Search Results (2,808)

The safety and reliability of intelligent transportation systems (ITSs) can be greatly enhanced through adding redundancy in the information-sharing network of the vehicles. In this paper, we first model the mixed traffic of human-driven and autonomous vehicles as a distributed system observability problem using a network of communicating vehicles. We clearly show that a strongly connected network with a minimum of $n$ links (with $n$ as the network size) is sufficient for the observability of a mixed-traffic network. Then, we present graph-theoretic results on adding redundancy to the changing network of vehicles to make it resilient to the failure of a certain number of vehicles/sensors or their data-sharing links. Finally, we employ a distributed observer design to validate our results using a simple mixed-traffic example. Full article

Accurate characterization of pavement responses under real traffic loading is essential for improving pavement design reliability. This study presents SmartPave, a full-scale embedded monitoring system for measuring multilayer pavement responses under heavy vehicle loading. The system integrates embedded multi-sensors to capture stress, strain, temperature, and moisture within pavement layers. Field experiments were conducted under static and moving loading conditions. The results show that peak vertical stresses in the granular base were approximately 1.7–2.0 times higher than those at the subgrade, indicating stress attenuation with depth, while tensile strains at the bottom of the asphalt layer ranged between 200 and 350 µε. Lower vehicle speeds increased load duration and amplified viscoelastic strain responses. These findings demonstrate the capability of the system to provide reliable field data for mechanistic analysis and model calibration. Full article

With accelerating global urbanization and increasingly diverse demands for public spaces, promoting urban low-carbon transitions and enhancing residents’ quality of life have become central missions of modern urban development. As one of the city’s primary arteries, streets—through their green landscapes, slow-moving transportation systems, and public facilities—play an indispensable role in reducing carbon emissions, promoting healthy living, and improving residents’ well-being. In this study, the Yubei District of Chongqing was selected as the research area, and an automated evaluation framework was proposed for street visual quality, based on multi-source street view data and ensemble learning. PSP-Net semantic segmentation model was employed to extract eight key visual indicators from street view images, including green view index, Visual Entropy (Entropy), sky view factor (SVF), drivable space, sidewalk, safety facilities, buildings, and enclosure. Based on these features, a Stacking-based ensemble learning model was constructed, integrating multiple base models such as Random Forest, XGBoost, and LightGBM, with Linear Regression as the meta-learner, to predict street visual quality. The results demonstrate that the ensemble model significantly outperforms any single model, achieving a correlation coefficient (r) of 0.77 and effectively capturing the complex perceptual features of street environments. This study provides a reliable, intelligent, and quantitative method for large-scale evaluation of urban street visual quality, while supplying data support and decision-making references for street renewal and spatial optimization. Full article

Vehicular Ad Hoc Networks (VANETs) are a key enabler of intelligent transportation systems, supporting safety-critical and latency-sensitive applications through vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node mobility, rapidly changing network topologies, and heterogeneous wireless conditions pose significant challenges to traditional distributed networking approaches, particularly in terms of quality of service (QoS) stability and handover performance. Software-Defined Networking (SDN) offers promising solutions by enabling centralized control, programmability, and flexible deployment of network functions. This paper presents an SDN-enabled vehicular networking platform designed for realistic, system-level experimentation under dynamic mobility conditions. The proposed platform tightly couples microscopic vehicular mobility generated by SUMO with wireless network emulation in Mininet-WiFi, enabling real-time interaction between vehicle movement, wireless connectivity, and SDN control decisions, where a custom SDN controller implements mobility-aware traffic management and handover handling across roadside units. Extensive experimental scenarios evaluate throughput, packet loss, jitter, and end-to-end latency under varying traffic loads and mobility patterns. Results indicate that SDN-based centralized control improves QoS consistency relative to the unmanaged baseline configuration considered in this study. The proposed platform provides practical insights and a reproducible experimental framework for the design and evaluation of software-defined vehicular networking systems. Full article

Online ride-hailing platforms increasingly rely on differentiated incentive mechanisms to regulate driver participation and balance supply and demand. However, drivers’ adaptive responses to such incentives introduce dynamic feedback and uncertainty that static equilibrium models fail to capture. This study develops a dual-layer Stackelberg–evolutionary game framework in which the platform acts as a strategic leader setting the order allocation rates and prices, while heterogeneous drivers adapt their working-hour strategies through evolutionary dynamics. Using operational data from Ningbo, China, we calibrated the demand elasticity and driver cost parameters and identified endogenous fatigue-cost thresholds that govern regime shifts in strategy dominance. Simulation results show that uniform incentives tend to drive the system toward single-strategy lock-in, whereas differentiated order allocation and pricing effectively sustain multi-strategy coexistence and mitigate extreme supply polarization. The findings reveal how platform-led differentiation reshapes the evolutionary fitness landscape of drivers, providing actionable guidance for incentive design aimed at stabilizing supply structures, improving platform revenue, and protecting driver welfare. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. Full article

Pavement rutting is caused by grooves formed by vehicle traffic, affecting driving comfort, safety, and service life. Rutting detection methods have evolved from manual and automated approaches to intelligent detection for smart cities and maintenance. However, lightweight intelligent detection still faces challenges such as insufficient accuracy and technical complexity, and a mature system has yet to be established. This study aims to develop a portable intelligent terminal for pavement rut detection, which can address the challenges associated with traditional pavement rut detection while providing accuracy and reliability. In this study, rutting detection experiments were performed on a full-scale accelerated loading track to collect data on vibration acceleration, angular velocity, and attitude angles. Comparative experiments were carried out between traditional and lightweight detection methods. Subsequently, GRU-CNN, LSTM–Transformer, GRU, and LSTM models were developed to analyze and compare their performance in predicting rutting depth. The results show that the terminal operates stably, offering convenient usability and reliable data acquisition. Furthermore, vehicle angular velocity and roll angle emerge as critical indicators reflecting rutting impacts on driving states and prove suitable for pavement rut depth detection. The proposed GRU-CNN model achieves superior accuracy and overall performance relative to widely used models. Under synchronous detection conditions, the lightweight method yields a mean absolute error (MAE) of 1.22 mm, achieving performance improvements of 17.32%, 8.74%, and 10.08% over the LSTM–Transformer, GRU, and LSTM models, respectively. Additionally, the method yields a mean absolute percentage error of approximately 10.6%, representing error reductions of 15.87%, 19.08%, and 23.74% compared to the aforementioned baseline models, which meets application requirements. Innovation lies in the development of a lightweight intelligent terminal and GRU-CNN hybrid model that integrates vehicle dynamic parameters for large-scale pavement rutting detection. This study presents a lightweight, real-time pavement rutting detection method based on vehicle operation data for the construction and maintenance of smart cities and intelligent transportation infrastructure, combining the features of high cost effectiveness, high accuracy, and ease of large-scale application. Full article

The transition to AI-driven Cyber–Physical Systems has fundamentally reshaped transportation, introducing systemic risks that transcend traditional physical boundaries. Unlike prior reviews focused on isolated technological domains, this survey proposes a novel “End-to-End” analytical framework that models the causal propagation of vulnerabilities from physical sensing hardware to human cognitive responses. Synthesizing 140 research contributions (2017–2025), we evaluate the paradigm shift from deterministic control to Generative AI and Large Language Models (Transportation 5.0). To substantiate our framework, we introduce a structured cross-layer threat matrix and mathematically formalize the technology–cognition cascade, explicitly mapping how physical layer perturbations, such as optical jamming, bypass digital edge security to trigger hazardous behavioral reactions in human drivers. We conclude that ensuring the resilience of next-generation infrastructure requires a unified analytical architecture that formally bounds hardware constraints, algorithmic safety, and human trust. Full article

Traffic signal control at urban intersections is one of the key determinants of the overall efficiency of the transportation system, given its direct impact on travel time, congestion levels, and emissions of exhaust fumes. This study proposes an integrated hybrid model that combines a metaheuristic Genetic Algorithm for generating potential signal timing plans with fuzzy multi-criteria decision-making (MCDM) for their evaluation and selection of the optimal solution. In order to determine the relative importance of criteria, the fuzzy methods F-AHP, F-FUCOM, and F-PIPRECIA were employed, thus providing stable assessments of criteria importance under conditions of uncertainty and expert subjectivity. The ranking of generated alternatives was performed by employing the F-TOPSIS, F-WASPAS, and F-ARAS methods, while the robust decision-making rule approach was employed to develop a robust decision-making rule by integrating multiple MCDM methods. The proposed model was tested using data collected from a real urban intersection. The results show that the integrated hybrid approach enables a significantly more reliable selection of the optimal signal timing plan and achieves higher traffic management efficiency compared to traditional methods. The proposed model provides a flexible and scalable framework that can be adapted to different types of intersections and traffic demand conditions, thereby significantly contributing to the development of modern intelligent traffic management systems. Full article

To enhance the measuring capabilities of modern Non-Destructive Evaluation (NDE) devices, it has become essential to integrate standardized digitization services and industry-compliant functionalities. This perspective paper examines approaches for improving NDE systems by incorporating key Industry 4.0 technologies, specifically digital representations such as the Asset Administration Shell (AAS) and OPC UA (Open Platform Communications Unified Architecture). We discuss requirements for interoperable, semantically rich descriptions of NDE systems, outline how OPC UA information models and AAS submodels can be combined with MQTT-based transport, and illustrate these concepts through representative prototype implementations, including predictive maintenance and chatbot assistant use cases. By leveraging these technologies, NDE devices can be transformed into interoperable, data-rich, and intelligent components within smart industrial ecosystems. Compared with previous studies, this Perspective is the first to systematically bring together the requirements, architectural patterns, and evaluation criteria for digital representations designed specifically for NDE systems. It also provides, in a practical and accessible way, NDE-focused OPC UA and AAS-based architectures that support both predictive maintenance and LLM-assisted operator guidance. The presented implementations are at an early stage and serve as illustrative examples, while systematic quantitative validation is ongoing and is outlined as future work. Full article

Urban mobility is a central challenge for sustainable and inclusive cities, as climate change, congestion, and spatial inequality increasingly reveal mobility patterns as expressions of deeper social and spatial structures. Inclusive urban mobility examines whether transport systems equitably support the everyday movements and accessibility needs of historically marginalized and underserved populations. The integration of artificial intelligence with geographic information science, combined with multimodal geospatial data fusion, provides powerful tools to diagnose and address these disparities by integrating heterogeneous data sources such as satellite imagery, GPS trajectories, transit records, volunteered geographic information, and social sensing data into scalable, high-resolution urban mobility analytics. This paper presents a systematic survey of recent GeoAI studies that fuse multiple geospatial data modalities for key urban mobility tasks, including accessibility mapping, demand forecasting, and origin–destination flow prediction, with particular emphasis on inclusive and equity-oriented applications. The review examines 18 multimodal GeoAI studies identified through a PRISMA-ScR screening process from 57 candidate publications between 2019 and 2025. The survey synthesizes methodological trends across data-, feature-, and decision-level fusion strategies, highlights the growing use of deep learning architectures, and examines emerging techniques such as knowledge graphs, federated learning, and explainable AI that support equity-relevant insights across diverse urban contexts. Building on this synthesis, the review identifies persistent gaps in population coverage, multimodal integration, equity optimization, explainability, validation, and governance, which currently constrain the inclusiveness and robustness of GeoAI applications in urban mobility research. To address these challenges, the paper proposes a structured research roadmap linking these gaps to concrete methodological and governance directions including equity-aware loss functions, adaptive multimodal fusion pipelines, participatory and human-in-the-loop workflows, and urban data trusts to better align multimodal GeoAI with the goals of inclusive, just, and sustainable urban mobility systems. Full article

The development of modern transport energy systems is driven by increasing demands for energy efficiency, environmental sustainability, and operational reliability of vehicles. One of the most critical challenges in internal combustion engine operation is the cold-start condition, which results in increased fuel consumption, intensified component wear, and elevated emissions. Under these conditions, the development of intelligent thermal conditioning systems capable of accelerating engine warm-up and maintaining optimal thermal regimes becomes essential. This study proposes an intelligent engine and vehicle thermal conditioning system based on the integration of digital twin technology and phase-change thermal (PCM) energy storage. A digital twin architecture of the engine thermal conditioning system is developed to enable the integration of monitoring, simulation and predictive control of engine thermal processes. A mathematical model of the thermal conditioning system describing the dynamic temperature behavior of the engine, coolant, engine oil and PCM-based thermal energy storage units is formulated. A model predictive control strategy is implemented within the digital twin environment to support decision-making and optimization of engine thermal conditioning processes. Simulation and experimental results demonstrate that the proposed system can reduce engine warm-up time by 17.8–68.4%, decrease fuel consumption during the cold start phase by approximately 19.5–56.25%, and reduce harmful emissions. These findings confirm the potential of integrating digital twin technologies, predictive control and phase change thermal energy storage for improving the energy efficiency and environmental performance of modern transport power systems. Full article

Appointment-based lock scheduling can mitigate congestion at inland ship locks, but the inherent uncertainty in vessel arrivals frequently causes severe schedule degradation, disrupting the original lockage plans. To address this challenge, we develop an optimization framework that quantifies arrival-time uncertainty using a deep ensemble to generate generates reliable prediction intervals, and embeds a rescheduling mechanism for missed appointments within a multi-objective model. The model is solved with a hybrid heuristic that combines Differential Evolution, Variable Neighborhood Search, and Non-dominated Sorting Genetic Algorithm II (DE–VNS–NSGA-II). Compared to conventional evolutionary techniques, hybrid Particle Swarm Optimization (PSO) approaches, and recent advanced algorithms (GSAA-RL and ADEA-KC), the proposed algorithm effectively overcomes premature convergence in highly constrained discrete scheduling spaces by leveraging DE for robust global exploration and VNS for deep local refinement. In simulations with 143 vessels, the approach reduced average waiting time by 18.51% (28.63 h to 23.33 h), lowered the schedule adjustment rate by 9.02% (0.331 to 0.301), and decreased lock-utilization loss by 5.06% (0.413 to 0.392) relative to a standard baseline. The results demonstrate more stable schedules and more efficient use of lock capacity under uncertainty, providing a data-driven decision-support tool for lock operators to dynamically mitigate disruptions and reallocate passage quotas at inland navigation hubs. Full article

Satellite communication protocols are increasingly optimized in software-defined, multiorbital networks that combine broadband satellite systems, non-terrestrial 5G components, and inter-satellite transport. This review examines intelligent optimization across the physical, medium-access, network, and transport layers, with emphasis on what can be measured, what can be controlled, and what can be safely deployed under standards and operational constraints. This paper first positions the literature across DVB/ETSI, 3GPP NTN, CCSDS/DTN, LEO routing, and recent AI and digital-twin research. It then links standards-defined control surfaces to layer-specific measurements, feedback delays, and safety constraints and compares optimization families using deployment-relevant criteria such as observability, runtime predictability, verification burden, and robustness. The review argues that the central challenge is not only a simulation-to-reality gap but an evidence gap between experimental gains and operational trust. To address this gap, this paper analyzes delayed observability, rare events, bounded onboard compute, action surface mismatch, certification, and security; formalizes a generic constrained optimization problem with delayed observations and standards-compliant actions; and proposes a digital-twin-assisted research methodology supported by a worked beam-hopping example. The main conclusion is that future progress is most likely to come from hybrid, standards-compliant, and twin-assisted optimization methods whose performance claims are tied to calibration, traceability, and explicit rollback logic. Full article