Editorial: Integrated perspectives in transport engineering: infrastructure, behaviour, modelling and decision making

Transport systems are at the centre of contemporary societal and economic challenges. Rapid urbanisation, climate imperatives, technological change and evolving patterns of mobility demand robust analytical frameworks and evidence-based solutions. In response, transport engineering is increasingly required to adopt integrated approaches that consider infrastructure performance, human behaviour, system efficiency and decision making under uncertainty. This issue of Proceedings of the Institution of Civil Engineers – Transport assembles eight contributions that collectively strengthen the empirical and methodological foundations of transport research while addressing practical needs in infrastructure, safety, modelling and strategic investment.Recent years have seen an intensification of research combining data-driven methods with engineering judgement to inform real-world practice. This development reflects the complexity of contemporary transport problems, where uncertainty, heterogeneity of users and multi-stakeholder decision settings are the norm rather than the exception. The papers in this issue demonstrate both conceptual advances and practical relevance across multiple transport domains.Shi et al. (2026) examine the deformation–time behaviour of airport runways during horizontal directional drilling works, offering a nuanced understanding of how subsurface construction can affect critical operational infrastructure. Their analysis provides practical evidence for planning and mitigating deformation risks in airport environments, where disruptions carry significant operational and economic implications.Safety and user behaviour remain central concerns in transport engineering, particularly for vulnerable road users. Sudhakaran et al. (2026) present a comprehensive review of psycho-behavioural determinants affecting vulnerable road user safety. Their synthesis highlights cognitive, perceptual and behavioural factors that contribute to risk exposure, and underscores the need for integrative approaches that go beyond infrastructure design to incorporate behavioural insights into safety interventions.Investment decision making in transport infrastructure is addressed by Zou et al. (2026), who propose an intuitionistic fuzzy multi-attribute decision making method for waterway projects. The framework accommodates both qualitative and quantitative criteria, enhancing robustness in project judgement and offering strategic insights for inland water transport investment decisions.Data science and predictive methodologies are exemplified in the work of Abed et al. (2026), who develop a machine-learning-based pothole prediction model using pavement condition indicators. Their study demonstrates how predictive maintenance can improve resource allocation and extend service life, providing practitioners with tools to support more effective asset management.Driver behaviour in complex traffic environments is further explored by Diwakar and Ravishankar (2026) in their analysis of entering driver path changes at roundabouts. By applying both binary logit models and machine learning methods, the authors provide comparative insights into model performance and behavioural interpretation, with implications for intersection design and traffic simulation.At the material and structural level, Li et al. (2026) investigate the mechanical response of asphalt pavements under falling weight dynamic loading through numerical simulation. Their findings contribute to mechanistic understanding of pavement behaviour under transient loads, informing pavement design, evaluation and maintenance practices.The interplay between the built environment and travel demand is examined by Cui et al. (2026), who analyse non-linear effects of urban form variables on origin–destination passenger flows in Tianjin’s urban rail system. Their results reveal threshold effects and spatial heterogeneity in travel demand responses, challenging conventional linear assumptions and offering insights for more nuanced transit planning.Finally, Chung and Kuo (2026) propose a dual-metric performance assessment for public bicycle-sharing systems, demonstrated through the Taichung YouBike scheme. By combining operational and user-centred performance measures, the framework provides a balanced evaluation of system effectiveness and informs policy and operational decision making in active mobility schemes.Taken together, the contributions in this issue exemplify the multidimensional and multidisciplinary nature of contemporary transport research. They underscore the importance of integrating physical infrastructure analysis, human behaviour, data-driven modelling and decision support to address complex challenges in transport planning and engineering. We trust that this collection will inform ongoing research, guide professional practice and stimulate future inquiry into sustainable, safe and efficient transport systems.