As the widespread usage of autonomous vehicles is closer to becoming a reality, substantial consideration should be paid to the extent to which individuals choose vehicular mobility tools. The purpose of this study is to examine vehicle ownership models to better understand the adoption of vehicles by considering some factors such as liability issues, cost, safety, and environmental characteristics. 323 respondents were recruited from Istanbul, Turkey to complete the stated choice experiment through a web-based survey. Multinomial logit and Heteroskedastic mixed logit models were estimated to unravel users’ preferences concerning the selection of autonomous vehicles with distinction among private, shared, and ride-hailing vehicles. Results indicate that the adoption of those vehicles varied concerning the aforementioned characteristics, and these findings could help decision-makers to develop a vehicular mobility system for future transportation dynamics.

The transport sector plays a significant role in global greenhouse gas emissions, with major impacts on climate change and urban air quality. With escalating environmental concerns and rapid technological advancements, reducing carbon emissions in digitalized transportation has become an international priority. Despite this urgency, limited research integrates carbon-neutral objectives with digital transformation solutions for the transportation sector. This study aims to address this gap by evaluating and prioritizing digital transition strategies for achieving net-zero carbon transportation using a novel hybrid multi-criteria decision-making (MCDM) approach. Specifically, the study employs a combined Stepwise Weight Assessment Ratio Analysis (SWARA) and Combined Compromise Solution (CoCoSo) method under q-rung orthopair fuzzy sets. The reliability and robustness of the proposed approach are demonstrated through a comprehensive three-phase process, including two sensitivity analyses and a comparative analysis. The results reveal that consumer-centric digital solutions, such as Mobility-as-a-Service (MaaS), and electrified and hydrogen-fueled vehicles, rank highest due to their direct user impact and potential to accelerate sustainable transportation adoption. In contrast, policy- and infrastructure-focused strategies, such as AI-driven traffic management and blockchain-based systems, receive lower rankings, reflecting the less immediate visibility of their benefits to end users. This research offers valuable insights for policymakers, transportation authorities, and technology developers seeking to reduce transportation-related carbon emissions and foster technological innovation through digital integration. Future research could expand the application of the proposed model across diverse geographic and cultural contexts, or explore its adaptability to emerging digital innovations in transportation. Graphical Abstract

Enhancing railway freight logistics efficiency is crucial for strengthening global supply chain performance, yet persistent challenges such as infrastructure limitations, operational inefficiencies, and fragmented intermodal integration hinder optimal performance. Despite its critical role in economic and environmental sustainability, limited research offers comprehensive, universally applicable solutions for addressing these issues. This study bridges this gap by introducing a novel multi-criteria decision-making framework that integrates inter-criteria correlation (CRiteria Importance Through Intercriteria Correlation (CRITIC)) and multi-objective optimization based on ratio analysis (Multi-attribute Multi-Objective Optimization based on Ratio Analysis (MULTIMOORA)) with Q-rung orthopair fuzzy sets (q-ROFSs) to handle complex and conflicting decision-making scenarios. These methods were selected for their complementary strengths. CRITIC effectively quantifies the importance of criteria by considering their interdependencies, MULTIMOORA offers robust multi-objective optimization capabilities, and q-ROFSs manage the inherent uncertainty and ambiguity of real-world logistics problems. Their integration provides a comprehensive framework capable of addressing both the complexity and uncertainty in railway freight logistics decision-making. A two-phase sensitivity analysis validates the framework’s reliability and consistency. Results indicate that “infrastructure investment” ranks as the most impactful strategy, followed by “intermodal transportation”. These findings offer practical guidance for policymakers and industry leaders, providing actionable solutions to enhance operational performance and sustainability while advancing the theoretical discourse in transportation logistics.

Recent advancements in digitalization have transformed the logistics sector by introducing in-novative solutions that enhance efficiency, sustainability, and decision-making. In intermodal freight transport, the adoption of digital technologies offers significant potential to optimize operations, reduce costs, and improve environmental performance. However, prioritizing these technologies is crucial for ensuring strategic investments and maximizing their impact. This study proposes a hybrid multi-criteria decision-making (MCDM) framework that integrates Cri-teria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) within a Picture Fuzzy environment to evaluate and rank digital technology applications in intermodal freight transport. The findings indicate that “Artificial Intelligence (AI) for Optimization” is the most critical digital technology, followed by “Cloud Computing and Big Data Analytics” and “Internet of Things (IoT) for Asset Tracking”. Additionally, Operational Efficiency and Economic Efficiency emerged as the most influential evaluation criteria for digital adoption. To validate the reliability and consistency of the proposed methodology, a sensitivity analysis was conducted by modifying the weight values of the criteria, with robustness tested across 15 different scenarios. The results provide logistics managers with a structured approach for selecting and implementing the most impactful digital technologies to improve efficiency, cost-effectiveness, and supply chain resilience. Furthermore, the study offers insights for the automotive industry to integrate smart vehicle technologies and AI-driven solutions, increasing connectivity, automation, and sustainability in intermodal logis-tics. Future research can extend this framework by incorporating additional MCDM methods and real-world case studies to further refine digital transformation strategies in freight transport.

As an emerging technology for sustainable, safe, energy-efficient, and smooth traffic flow, autonomous public transport (APT) has been widely studied in recent years. However, the influence of conventional public transport (CPT) on behavioural intentions toward APT is largely overlooked. While APT is in its nascent phase, users’ choices may be shaped by their perceptions and attitudes toward CPT. Therefore, identifying these perceptions and examining their effect on behavioural intention is crucial. In this study, the Unified Theory of Acceptance and Use of Technology (UTAUT) is integrated with the satisfaction-loyalty model to analyze the key factors influencing behavioural intentions toward APT. To obtain more precise findings, this study examined public transport by type, including rubber-tired systems, urban rail, and bus rapid transit, rather than as a single mode, unlike many previous studies. A survey (n = 1271) was employed to validate the theoretical model among CPT users in Istanbul. The results indicate that loyalty to CPT significantly influences behavioural intention toward APT. Moreover, users of different CPT types have distinct priorities influencing their intention to use APT. While users of rubber-tired systems prioritize effort expectancy, social influence and facilitating conditions, users of urban rail systems consider social influence, trust and loyalty to CPT to be decisive factors. Furthermore, users of bus rapid transit systems consider performance expectancy, effort expectancy, trust, and loyalty to CPT as key factors influencing their behavioural intention. The findings are expected to enrich theoretical research on behavioural intention toward APT and guide future integration and transition between CPT and APT.