Search Results (141)

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

With the increasing severity of traffic congestion, the impact of random traffic patterns has emerged as an indispensable factor in the fatigue design and assessment of highway bridges. In this study, an analytical approach has been proposed for modeling the effects of random traffic patterns on fatigue damage. A fatigue damage ratio, referred to as RPEF, is introduced to establish the correlation between damage and traffic characteristics. Two quantitative parameters representing two characteristics of traffic loads, namely the average loading occurrence number (scale parameter) and the vehicle headway COVs (shape parameter), have been found to be excellent indices for clustering the different dimensional randomness of RPEFs. Based on a comprehensive case study, the realization of using equivalent RPEFs to evaluate bridge fatigue damage under mixed traffic conditions was explored. The results indicate that the actual fatigue damage of bridges can be evaluated through the superposition of different traffic pattern effects, provided that the pattern classification number fits the fluctuations in traffic flow. It is necessary to ensure the rationality of traffic pattern classification for structures with spans greater than 50 m, as an overly simplistic traffic pattern classification may lead to an underestimation of fatigue damage. Full article

Located on the northern side of the China–Pakistan Highway in the Pamir Plateau, Laqi Gully represents a typical rainfall–meltwater coupled debris flow gully. During 2020–2024, seven debris flow events occurred in this area, four of which disrupted traffic and posed significant threats to the China–Pakistan Economic Corridor (CPEC). The hazard assessment of debris flows constitutes a crucial component in disaster prevention and mitigation. However, current research presents two critical limitations: traditional models primarily focus on single precipitation-driven debris flows, while low-resolution digital elevation models (DEMs) inadequately characterize the topographic features of alpine narrow valleys. Addressing these issues, this study employed GF-7 satellite stereo image pairs to construct a 1 m resolution DEM and systematically simulated debris flow propagation processes under 10–100-year recurrence intervals using a coupled rainfall–meltwater model. The results show the following: (1) The mudslide develops rapidly in the gully section, and the flow velocity decays when it reaches the highway. (2) At highway cross-sections, maximum velocities corresponding to 10-, 20-, 50-, and 100-year recurrence intervals measure 2.57 m/s, 2.75 m/s, 3.02 m/s, and 3.36 m/s, respectively, with maximum flow depths of 1.56 m, 1.78 m, 2.06 m, and 2.52 m. (3) Based on the hazard classification model of mudslide intensity and return period, the high-, medium-, and low-hazard sections along the highway were 58.65 m, 27.36 m, and 24.1 m, respectively. This research establishes a novel hazard assessment methodology for rainfall–meltwater coupled debris flows in narrow valleys, providing technical support for debris flow mitigation along the CPEC. The outcomes demonstrate significant practical value for advancing infrastructure sustainability under the United Nations Sustainable Development Goals (SDGs). Full article

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure. Full article

Interference signals can disrupt global navigation satellite system (GNSS) receivers and degrade or deny a position-velocity-time (PVT) solution. After detecting an interference signal, classifying its type can provide insight into its cause and help determine the necessary next steps to counteract it. In this paper, we present a method for interference detection and a resource-efficient model-aided on-the-edge machine learning (ML) model for interference signal classification running on low-cost commercial-off-the-shelf (COTS) hardware, enabling a highly cost-effective spectral monitoring solution. The choice of detection metrics is justified based on real-world spectral monitoring data from a German highway and the capability of the ML model to generalize across different environments is demonstrated in an outdoor field test. Overall, we present an operationally ready GNSS interference detection and classification system. Full article

Driving under the influence of alcohol (DUI) remains a leading cause of accidents globally, with accident risk rising exponentially with blood alcohol concentration (BAC). This study aims to distinguish between sober and intoxicated drivers using driving behavior analysis and driver monitoring system (DMS), technologies that align with emerging EU regulations. In a driving simulator, twenty-three participants (average age: 32) completed five drives (one practice and two each while sober and intoxicated) on separate days across city, rural, and highway settings. Each 30-minute drive was analyzed using eye-tracking and driving behavior data. We applied significance testing and classification models to assess the data. Our study goes beyond the state of the art by a) combining data from various sensors and b) not only examining the effects of alcohol on driving behavior but also using these data to classify driver impairment. Fusing gaze and driving behavior data improved classification accuracy, with models achieving over 70% accuracy in city and rural conditions and a Long Short-Term Memory (LSTM) network reaching up to 80% on rural roads. Although the detection rate is, of course, still far too low for a productive system, the results nevertheless provide valuable insights for improving DUI detection technologies and enhancing road safety. Full article

In traffic safety analysis, previous research has often focused on tabular data or textual crash narratives in isolation, neglecting the potential benefits of a hybrid multimodal approach. This study introduces the Multimodal Data Fusion (MDF) framework, which fuses tabular data with textual narratives by leveraging advanced Large Language Models (LLMs), such as GPT-2, GPT-3.5, and GPT-4.5, using zero-shot (ZS), few-shot (FS), and fine-tuning (FT) learning strategies. We employed few-shot learning with GPT-4.5 to generate new labels for traffic crash analysis, such as driver fault, driver actions, and crash factors, alongside the existing label for severity. Our methodology was tested on crash data from the Missouri State Highway Patrol, demonstrating significant improvements in model performance. GPT-2 (fine-tuned) was used as the baseline model, against which more advanced models were evaluated. GPT-4.5 few-shot learning achieved 98.9% accuracy for crash severity prediction and 98.1% accuracy for driver fault classification. In crash factor extraction, GPT-4.5 few-shot achieved the highest Jaccard score (82.9%), surpassing GPT-3.5 and fine-tuned GPT-2 models. Similarly, in driver actions extraction, GPT-4.5 few-shot attained a Jaccard score of 73.1%, while fine-tuned GPT-2 closely followed with 72.2%, demonstrating that task-specific fine-tuning can achieve performance close to state-of-the-art models when adapted to domain-specific data. These findings highlight the superior performance of GPT-4.5 few-shot learning, particularly in classification and information extraction tasks, while also underscoring the effectiveness of fine-tuning on domain-specific datasets to bridge performance gaps with more advanced models. The MDF framework’s success demonstrates its potential for broader applications beyond traffic crash analysis, particularly in domains where labeled data are scarce and predictive modeling is essential. Full article

Energy management strategies (EMS) in the context of electric or hybrid vehicles can optimize the available energy by minimizing consumption. Most optimization-based EMS are not real-time-applicable for an accurate estimation of future consumption. The performance of these strategies also strongly depends on the driving patterns, which may be influenced by road and traffic conditions, among other factors such as driving style, weather, vehicle type, etc. The primary contribution of this work is to develop a novel two-layer driving pattern recognition (DPR) system for roadway type and traffic classification, thus enabling the identification of unknown patterns for the enhancement of the prediction of energy consumption of an electric vehicle (EV). The novelty of this work lies in the development of a strategy based on real-time data which is capable of classifying driving patterns and implementing an optimized EMS based on the results of the DPR. In the approach, first, labels are defined based on statistical features related to speed followed by the creation of representative driving patterns (RDPs). A neural network-based classifier is then employed for classification into six classes based on four features. A training accuracy of 97.7% is achieved with the classification of unknown speed profiles into the known RDPs. Testing with patterns from two different test routes shows an accuracy of 97.45% and 96.98% during morning and 96.65% and 94.12% during evening hours, respectively. Apart from the route and time of data collection, accuracy is also a function of sampling time horizon and the threshold values chosen for the features. A sensitivity analysis was also performed to evaluate the relative importance of each feature. An EMS based on sequential quadratic programming (SQP) was combined with DPR for the computation of optimal energy consumption. Simulation results show that maximum and minimum energy savings of 61% and 18% were obtained under suburban low traffic and highway high traffic conditions, respectively. An eco-driving or driver speed advisory system may further be developed based on information obtained from multiple routes and varying traffic scenarios. Full article

This paper presents the development and analysis of a bridge bearing database consistent with the 2020 Italian Guidelines (LG2020), currently enforced by the Italian law for risk classification and management of existing bridges. The database was developed by putting together the contribution of 24 research teams from 18 Italian universities in the framework of a research project foreseen by the agreement between the High Council of Public Works (CSLP, part of the Italian Ministry of Transportation) and the research consortium ReLUIS (Network of Italian Earthquake and Structural Engineering University Laboratories). This research project aimed to apply LG2020 to a set of about 600 bridges distributed across the Italian country, in order to find possible issues and propose modifications and integrations. The database includes almost 12,000 bearing defect forms related to a portfolio of 255 existing bridges located across the entire country. This paper reports a preliminary analysis of the dataset to provide an overview of the bearings installed in a significant bridge portfolio, referring to major highways and state roads. After a brief state of the art about the main bearing types installed on the bridges, along with inspection procedures, the paper describes the database structure, showing preliminary analyses related to bearing types and defects. The results show the prevalence of elastomeric pads, representing more than 55% of the inspected bearings. The remaining bearings are pot, low-friction with steel–Teflon surfaces and older-type steel devices. Lastly, the study provides information about typical defects for each type of bearing, while also underscoring some issues related to the current version of the LG2020 bearing inspection form. Full article

This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation. Full article

Established unmanned aerial vehicle (UAV) highway distress detection (HDD) faces the dual challenges of accuracy and efficiency, this paper conducted a comparative study on the application of the YOLO (You Only Look Once) series of algorithms in UAV-based HDD to provide a reference for the selection of models. YOLOv5-l and v9-c achieved the highest detection accuracy, with YOLOv5-l performing well in mean and classification detection precision and recall, while YOLOv9-c showed poor performance in these aspects. In terms of detection efficiency, YOLOv10-n, v7-t, and v11-n achieved the highest levels, while YOLOv5-n, v8-n, and v10-n had the smallest model sizes. Notably, YOLOv11-n was the best-performing model in terms of combined detection efficiency, model size, and computational complexity, making it a promising candidate for embedded real-time HDD. YOLOv5-s and v11-s were found to balance detection accuracy and model lightweightness, although their efficiency was only average. When comparing t/n and l/c versions, the changes in the backbone network of YOLOv9 had the greatest impact on detection accuracy, followed by the network depth_multiple and width_multiple of YOLOv5. The relative compression degrees of YOLOv5-n and YOLOv8-n were the highest, and v9-t achieved the greatest efficiency improvement in UAV HDD, followed by YOLOv10-n and v11-n. Full article

In recent years, an increasing number of accidents at ski resorts have raised significant safety concerns. To address these issues, it is essential to understand skiing traffic and the underlying dynamics. We collected 225 trajectories, which were analyzed after a correction process. To obtain a quantitative classification of typical trajectories we focus on three main quantities: turning points, minimum distance, and the fundamental diagram. Our objective was to analyze these trajectories in depth and identify key statistical properties. Our findings indicate that three factors—turning angle, curvature, and velocity change—can be used to accurately identify turning points and classify skiers’ movement styles. We found that aggressive skiers tend to exhibit larger and less stable turning angles, while conservative skiers demonstrate a more controlled style, characterized by smaller, more stable turns. This is consistent with observations made for the distribution of the minimum distance to other skiers. Furthermore, we have derived a fundamental diagram which is an important characteristic of any traffic system. It is found share more similarities with the fundamental diagram of ant trails than those of highway traffic. Full article

The urban climate in South China is marked by high complexity and substantial precipitation, posing significant challenges to road performance. This study focuses on the importance of precise climate zoning for urban roads in South China and the application of performance grade (PG) asphalt grading technology to enhance pavement durability. Meteorological data from multiple stations across the region were analyzed to identify key climatic indicators. Using spatial interpolation methods and fuzzy c-means clustering, urban roads were classified into five distinct climate zones. Zone I has the highest temperature; Zone II experiences the lowest temperature, necessitating attention to low-temperature pavement cracking; Zone III exhibits greater temperature variability, requiring consideration of both low-temperature cracking and water stability; Zone IV demonstrates relatively stable climatic conditions; and Zone V receives the highest precipitation, demanding a focus on water stability in pavement design. Trend analysis indicates increasing precipitation across all zones except Zone II and a general rise in minimum temperatures, suggesting a diminishing influence of low-temperature conditions. By integrating the Strategic Highway Research Program temperature conversion method and PG classification technology, this study provides asphalt grade recommendations tailored to each climate zone, addressing diverse environmental challenges and optimizing pavement performance. Full article

Road classification, knowing whether we are driving in the city, in rural areas, or on the highway, can improve the performance of modern driver assistance systems and contribute to understanding driving habits. This study focuses on solving this problem universally using only vehicle speed data. A data logging method has been developed to assign labels to the On-board Diagnostics data. Preprocessing methods have been introduced to solve different time steps and driving lengths. A state-of-the-art conventional method was implemented as a benchmark, achieving 89.9% accuracy on our dataset. Our proposed method is a neural network-based model with an accuracy of 93% and 1.8% Type I error. As the misclassifications are not symmetric in this problem, loss function weighting has been introduced. However, this technique reduced the accuracy, so cross-validation was used to use as much data as possible during the training. Combining the two approaches resulted in a model with an accuracy of 96.21% and unwanted Type I misclassifications below 1%. Full article