Search Results (314)

This study investigates the potential of Floating Car Data (FCD) collected from Volkswagen Group vehicles since 2022 for monitoring pavement conditions along two Italian road stretches. While such data are primarily gathered to analyze vehicle dynamics and mechanical behaviour, here, they are repurposed to support road network assessment through the estimation of the International Roughness Index (IRI). Daily aggregated datasets provided by NIRA Dynamics were analyzed to evaluate their reliability in detecting spatial and temporal variations in surface conditions. The results show that FCD can effectively identify critical sections requiring maintenance, track IRI variations over time, and assess the performance of surface rehabilitation, with high consistency on single-lane roads. On multi-lane roads, limitations emerged due to data aggregation across lanes, leading to reduced accuracy. Nevertheless, FCD proved to be a cost-efficient and continuously available source of information, particularly valuable for identifying temporal changes and supporting the evaluation of maintenance interventions. Further calibration is needed to enhance alignment with high-performance measurement systems, considering data density at the section level. Overall, the findings highlight the suitability of FCD as a scalable solution for real-time monitoring and long-term maintenance planning, contributing to more sustainable management of road infrastructure. Full article

This review surveys theoretical frameworks developed to describe rubber contact and friction on rough surfaces, with a particular focus on tire–road interaction. It begins with classical continuum approaches, which provide valuable foundations but show limitations when applied to viscoelastic materials and multiscale roughness. More recent formulations are then examined, including the Klüppel–Heinrich model, which couples fractal surface descriptions with viscoelastic dissipation, and Persson’s theory, which applies a statistical mechanics perspective and later integrates flash temperature effects. Grosch’s pioneering experimental work is also revisited as a key empirical reference linking friction, velocity, and temperature. A comparative discussion highlights the ability of these models to capture scale-dependent contact and energy dissipation while also noting practical challenges such as calibration requirements, parameter sensitivity, and computational costs. Persistent issues include the definition of cutoff criteria for roughness spectra, the treatment of adhesion under realistic operating conditions, and the translation of detailed power spectral density (PSD) data into usable inputs for predictive models. The review emphasizes progress in connecting material rheology, surface characterization, and operating conditions but also underscores the gap between theoretical predictions and real tire–road performance. Bridging this gap will require hybrid approaches that combine physics-based and data-driven methods, supported by advances in surface metrology, in situ friction measurements, and machine learning. Overall, the paper provides a critical synthesis of current models and outlines future directions toward more predictive and application-oriented tire–road friction modeling. Full article

Here we present a high-resolution landslide susceptibility model for Guiyang County, China, developed to support sustainable disaster risk management. Our approach couples optimized positive and negative training samples with an ensemble of machine-learning algorithms to maximize predictive fidelity. We compiled a georeferenced inventory of 146 landslides by integrating historical records with systematic field validation. Sample optimization was central to our methodology: landslide presence points were refined via buffer-based dilution, and four classifiers—SVM, LDA, RF, and ET—were trained with identical covariate sets to ensure comparability. Three strategies for selecting pseudo-absences—buffering, low-slope filtering, and coupling with the IOE—were benchmarked. The Slope-IOE-O model, which synergizes low-gradient screening with entropy-weighted sampling, yielded the highest predictive capacity (AUC = 0.965). SHAP-based interpretability revealed that slope, monthly maximum rainfall, surface roughness, and elevation collectively dominate susceptibility, with pronounced non-linearities and interactions. Slope contribution peaks at 20–30°, monthly maximum rainfall exhibits a critical threshold near 225 mm, and the synergy between high roughness and road density amplifies landslide risk. Spatially, susceptibility follows a pronounced north–south gradient, with high-hazard corridors aligned along northern and southern mountain belts and the urban core of southern Guiyang County. By integrating rigorously curated training data with robust machine-learning workflows, this study provides a transferable framework for proactive landslide risk assessment, offering scientific support for sustainable land-use planning and resilient development in mountainous regions. Full article

Accurate prediction of the International Roughness Index (IRI) is critical for road safety and maintenance decisions. In this study, we propose a novel Physics-Aware Informer (PA-Informer) model that integrates the efficiency of the Informer structure with physics constraints derived from partial differential equations (PDEs). The model addresses two key challenges: (1) performance degradation in short-sequence scenarios, and (2) the lack of physics constraints in conventional data-driven models. By embedding residual PDEs to link IRI with influencing factors such as temperature, precipitation, and joint displacement, and introducing a dynamic weighting strategy for balancing data-driven and physics-informed losses, the PA-Informer achieves robust and accurate predictions. Experimental results, based on data from four climatic regions in China, demonstrate its superior performance. The model achieves a Mean Squared Error (MSE) of 0.0165 and R² of 0.962 with an input window length of 30 weeks, and an MSE of 0.0152 and R² with an input window length of 120 weeks. Its accuracy is superior to that of other models, and the stability of the model when the input window length changes is far better than that of other models. Sensitivity analysis highlights joint displacement and internal stress as the most influential features, with stable sensitivity coefficients (S_p ≈ 0.89 and S_p ≈ 0.81). These findings validate the PA-Informer as a reliable and scalable tool for predicting pavement performance under diverse conditions, offering significant improvements over other IRI prediction models. Full article

Travel safety and comfort depend on the design and maintenance of road and stormwater drainage systems. In low-lying areas, poor drainage systems can—especially near underpasses—lead to flooding and serious risks, such as reduced load-bearing capacity hydroplaning, where tires lose grip. This study focuses on the effect of granular material deposits on the surface roughness of roadside gutters, as expressed through the Gauckler–Strickler coefficient. The literature equations have pointed out that this coefficient is largely affected by the grain size distribution of granular material. To this end, a field study was carried out in six urban roads in San Nicola la Strada, Italy, with the objectives of the following: (1) identifying the grain size distribution of the material deposited in roadside gutters; (2) estimating how such material decreased in the cross-sectional area of the gutters, as well as increasing their flow resistance, ultimately resulting in decreased water conveyance. Considering gutters with deposited material rather than clean ones results in the failure of three out of six gutters to effectively drain stormwater. Full article

In response to the problems of rough modeling and insufficient coverage of edge interaction scenarios in autonomous driving tests, this paper proposes a scene generation method based on interaction coding. The method constructs a hierarchical parameter system of function–logic–specific scene, uses the time difference of arrival at interaction points (TTC_diff) to determine the critical state of interaction, and realizes the efficient generation and iterative optimization of high-risk scenes. Taking the unprotected left turn at the signal intersection of urban roads as an example, the interaction coding combination is determined in combination with real traffic data, the test scene compatible with OpenSCENARIO is generated, and CARLA0.9.15 is called for test verification. The results show that the interaction intensity is significantly negatively correlated with the trustworthiness score (−0.815), the generated scene has high coverage, and both safety and challenge are taken into account. Compared with the simulated annealing method, the method in this paper performs better in terms of iteration efficiency, scene difficulty control, and score stability, which provides an efficient and reliable test strategy for the trustworthiness evaluation of autonomous driving. Full article

Many agencies use pavement condition assessments such as the Pavement Surface Evaluation and Rating (PASER) and Pavement Condition Index (PCI) to develop localized pavement management programs. However, both techniques involve some subjectivity and inconsistent measurement practices, making it difficult to scale uniformly across all 86 thousand miles of local agency roadway in Indiana’s 92 counties. International Roughness Index (IRI) data is one emerging data source that could address this need. This paper evaluates the feasibility of using Connected Vehicle-estimated IRI (IRI_CVe) data for long-term statewide pavement monitoring on local roads. The analysis is based on approximately 4.1 billion daily IRI_CVe records collected over a multi-year study period from connected vehicles operating throughout the state. A modular data processing workflow was developed to clean and process these records and is presented in detail in the paper. The study includes network-level condition comparisons, insights on spatiotemporal trends, and localized segment-level condition monitoring. In 2024, approximately 53% of paved local roads in Indiana had at least one IRI_CVe observation per year. Coverage varied widely by county: for example, 79% of roads in urban Hamilton County had coverage, but only 14% had coverage in rural Martin County. The findings in this study demonstrate the potential of IRI_CVe to support local agency pavement asset management by providing cost-effective data-driven insights in near real-time. Full article

As smart cities evolve, integrating new technologies into school transportation is becoming increasingly important to ensure student comfort and safety. Monitoring and enhancing comfort during daily commutes can significantly influence well-being and learning readiness. However, most existing research addresses isolated factors, which limits the development of comprehensive and scalable solutions. This study presents the design and implementation of a low-cost, generalized IoT-based system for monitoring comfort in school transportation. The system processes multiple environmental and operational signals, and these data are transmitted to a cloud computing platform for real-time analysis. Signal processing incorporates standardized metrics, such as root mean square (RMS) values from ISO 2631-1 for vibration assessment. In addition, machine learning techniques, including a Random Forest classifier and ensemble-based models, are applied to classify ride comfort levels using both road roughness and environmental variables. The results show that stacked multisensor fusion achieved a significant improvement in classification performance compared with vibration-only models. The platform also integrates route visualization with commuting time per student, providing valuable information to assess the impact of travel duration on school mobility. Full article

The dynamic load caused by vehicle vibration due to an uneven pavement surface is a primary factor affecting the structural performance and service life of asphalt pavement. As the principles of vibration mechanics, in conjunction with the coherence function of the vehicle’s left and right wheels, along with the lag between front and rear wheels, the entire vehicle vibration model for three-axle and four-axle heavy-load vehicles was developed using Simulink software. Through simulation, the root-mean-square value of the dynamic load and the dynamic load coefficient of the vehicle with different pavement roughness grades, speeds, loads, and cornering radii were analyzed. The outcomes demonstrate that a nonlinear rise in the wheel dynamic load occurs when pavement roughness increases. The greater the speed, the greater the impact of pavement roughness on the dynamic load. An increase in vehicle load tends to reduce vehicle vibrations. The interaction between vehicle vibration frequency and road excitation frequency is essential in figuring out the loads, and a negative influence on the pavement structure should be given more attention when the vehicle is driving at low speed. The dynamic load coefficient of the left and right wheels is greatly affected when the vehicle is in a lateral tilt. The findings offer valuable insights for selecting appropriate loads in pavement structure design. By constructing 11 degrees of freedom for a three-axle vehicle and 16 degrees of freedom for a four-axle heavy-duty vehicle model, the dynamic load variation law under different roughness excitation conditions is systematically analyzed. The results can be applied to the selection of vehicle load in asphalt pavement design to make it closer to the actual driving state, which will be helpful for improving accuracy in the design of pavement structure and avoiding early damage to the pavement. Full article

In order to investigate the influence of different tire textures, pavement types, and vehicle parameters on the tire/road noise level and its spectrum characteristics, 19 kinds of asphalt pavement main structures of RIOHTrack full-scale test track were tested by the close-proximity trailer (CPXT) tire/road noise detection method. Considering investigated parameters such as tire texture, vehicle speed, and trailer axle weight, and relying on multi-functional road condition rapid detection vehicle and laboratory tests to collect a variety of road surface information and material parameters, a multiple-linear-regression model of tire/road surface noise level of RIOHTrack (Research Institute of Highway Full-scale Test Track) asphalt pavement was constructed. Finally, the causes of noise level differences among different influencing factors were further analyzed through spectrum characteristics. The results show that vehicle speed is the most important factor affecting tire/road noise. The noise level of different tires varies due to different textures, but the noise level among different trailer axle weights is roughly the same. Vehicle speed (v), FWD center deflection (D₀), surface asphalt mixture air voids (VV), sensor-measured texture depth (SMTD) and international roughness index (IRI) were selected to establish the noise prediction models of different tire textures. Noise spectrum analysis shows that the spectrum of different vehicle speeds is significantly wide in the full frequency range, and the spectrum variation of differently textured tires is mainly concentrated in a certain range of the peak frequency. The noise spectrum curve of porous asphalt concrete (PAC13) is significantly lower than that of other asphalt mixtures in the full frequency range above 800Hz, indicating a greater noise reduction effect. Full article

This study presents a smartphone-based dual-index framework for evaluating bicycle pavement conditions, aimed at supporting sustainable urban mobility and cyclist safety. Conventional assessment methods, such as the International Roughness Index (IRI), often overlook short-range discontinuities and are impractical for micromobility-scale infrastructure monitoring. To address these limitations, two perception-aligned indices were developed: the Bicycle Road Roughness Index (BRI), reflecting sustained surface discomfort, and the Faulting Impact Index (FII), quantifying acute vertical shocks. Both indices were calibrated through structured panel surveys involving 40 experienced cyclists and validated using high-frequency tri-axial acceleration data collected in both experimental and field settings. Regression analysis confirmed strong alignment between sensor signals and user perception (R² = 0.74 for BRI; R² = 0.76 for FII). A five-grade classification system was proposed, with critical FII thresholds at 87.3 m/s² for “risky” and 119.4 m/s² for “not rideable” conditions. Field validation across four diverse sites revealed over 380 hazard segments requiring attention, demonstrating the framework’s ability to identify localized risks that may be masked by traditional metrics. By leveraging off-the-shelf smartphones and open-source sensing tools, the proposed approach enables scalable, low-cost, and cyclist-centered diagnostics. The dual-index system not only enhances rideability evaluation but also supports targeted maintenance planning, real-time hazard detection, and broader efforts toward data-driven, sustainable micromobility management. Full article

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management. Full article

Landslide susceptibility modelling represents an efficient approach to enhance disaster management and mitigation strategies. The focus of this paper lies in the development of a landslide susceptibility evaluation in northwestern Tunisia using the Analytical Hierarchy Process (AHP) and Artificial Neural Network (ANN) approaches. The used database covers 286 landslides, including ten landslide factor maps: rainfall, slope, aspect, topographic roughness index, lithology, land use and land cover, distance from streams, drainage density, lineament density, and distance from roads. The AHP and ANN approaches were applied to classify the factors by analyzing the correlation relationship between landslide distribution and the significance of associated factors. The Landslide Susceptibility Index result reveals five susceptible zones organized from very low to very high risk, where the zones with the highest risks are associated with the combination of extreme amounts of rainfall and steep slope. The performance of the models was confirmed utilizing the area under the Relative Operating Characteristic (ROC) curves. The computed ROC curve (AUC) values (0.720 for ANN and 0.651 for AHP) convey the advantage of the ANN method compared to the AHP method. The overlay of the landslide inventory data locations of historical landslides and susceptibility maps shows the concordance of the results, which is in favor of the established model reliability. Full article

Structural damage in bridges is typically a local phenomenon. When a vehicle passes over the damage location, it induces a local response, which is highly sensitive to the damage. The interaction between the bridge and moving vehicle is a non-stationary time-varying process. The local damage can be accurately identified by analyzing the time-varying characteristics of the bridge response subjected to a moving vehicle. Synchrosqueezing transform, a reassignment method used to sharpen time–frequency representations, offers an effective tool to decompose the non-stationary signal into distinct components. This paper proposes a novel method based on multi-synchrosqueenzing transform to extract the time-varying characteristics of the vehicle–bridge interaction systems for bridge structural health monitoring. A vehicle–bridge interaction model is built to simulate the bridge under moving vehicles. Different damage scenarios of concrete bridges have been simulated. The effect of bridge damage parameters, the vehicle speed, the road surface roughness on the time-varying characteristics of the vehicle–bridge interaction system is studied. Numerical and experimental results demonstrate that the proposed method efficiently and accurately extracts the time-varying features of the vehicle–bridge interaction system, which could serve as potential indicators of structural damage in bridges. Full article

This research aims to characterize sediment dynamics in the Rupinaro catchment, a uniquely terraced and human-shaped basin in Italy’s Liguria region, employing geomorphometric methods to unravel sediment connectivity in a landscape vulnerable to shallow landslides. Within a scenario-based approach, we utilized high-resolution LiDAR-derived digital terrain models (DTMs) to calculate the Connectivity Index, comparing sediment dynamics between the original terraced landscape and a virtual natural scenario. To reconstruct a pristine slope morphology, we applied a topographic roughness-based skeletonization algorithm that simplifies terraces into linear features to simulate natural hillslope conditions and remove anthropogenic structures. The analysis was carried out considering diverse targets (e.g., hydrographic networks, road networks) and the effect of land use. The results reveal significant differences in sediment connectivity between the anthropogenic and natural morphologies, with implications for erosion and landslide susceptibility. The findings reveal that sediment connectivity is moderately higher in the scenario without terraces, indicating that terraces function as effective barriers to sediment transfer. This highlights their potential role in mitigating landslide susceptibility on steep slopes. Additionally, the results show that roads exert a stronger influence on the Connectivity Index, significantly altering flow paths. These modifications appear to contribute to increased landslide susceptibility in adjacent areas, as reflected by the higher observed landslide density within the study region. Full article