The evolution of transport infrastructure through megaprojects encompasses extensive, intricate and transformative initiatives designed to enhance the efficiency, capacity and connectivity of transportation networks. These endeavors typically necessitate substantial financial investment, the application of advanced technologies and meticulous planning processes. The spectrum of such megaprojects includes automated rail systems as well as challenging road and tunnel constructions. A critical aspect of these transportation initiatives is their commitment to sustainability and environmentally friendly practices. Collectively, these megaprojects signify remarkable progress in transport infrastructure, fundamentally altering the movement of people and goods worldwide. Furthermore, many of these initiatives prioritize sustainability, the integration of intelligent technologies and the establishment of interconnected global economies – leading to sustainable development for cities.
Subsequently, this Special Issue (SI) investigates the intricate interplay between the development of mega transportation infrastructure projects and their implications for sustainable development, with a particular emphasis on significant transportation initiatives. It advocates for a transformation in both technical and methodological contemporary policies, spanning from the identification of issues and options to their eventual adoption. Such changes are essential for facilitating a transition towards more sustainable transport systems, while also acknowledging the necessity of integrating other transportation modes. Consequently, this SI underscores the various challenges encountered, the complexities associated with the use of performance metrics, and the vital institutional factors that require attention. The contributions included are informed by current international case studies from post-industrialized nations such as the United Kingdom, Australia and Italy, among others. Furthermore, the SI aims to investigate innovative strategies for sustainable planning and development concerning major transport infrastructure projects, thereby concentrating on the principal planning hurdles, challenges and uncertainties that arise throughout the project development process.
This SI is anchored in a globally collaborative research group. Its primary strength lies in the ability to apply the principles of sustainable transportation planning to practical development scenarios. By employing diverse methodologies, the issue aims to demonstrate the journey of sustainable development from the inception of projects to their completion. The insights and lessons derived from the articles within this SI are designed to provide valuable guidance based on the findings and results of each study. Consequently, these insights can support a range of researchers, practitioners and other essential stakeholders engaged in transportation megaprojects as they strive to address the sustainability challenges associated with such initiatives through improved planning and development strategies. Furthermore, a significant body of recent literature has emerged that addresses a range of infrastructure initiatives and their associated processes. Nevertheless, this SI will concentrate specifically on advancements in transport infrastructure, which serve as a foundation for the planning and development of megaprojects. This focus entails a thorough examination and assessment of diverse sustainable planning frameworks and the current processes involved in project development.
Selected contributions
This special issue consists of eleven articles that explore a diverse array of subjects related to the specified theme, with a particular emphasis on recent developments in the field of transportation megaprojects.
In their research, Farnes et al. (2025) examined and evaluated the advantages and disadvantages associated with transport-oriented development (TOD) in neighborhoods located near public transport hubs. Their exploratory study revealed that existing literature supports the notion that TOD fulfills its potential to foster high-density, mixed-use, walkable communities that are underpinned by transport infrastructure, thereby enhancing accessibility, reducing reliance on vehicles, alleviating traffic congestion, curbing urban sprawl and mitigating pollution. However, they noticed that there is a scarcity of literature addressing the adverse effects of TOD, particularly concerning social exclusion, crime, sustainability and the gentrification of local communities. Moreover, Pagliara et al. (2025) introduce a methodology aimed at assessing the benefits and costs associated with stakeholder engagement (SE). They emphasize that effective decision-making in the transport sector necessitates the involvement of key stakeholders, yet the specific benefits and costs of SE remain unclear. Their proposed methodology sought to quantify these aspects and was applied in a case study to identify both direct and indirect cost and benefit drivers within the relevant context. Furthermore, the authors provided guidelines for quantifying the costs and benefits of SE in transport projects, building upon an established framework that integrates SE into the broader transportation decision-making process. They also presented various examples of both monetary and non-monetary costs and benefits associated with SE.
In their research, Al-Mhdawi et al. (2025) underlined the importance of examining a range of studies concerning the critical risks associated with Critical Infrastructure Projects (CIPs). By analyzing a selection of premier academic journals published between 2011 and 2023, they identified a total of 128 distinct risks, which were categorized into ten specific groups: construction, cultural, environmental, financial, legal, management, market, political, safety and technical risks. Additionally, they found that literature reviews, in conjunction with questionnaire surveys, were the predominant methods employed for identifying Critical Infrastructure Risks (CIRs) compared to other approaches. Their research also revealed that oil and gas projects were the most frequently investigated topics within the reviewed literature. Furthermore, it was noted that research contributions from Iran, the USA and China were particularly prominent in the field of CIRs, collectively representing 49.65% of the articles analyzed.
In addressing the demands for extensive rail infrastructure within urban and metropolitan regions, Mihocic et al. (2025) explored the extent to which social, environmental and economic considerations impact governmental planning for sustainable rail projects. Their investigation focused on two significant Australian rail initiatives: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE). Through factor analysis, the authors identified that in the case of the MRE, elements such as network capacity, accessibility, employment and urban planning were prominently featured, whereas political influences and economic growth appeared less frequently. This disparity in the prominence of various factors likely contributed to the SWRL being deemed more sustainable than the MRE. Additionally, a SWOT analysis revealed that the MRE possessed certain advantages over the SWRL, particularly in areas like resource utilization, waste management and the preservation of natural habitats. These analyses indicated that the assessment of a project’s sustainability can be inherently subjective, influenced by the specific methodologies employed. Conversely, Okonta et al. (2025) advocate for a systematic approach to sustainable railway infrastructure planning by proposing a flexible framework that incorporates systems thinking principles and acknowledges the involvement of key stakeholders. Their case study focused on Milton Keynes in the United Kingdom, where they discovered that the chosen systems thinking tools and techniques effectively facilitate the mapping of stakeholders and their attributes. Thus enhancing the development of sustainable rail transport systems while considering the intricate systems in which these stakeholders operate.
Rehman et al. (2025) identified that autonomous vehicles (AVs) possess the capacity to significantly alter the infrastructure, mobility and social welfare frameworks in New Zealand, particularly in light of the country’s unique challenges related to population growth and road safety. Nonetheless, they emphasized that the acceptance of these technologies by the public, the concurrent evolution of regulatory frameworks, and the establishment of trust—both interpersonal and institutional—regarding AV technology present considerable obstacles. Their research pinpointed nine critical determinants that influence trust in the domains of human–machine interaction (HMI) and legal preparedness, which were examined concerning eight distinct AV scenarios across three developmental phases. This analysis culminated in the creation of a guiding framework, termed the HMI autonomous driving events relationship identification framework (HMI-ADERIF), aimed at facilitating the implementation of AVs in New Zealand. In a complementary study, Thakur et al. (2025) explored the factors affecting the adoption intention of electric vehicles (EVs), concluding that attitudes, subjective norms and perceived trust play pivotal roles in this process. Their results offer valuable insights for policymakers and marketers, suggesting that targeted marketing strategies should emphasize the environmental benefits, cost efficiency and advanced technology associated with EVs. Furthermore, the limitations identified in their study serve as a foundation for future research endeavors.
Furthermore, Karunarathna et al. (2025) emphasized that delays in the shifting of utilities during road construction projects have extensive consequences, asserting that such delays not only prolong the overall project duration but also escalate costs and complicate resource management. Their research specifically examined the impact of utility shifting delays on time extension claims within road construction projects in Sri Lanka. The authors identified a total of 33 factors contributing to these delays, from which 11 critical factors were selected based on their significant likelihood and impact. Ultimately, they proposed 45 strategies aimed at mitigating the most critical delay causes. In a separate study, Elghaish et al. (2025) explored the application of artificial intelligence (AI) and deep learning (DL) techniques for the detection of cracks, reporting that the accuracy rates of three optimization algorithms, ADAM, SGDM, and RMSProp, when applied to a five-layer DL model were 97.4%, 98.2% and 96.09%, respectively. To further improve accuracy, they implemented eight feature selection algorithms, with particle swarm optimization (PSO) yielding the highest F-score of 98.72. The model demonstrated a precision of 98.19% and an F-score of 98.72 when utilizing PSO, indicating its high accuracy and effectiveness in assessing pavement crack conditions. Consequently, this model holds significant promise for reducing the labor intensity associated with crack detection and evaluation.
In their study, Gharehbaghi et al. (2025) explored an innovative approach to enhance the performance of rail systems. This involved assessing rail system dynamics (SD) through the application of discrete event simulation (DES). They emphasized that urban transportation systems worldwide are grappling with persistent challenges, many of which stem from the complexities inherent in rail SD. To address these issues, the researchers employed DES as a foundational tool for analyzing the dynamics of the Melbourne Metro Rail. They developed processes related to transportation SD, focusing on aspects such as efficiency and reliability. Through the use of DES, the research provided insights into the operational dynamics of the Melbourne Metro Rail. The authors accentuated that while the Melbourne Metro Rail is still under development, the DES framework they created effectively evaluated the system’s requirements concerning functionality, performance and integration. Their analysis involved simulating the optimization process of the Melbourne Metro Rail, conducting a total of 50 trials, with 25 samples each for efficiency and reliability. The simulation not only examined the SD but also identified several shortcomings. The findings indicated that information and communication technology (ICT) plays a crucial role in system application. The development of their DES underscored that both efficiency and reliability are vital components of SD, essential for the operational effectiveness of the Melbourne Metro Rail system. They concluded that the three critical elements of SD—capacity, continuity and integration—are fundamental to enhancing the functionality of the Melbourne Metro Rail system.
In the final analysis of this special issue, Abu Dabous et al. (2025) emphasized the critical role of prediction models as vital instruments for transportation agencies in forecasting the state of bridge decks utilizing existing data, with AI being a key component in this endeavor. The objective of their study was to develop a predictive model for bridge deck conditions by evaluating a range of classification and regression algorithms. Their results indicated that classification algorithms significantly surpass regression algorithms in terms of accuracy for predicting deck condition ratings. They specifically stressed the random forest classifier, which demonstrated a minimal mean absolute error (MAE) of 0.369 when utilizing eleven features, as the most effective model for condition prediction. The research identified several key features influencing predictions, including superstructure condition, age, structural evaluation, substructure condition, inventory rating, maximum span length, deck area, average daily traffic, operating rating, deck width and the number of spans, thereby providing valuable insights for practical applications in the field.
