Applications of Intelligent Technologies in the Life Cycle of Transportation Infrastructure

Adding inspection trains to existing railway timetables is a complex task that must balance operational efficiency and service reliability, which are essential for the sustainable operation and maintenance of high-speed railway infrastructure. To address this challenge, a feasible region-based space–time network modeling approach is proposed for incorporating Comprehensive Inspection Trains (CITs) into existing railway schedules, aiming to enhance inspection efficiency while minimizing operational disruptions. Firstly, the constraints that need to be considered when scheduling for CIT are comprehensively analysed and modelled, and a mixed-integer nonlinear model with the objective of minimizing the total number of stops is constructed. In order to eliminate the difficulty of solving this model, based on the original space–time network method, more kinds of train event arcs are introduced to accurately portray the train operation process; in particular, the extra time consumed due to the acceleration and deceleration process is also reflected in the network construction process. The feasibility of various event arcs is evaluated with time windows, and the original problem finally transforms into the equivalent shortest path problem on a feasible event arc network. The processing procedure includes key stages, such as station space–time discretization, interval operation event processing, station capacity handling, and network simplification. The experimental results indicate that the approach effectively resolves all station capacity conflicts, compresses inspection durations, and optimizes the number of stops. Remarkably, the number of non-full-speed inspection sections is reduced by 43.16%, demonstrating the model’s efficiency. Additionally, the proposed approach is computationally efficient, improves timetable capacity utilization for infrastructure inspection, and supports the sustainable operation of high-speed railway systems. Full article

Bridge maintenance research has increasingly expanded toward Digital Twin (DT), Structural Health Monitoring (SHM), Artificial Intelligence (AI), sensing technologies, and object-based information management. As maintenance paradigms shift from reactive to preventive and prescriptive approaches, digital twins have gained attention as a means of integrating fragmented technological components. However, the growing emphasis on AI- and DT-based analytics raises questions about how object-based information structures, sensing systems, SHM, AI-based analytics, and interoperability mechanisms are thematically connected and structurally associated. This study conducted a scientometric analysis of publications retrieved from the Web of Science (WoS) database without year restrictions. To avoid predetermining the importance of any single information-modeling technology, the main search query excluded BIM-related terms and combined the bridge domain, DT-related technology layer, and maintenance domain. After applying document type, language, and research-area filters, 406 records were screened by title and abstract. Six records that were not directly related to bridge DT maintenance research were excluded, resulting in a final analytical corpus of 400 records. Among these, 77 records were identified as the BIM-related subset for sensitivity analysis. Using VOSviewer-based bibliographic coupling as the core method, supported by keyword co-occurrence, density and overlay visualization, and CiteSpace analysis, this study examined contemporary research structures and historical intellectual bases. The results show that bridge DT development is not detached from existing technological foundations but reflects the cumulative convergence of object-based information modeling, sensing, SHM, AI-based analytics, and interoperability mechanisms within integrated DT architectures. Full article

Ride-hailing dispatch systems face significant challenges under fluctuating demand and dynamic traffic conditions, where efficient coordination is essential for both platform performance and driver income among large-scale ride-hailing vehicles. This paper constructs a grid-based ride-hailing vehicle dispatch decision model (GRV-DDM), which provides a structured and quantifiable representation of vehicles and orders, effectively capturing spatio-temporal heterogeneity in dynamic traffic environments. Based on this model, a Bi-Level Optimization Multi-Directional Dispatch Decision Algorithm (BO-MDDA) is proposed. At the macro level, evolutionary game theory is employed to adaptively guide collective vehicle strategies toward supply–demand equilibrium, while at the micro level, deep reinforcement learning optimizes individual drivers’ real-time dispatch decisions to maximize long-term profits. A bidirectional feedback mechanism is further designed to integrate macro-level collective intelligence with micro-level individual decision-making. Experimental results across diverse traffic scenarios demonstrate that the proposed approach outperforms classical dispatch algorithms in terms of efficiency and robustness. Full article

Multi-Agent Reinforcement Learning (MARL) provides an effective approach for urban multi-intersection traffic signal control. However, existing methods have faced two fundamental challenges, policy homogenization and inefficient credit assignment. The former led to convergent agent policies that failed to adapt to heterogeneous traffic patterns, while the latter prevented agents from accurately evaluating their individual contributions to system performance. To address these issues, this paper proposes a Multi-Agent Hierarchical Contrastive Learning Traffic Signal Control (MAHCL-TSC) model. The model incorporates an unsupervised contrastive learning module that enhances the discriminative power of state representations, thereby alleviating policy homogenization. Additionally, it designs a hierarchical graph convolutional credit allocation network that leverages road network topology and functional characteristics to enable structure-aware collaborative value estimation, significantly improving the precision of credit assignment. Based on these components, a Contrastive QTRAN with Hierarchical Graph Convolution (CQTRAN-HGC) algorithm is proposed, which jointly optimizes contrastive learning loss and QTRAN constraint loss. Experiments conducted in the Simulation of Urban Mobility (SUMO) simulation environment on 4 × 4 and 6 × 6 synthetic grid networks demonstrate that the proposed model improves traffic signal control performance under the tested structured simulation settings and shows potential scalability as the network size increases. Full article

Reliable traffic load characterization remains a critical challenge in many African countries due to the lack of continuous field measurements. This study developed an integrated dynamic traffic monitoring and weigh-in-motion system on representative highways in Kenya to obtain long-term, multi-source traffic data. Traffic operations were quantified across hourly, weekly, and monthly scales, including flow variability, vehicle class composition, axle loads, overload behavior, and speed distributions. Results indicate that the spatiotemporal characteristics of traffic volume show pronounced short-term fluctuations but strong long-term stability. Despite their lower proportion, multi-axle heavy trucks dominate structural loading, with overload ratios exceeding 80% and gross weights approaching 100 t. Over 60% of vehicles operate at medium-to-low speeds (20–60 km/h), extending load duration and increasing pavement damage potential. These combined effects indicate that average indicators alone underestimate true loading demand. The proposed framework provides field-based traffic load spectra and a transferable methodology for traffic monitoring and pavement design optimization across developing regions in Africa. Full article

In smart campus construction, behavior pattern recognition and perturbation analysis serve as the cornerstones for achieving a transition from passive response to dynamic regulation, with intelligent perception and anomaly diagnosis methods based on campus traffic flow underpinning transportation system resilience. Traditional research methods suffer from issues such as privacy risks, coarse modeling, and limitations from single data formats, labeling difficulties, and coverage gaps. This study proposes a refined semantic trajectory construction method that integrates multi-source data (e.g., mobile signaling data, maps and weather conditions), known as the Campus Transportation Semantic Trajectories Space (CTSTSpace) framework. It enables the precise identification of semantic origin–destination points from dynamic personnel trajectories, quantifies service performance through real-time road network mapping, and models multidimensional perturbations, achieving full campus coverage without complex labeling while ensuring robust privacy protection. Under clear weather conditions, the analysis demonstrates accurate recognition of travel behavior patterns (dwelling, aggregation, mobility, and congestion) that synchronize with class schedules, where vehicle speeds drop by over 50% during peak hours. Under rainy weather perturbations, it captured demand shifts (e.g., peak hour offsets of 30–60 min and a 6.8–9.2% reduction in long-distance dining trips) and speed reductions (52.15–73.74%). This approach provides critical insights for resilient smart campus traffic management. Full article

Promoting older adults’ active travel (AT) is important for healthy ageing, yet the optimal spatial units and scales for built environment (BE) interventions remain unclear. Existing studies often ignore the Modifiable Areal Unit Problem and fail to distinguish macro-scale land-use patterns from micro-scale street design under potentially nonlinear behavior–environment relationships. This study aims to clarify how multi-scale BE influences older adults’ AT and to identify the most effective intervention scale. Using survey data from 2494 older adults in Wuhan, China, we construct six behaviorally meaningful sliding units (5, 10, and 15 min walking network buffers and distance-equivalent Euclidean buffers), derive macro- and micro-scale indicators from GIS, census data, and street view images, and build separate Extreme Gradient Boosting (XGBoost) models with Accumulated Local Effects plots for interpretation. A model comparison reveals pronounced scale effects: network-based buffers systematically outperform circular buffers, and the 15 min walking network buffer emerges as the optimal intervention unit. Across all scales, BE variables contribute more to model performance than socio-demographic factors, and macro-scale attributes (e.g., land-use mix, facility density, and transit access) consistently outweigh micro-scale street features. Nonlinear effects and thresholds are identified for key density, accessibility, and streetscape indicators. These findings underscore the necessity of multi-scale analysis and support planning “15 min life circles” for older adults that prioritize macro-scale land-use and facility optimization, complemented by targeted, context-specific street-level improvements to create safe, age-friendly walking environments. Full article

Point cloud technology, characterized by its high-precision 3D geometric acquisition in complex railway environments, has become a cornerstone for the intelligent detection, monitoring, and maintenance of railway infrastructure. This paper provides a systematic review of point cloud applications across critical railway scenarios, encompassing track geometry extraction, infrastructure component identification, tunnel and bridge modeling, clearance and encroachment analysis, and structural condition monitoring. We evaluate various mobile and stationary acquisition platforms alongside their typical data processing workflows. Furthermore, this review synthesizes cutting-edge advancements in processing algorithms, with a focus on feature extraction, semantic segmentation, and the transformative impact of deep learning and artificial intelligence on data fusion. Notably, the paper explores the synergy between point clouds and computational mechanics, specifically the construction of high-fidelity digital twins through multi-physics coupling to enable real-time simulation of structural stress distribution and damage evolution. We critically analyze persistent technical bottlenecks, such as acquisition efficiency, monitoring precision, data fragmentation, environmental interference, and the complexities of multi-modal data fusion. Finally, the paper outlines future research trajectories, focusing on autonomous intelligent sensing, multi-sensor integration, and the comprehensive digital transformation of railway infrastructure management, aiming to provide a robust theoretical framework and technical roadmap for the sustainable intelligentization of global railway systems. Full article

Spatiotemporal traffic flow prediction is a fundamental task in intelligent transportation systems and is crucial for promoting efficient and sustainable urban mobility, especially under increasingly complex and rapidly evolving traffic conditions. To overcome the challenges of modeling high-order spatial dependencies and heterogeneous temporal patterns, this study develops a novel Hierarchical Spatiotemporal Graph–Hypergraph Network (HSTGHN). For spatial representation learning, a hypergraph neural module is employed to capture high-order interactions across the road network, while a hypernode mechanism is designed to characterize complex correlations among multiple road segments. Furthermore, an adaptive adjacency matrix is constructed in a data-driven manner and enriched with prior knowledge of bidirectional traffic flows, thereby enhancing the robustness and accuracy of graph structural representations. For temporal modeling, HSTGHN integrates the complementary strengths of Gated Recurrent Units (GRUs) and Transformers: GRUs effectively capture local sequential dependencies, whereas Transformers excel at modeling global dynamic patterns. This joint mechanism enables comprehensive learning of both short-term and long-term temporal dependencies. Extensive experiments on multiple benchmark datasets demonstrate that HSTGHN consistently outperforms state-of-the-art baselines in terms of prediction accuracy and stability, with particularly significant improvements in long-term forecasting and highly dynamic traffic scenarios. These improvements provide more reliable decision support for intelligent transportation systems, contributing to enhanced traffic efficiency, reduced congestion, and ultimately more sustainable urban mobility. Full article

This study investigates the transferability of measured road-damage data between distinct geographic domains using the YOLOv8s deep-learning framework. A comparative evaluation was performed on two datasets: the locally developed RDD2024_SA (South Africa) and the publicly available RDD2022_India (India). Five training–testing scenarios were designed to analyze intra- and inter-dataset generalization, emphasizing the influence of dataset scale, annotation consistency, and class structure on detection performance. When trained and tested within the same domain, YOLOv8s achieved high accuracy (mAP@0.5 > 0.95), confirming the strength of localized feature learning. However, performance degraded substantially under cross-domain testing, revealing a sensitivity to differences in road texture, illumination, and labeling style. Reducing the number of classes from six to four dominant types improved stability (mAP@0.5 ≈ 0.78) by mitigating annotation noise and class imbalance. Furthermore, a transfer-learning configuration, in which the India-trained model was fine-tuned on 20% of the South-African dataset, achieved mAP@0.5 = 0.86, demonstrating effective recovery of cross-domain detection performance. These findings highlight the importance of domain-aligned data preparation, targeted fine-tuning, and balanced class representation in building robust and transferable AI systems for sustainable, data-driven road maintenance. Full article

Aiming at the signal interference and feature recognition difficulties existing in the detection of concealed defects such as cracks and voids behind the tunnel lining, this study carried out a 1:1 reinforced concrete–steel arch frame composite lining model test; simulated the surrounding rock defects scenarios of three types of filling media, namely crushed stone, air, and water; and analyzed the time-domain, frequency-domain, and time–frequency-domain characteristics of the geological radar signal data. The research finds that the water-filled area generates a strong reflection due to the high dielectric constant, with the spectral peak reaching 712 MHz and the high-frequency component significantly enhanced. The peak frequency of the air-filled zone spectrum is 531 MHz, and the high-frequency bandwidth is broadened. The spectral peak of the crushed stone filling area is 507 MHz, with fast high-frequency attenuation and energy dispersion. The time-domain waveforms show that the amplitude in the water-filled area is the highest and the tailing is obvious, the waveform in the air-filled area is sharp, and the amplitude in the crushed stone-filled area is gentle. The peak frequency of the spectrum, the amplitude attenuation law, and the waveform shape can be used as the key indicators for discriminating the category of filling materials. The analysis method of feature fusion in the time–frequency domain has important engineering application value for improving the detection accuracy of geological radar in complex lining structures. Full article

Efficient generation of 3D mesoscale concrete models with high aggregate volume fractions remains challenging due to the computational complexity of detecting overlaps between irregularly shaped aggregates. This study presents an efficient modeling approach utilizing an improved Gilbert–Johnson–Keerthi (GJK) algorithm for rapid collision detection between convex polyhedral aggregates. The enhanced algorithm significantly reduces computational time by approximately 20–25% compared to the classical GJK algorithm, while maintaining detection accuracy, enabling the direct generation of high-volume-fraction (50%) concrete models without requiring additional settlement procedures. The “take-and-place” method is employed to generate and place aggregates according to specified gradation and volume fraction of aggregates. The model is validated against experimental uniaxial compression tests; the simulations accurately capture the macroscopic mechanical response and failure patterns, with the peak stress showing good agreement with experimental data (relative error ≈ 7.6%). The validated model is then employed in a comprehensive parametric study to systematically investigate the influence of key mesoscale parameters, providing profound insights into the underlying failure mechanisms. The proposed approach provides an efficient solution for rapid generation of realistic 3D mesoscale concrete models, facilitating more extensive parametric studies and mechanical analyses. Future extensions may include handling more complex aggregate shapes and leveraging parallel computing for further acceleration. Full article

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide. Full article