and uncertain future developments. While adaptive policymaking has been proposed as a promising approach to handle these uncertainties, its practical application in sustainable transport remains limited.This paper addresses these challenges by (i) systematically identifying key challenges that hinder sustainable transport implementation, and (ii) proposing a decision-support framework for adaptive policymaking in transport. The framework is predicated on concepts from Decision Making Under Deep Uncertainty (DMDU) and provides a step-by-step approach to assist policymakers in formulating, monitoring, and adapting strategies under uncertainty. It conceptualises transport as a system embedded within broader socio-technical systems, with accessibility as a focal outcome, and incorporates methods that integrate multidisciplinary and transdisciplinary perspectives. To illustrate this framework, a stylised area redevelopment case based on Katoren Zuid, a mid-sized regional city in the Netherlands, is presented. The case demonstrates how a basic plan can be formulated and examined for vulnerabilities and opportunities, and a monitoring system with signposts, trigger conditions, and adaptive actions can be developed. The paper contributes by integrating DMDU principles with sustainable transport planning that has accessibility as its focal outcome. It provides a structured approach for operationalising adaptive policymaking under deep uncertainty.
Rapid motorization in low- and middle-income countries has intensified environmental challenges, particularly in motorcycle-dependent contexts such as Pakistan, where two-wheelers account for a substantial share of transport-related emissions. Despite the introduction of several national initiatives to promote electric mobility, market uptake of electric motorcycles remains low. Existing studies predominantly emphasize technological and psychological factors, with comparatively less attention to market-oriented dynamics in shaping consumer behavior. This study extends the Theory of Planned Behavior (TPB) by incorporating perceived marketing, perceived consumer effectiveness, product knowledge, and price consciousness alongside traditional socio-psychological determinants. Drawing on face-to-face survey data collected from 655 respondents across Pakistan, the analysis investigates how these market-oriented and behavioral constructs are jointly associated with intention to use electric motorcycles. Results indicate that attitude and perceived marketing were the strongest factors associated with intention to use electric motorcycles. The study provides a structured extension of TPB by integrating market-oriented constructs with its core behavioral components in an emerging electric-mobility market. From a practical standpoint, the findings provide guidance for policymakers and industry leaders to design targeted marketing campaigns, enhance public awareness, and introduce cost-related incentives to support the shift toward sustainable mobility.
Ensuring the resilience of global crude oil transportation is essential for energy security and supply chain stability. This study develops a multimodal crude oil transportation network by integrating Automatic Identification System data and global pipeline infrastructure, capturing both maritime and land-based transport systems. A two-stage resilience assessment framework is then proposed, combining short-term adaptive responses with long-term recovery strategies. Furthermore, a performance-integrated resilience metric is utilized to evaluate the network under a range of realistic disruption scenarios, including maritime chokepoints, key trading countries, and port clusters. The results reveal significant heterogeneity in resilience index across regions and disruption types. Specifically, maritime disruptions at strategic straits/canals generally exhibit the highest initial performance losses and the weakest short-term recoverability. In contrast, localized port disruptions show strong adaptability due to dense route redundancy. Notably, Pipeline transport provides a supplementary buffering function in scenarios where accessible cross-border pipeline capacity is available, particularly under country-level supply disruptions. Moreover, the proposed dynamic optimization-based recovery model outperforms traditional degree-based strategies by prioritizing node restoration based on evolving network conditions. These findings provide practical insights for designing resilient crude oil transport systems and contribute a novel methodological approach for system-wide infrastructure resilience assessment.
Mobility as a Service (MaaS) is an innovative solution addressing urban mobility issues and promoting sustainable travel for tourists at destinations. This study examines tourists’ MaaS adoption by considering both motivation and risk factors. Data were gathered from 623 potential domestic tourists in Taiwan. Findings showed that perceived savings, sensation and novelty-seeking, safety risk, subjective norm, multimodality, and environmental concern significantly associated with adoption intention, while cost risk does not. Additionally, environmental concern moderates the link between perceived risk and adoption intention. The fsQCA analysis identified four configurations linked to tourists’ MaaS adoption, indicating that different groups of tourists may arrive at similar adoption intentions through distinct decision-making pathways. This research provides new insights into decision-making processes related to MaaS for tourists. Both theoretical and managerial implications are also discussed.
