Huseyin Ayan

Transport planning for earthquakes concentrates on damaged supply, yet in most events the network keeps running, and demand determines how it is used. This paper examines who stopped using urban rail after a strongly felt but largely undamaging earthquake, and what explains the spatial pattern of the loss. The analysis uses open smart-card counts by station, day and age group for more than 340 rail stations in Istanbul in April–May 2024 and 2025, around the moment magnitude 6.2 earthquake of 23 April 2025. It struck on a public holiday and was followed by two days of school and university closures. An event-study difference-in-differences design with a same-date 2024 control separates the earthquake from the holiday, bridge-day placebos bound the long-weekend effect, and station-level regressions for 270 stations relate losses to modelled shaking, user age and neighbourhood socio-economic status. Ridership fell by about one-third on the day after the earthquake and was within 5% of normal within a week, although a small residual shortfall cannot be ruled out. The loss was largest among young users, as the closures imply. However, users aged 30–60, whom the closures affected only indirectly, still travelled about one-quarter less. Shaking intensity does not explain which stations lost most; losses were larger where stations served more young users and where neighbourhood status was higher. Overall, the demand shock of this moderate earthquake was large, mostly short-lived and socially graded, shaped more by institutional closures and the social composition of station catchments than by the strength of shaking.

Mountain transport corridors are increasingly exposed to compound flood hazards, yet conventional vulnerability assessments often depend on hydrological or hydraulic information that is sparse, incomplete, or spatially unrepresentative in steep and data-constrained terrain. This limitation is particularly critical in Himalayan valleys, where rivers, roads, bridges, settlements, and energy infrastructure are compressed into the same narrow geomorphic corridor. The 26 August 2026 Nepal flash flood exemplified this vulnerability, causing widespread road and bridge destruction along the strategically important Bhote Koshi–Trishuli corridor and disrupting regional accessibility, hydropower operations, and Nepal’s principal land connection with China. This study investigates whether river-planform geometry can provide an independent spatial indicator of transport-corridor fragility. A multi-scale framework integrating fractal and wavelet analyses was applied to river geometry and evaluated against independently mapped post-event road damage. The resulting morphometric anomalies showed strong and scale-persistent correspondence with damaged road sections, while wavelet analysis revealed markedly greater geometric variability in affected reaches. Statistical validation further indicated that the observed concentration of destroyed roads was unlikely to arise from random spatial association. These findings demonstrate that river-planform complexity can provide a robust, data-efficient screening layer for prioritizing vulnerable infrastructure where conventional hazard information is limited.

Light rail transit (LRT) is seen as an environmentally friendly public transport alternative compared to other motorised modes due to reduced air pollution, lower energy consumption, and greenhouse gas emissions. This study aims to investigate regular users' perception of LRT and whether socio-demographic variables influence its use. A questionnaire with attitudinal statements categorised into six main themes, namely service quality, network and infrastructure, accessibility, user benefits, sustainability aspects, and socio-demographics, was piloted and distributed using online channels across the Tyne and Wear Metro catchment area. Using a combination of Tree Augmented Naïve (TAN) Bayes and Partial Least Square Structural Equation Modelling (PLS-SEM) techniques, the interrelationships between statistically significant perception variables affecting regular users' metro use were identified. The analysis emphasises the influential role of accessibility, direct benefits, policy implementation, reliability and availability, sustainability, and security and safety in enhancing user satisfaction. Notable interrelationships were also found, including positive associations between comfort-customer care and reliability-availability, and between safety-security and accessibility. Valuable insights into factors affecting metro use provide transport planners and policymakers with scientific evidence to make informed decisions on improving the metro system's efficiency and effectiveness, enabling cost-effective investments to make LRT more attractive and increase patronage.

The integration of electric vehicles (EVs) into smart grids (SGs) is reshaping both energy systems and mobility infrastructures. This review presents a comprehensive and cross-disciplinary synthesis of current technologies, methodologies, and challenges associated with EV–SG interaction. Unlike prior reviews that address these aspects in isolation, this work uniquely connects three critical pillars: (i) the evolution of energy storage technologies, including lithium-ion, second-life, and hybrid systems; (ii) optimisation and predictive control techniques using artificial intelligence (AI) for real-time energy management and vehicle-to-grid (V2G) coordination; and (iii) cybersecurity risks and post-quantum solutions required to safeguard increasingly decentralised and data-intensive grid environments. The novelty of this review lies in its integrated perspective, highlighting how emerging innovations, such as federated AI models, blockchain-secured V2G transactions, digital twin simulations, and quantum-safe cryptography, are converging to overcome existing limitations in scalability, resilience, and interoperability. Furthermore, we identify underexplored research gaps, such as standardisation of bidirectional communication protocols, regulatory inertia in V2G market participation, and the lack of unified privacy-preserving data architectures. By mapping current advancements and outlining a strategic research roadmap, this article provides a forward-looking foundation for the development of secure, flexible, and grid-responsive EV ecosystems. The findings support policymakers, engineers, and researchers in advancing the technical and regulatory landscape necessary to scale EV–SG integration within sustainable smart cities.

Among urban public transport systems, light rail, mass transit, and tram systems offer sustainable travel options. However, many of these systems, particularly in developed countries, fail to meet user needs and the expectations of transport authorities. Increasing the demand for urban rail systems as an alternative to private cars is essential for achieving net zero targets and Sustainable Development Goals. This study investigates the factors influencing urban rail demand using qualitative data analysis, with a focus on thematic analysis. A systematic review of 53 studies from the UK, Europe, and worldwide, including journal articles and transport research reports, was conducted and coded using NVivo Version 15 software. Six main categories emerged: land use and accessibility, service quality, user benefits, governance, sustainability aspects, and user-focused elements. These categories, along with their themes and sub-themes, were analysed using cross-tabulations to compare attributes across domains. The key findings indicate that accessibility and intermodal connectivity are crucial for encouraging urban rail use, while ticketing, station facilities, walkability, travel costs, ventilation, and security also moderately influence user preferences. This study provides essential guidelines for policymakers and transport providers to improve urban rail systems and informed the development of a questionnaire to explore the interrelationships of these factors, discussed in a forthcoming paper.

The increasing adoption of electric vehicles (EVs) necessitates a comprehensive understanding of charging patterns and user behaviour to enable future transportation infrastructure to be planned and designed to meet user needs. This study uses machine learning to predict the costs of EV charging sessions and analyse user behaviour to support strategic planning and decision‐making. We examined data that included factors such as total energy consumption and charging duration, and compared three models: linear regression, random forest, and gradient boosting. The gradient boosting model performed the best, with a mean squared error of 0.041 and an ‐squared () of 0.91. Additionally, the analysis of user behaviour revealed peak charging times between 6:00 PM (18:00) and 9:00 PM (21:00), with the majority of sessions occurring on weekdays, particularly Wednesdays. Most users preferred charging infrastructures within a 10‐mile radius. These insights not only enhance the understanding of current EV charging behaviours but also provide valuable information for local authorities and decision‐makers in transportation planning and infrastructure development. By integrating predictive modelling and behavioural analysis, this research offers a novel and robust framework for designing EV charging networks, addressing user needs, and advancing the sustainability of urban transportation systems. This approach not only supports the efficient deployment of charging infrastructures but also introduces the concept of charging comfort by aligning infrastructure development with real user needs. Unlike traditional methods that overlook user preferences and waiting times, our model integrates behavioural analysis to improve the overall user experience. By quantifying when, where, and how users prefer to charge their vehicles, this framework supports not only infrastructure optimisation but also enhances user satisfaction, a key factor in accelerating EV adoption and reducing the environmental burden of urban mobility.