In Volume 178, Issue 6 (2025) of the Proceedings of the Institution of Civil Engineers – Transport, we have curated five high-quality articles contributed by researchers from around the world. These articles showcase diverse studies and insights from peers on advancements in the field of transportation engineering. I am greatly honoured to be invited to introduce the content of this issue to all of you.This issue of the journal covers an extensive array of subjects, ranging from railway and highway maintenance to fault detection at both macro and micro levels – including material analysis – and extending to the study of transportation vehicles as well as the analysis of driver behaviour. Several cutting-edge technologies have witnessed rapid development and widespread application in recent years. As technological advancements continue, new challenges emerge, while existing issues call for more thorough and innovative solutions. This issue leverages the latest cutting-edge technologies to offer novel perspectives and solutions for transportation challenges from multiple dimensions. Below is a brief overview of the articles included in this issue.In the first article of this issue, an analysis of driver behaviour is conducted, revealing its multi-dimensional and profound engineering significance in fields such as traffic engineering, road safety and intelligent driving. With the widespread research and application of autonomous driving and advanced driver-assistance systems in recent years, the study of the behavioural patterns of drivers during vehicle operation has increasingly benefitted multiple stakeholders, including automotive manufacturers, insurance companies and the drivers themselves. Salehikalam and Kordani (2025) proposed a quantitative method for identifying drivers' focus of attention by training a multi-objective optimisation model with real-world driving data, enabling clear observation of the primary targets that drivers prioritise during operation. The results of this study demonstrate strong performance in analysing driver behaviour and exploring the outcomes associated with different behavioural patterns.With the rapid development of electric vehicles (EVs) in recent years, a growing number of people have opted to purchase them. Consequently, researchers have turned their attention to the challenge of optimising the deployment of public charging stations (PCSs) for EVs within limited geographical areas. To address this issue, Zhou et al. (2025) proposed a framework for optimising the intra-regional deployment of PCSs for EVs, aiming to enhance the rationality and superiority of their placement. In a specific area of Changsha, the newly introduced coverage–location algorithm demonstrated higher average coverage and matching rates. As EV technology continues to advance and the demand for charging infrastructure surges in the future, this line of research will offer highly effective and practical solutions.High-risk road segments often represent accident-prone areas and are key targets requiring focused evaluation in highway maintenance optimisation. Shokat and Jameel (2025) proposed a detection scheme for high-risk road segments that integrates machine learning techniques. The research findings demonstrate that this technology holds significant potential for guiding pavement maintenance and improvement efforts, as well as enhancing road safety levels. Although the detection process still reveals certain limitations associated with underlying assumptions, the strategy proposed in this study – combining high-risk road segment detection with advanced machine learning techniques – merits recognition. Further advancements and outcomes from this line of research are eagerly anticipated.In the UK, railway maintenance also receives significant attention. Fathi et al. (2025) proposed an inverse analysis technique for detecting soil weakening zones in railway substructures caused by underground drainage network issues. Given that the UK's railway transport network is currently facing risks of structural failures due to malfunctions in underground drainage systems, the identification of soil weakening zones in railway substructures has emerged as a critically important consideration in railway maintenance efforts. This study integrates advanced machine learning techniques with sophisticated heuristic algorithms, demonstrating the powerful performance of artificial intelligence in addressing practical engineering challenges. This research direction also aligns with the ongoing focus of many researchers in the field.In addition to macroscopic research, this issue includes in-depth microscopic studies on cement materials. Yu et al. (2025) developed a hydration model for low-heat Portland cement through thermodynamic simulations, exploring the characteristics and evolutionary patterns of its permeability and thermal conductivity while revealing the underlying mechanisms of microstructural development. These findings hold significant implications for guiding the optimisation of cement performance and enhancing the quality and durability of engineering projects. Notably, the study enables quantitative assessment of how different environmental factors influence cement properties. Although the research is currently constrained by limited experimental data, it sufficiently demonstrates the advantages of integrating data simulation with physical experimentation in engineering applications.This issue features a total of five research articles that showcase the latest advancements across diverse fields of transportation studies. From these studies, it is evident that the development of cutting-edge scientific and technological tools has provided researchers with innovative approaches and methodologies to address challenges in the transportation sector. High-performance machine learning techniques, artificial intelligence methods and computer simulations have demonstrated exceptional effectiveness in both macroscopic monitoring/analysis and microscopic exploration. Overall, the research included in this issue advances the current stage of academic inquiry while enhancing the safety, efficiency and sustainability of transportation systems.On behalf of the esteemed editorial panel, I sincerely hope that you will fully recognise and deeply appreciate the groundbreaking innovation and irresistible allure embodied in the content meticulously curated for this distinguished issue of the Proceedings of the Institution of Civil Engineers – Transport. Here, I would like to extend my heartfelt gratitude to researchers worldwide who have generously shared their valuable research findings in the field of transportation. In addition, I am deeply thankful for the reviewers who dedicated substantial time and effort to evaluating their peers' manuscripts – their contributions are pivotal in ensuring the high quality of the published content in this issue. Finally, we sincerely invite all readers to explore the research presented in this issue. You can access the information you may seek by way of the 'Ahead of Print' section on the Transport Virtual Library homepage.Until next time!
Meng et al. (Sat,) studied this question.