Search Results (8)

Rainfall can easily cause local sliding and collapse of carbonaceous mudstone deep road cut slopes. In order to study the strength characteristics of carbonaceous mudstone under different water environments, large-scale horizontal push shear tests were conducted on carbonaceous mudstone rock masses in their natural state and after immersion in saturated water. The push shear force–displacement relationship curve and fracture surface shape characteristics of carbonaceous mudstone samples were analyzed, and the shear strength index of carbonaceous mudstone was obtained, and numerical simulations on the stability and support effect of carbonaceous mudstone slopes were conducted. The research results indicate that carbonaceous mudstone can exhibit good structural properties and typical strain softening characteristics under natural conditions. The fracture surface, shear strength, and shear deformation process of carbonaceous mudstone samples will undergo significant changes after being soaked in saturated water. The average cohesion decreases by 33% compared to the natural state, and the internal friction angle decreases by 15%. The numerical simulation results also fully verify the attenuation of mechanical properties of carbonaceous mudstone after immersion, as well as the effectiveness of prestressed anchor cables and frame beams in supporting carbonaceous mudstone slopes. The research results provide an effective method for understanding the shear performance of carbonaceous mudstone and practical guidance for evaluating the stability and reinforcement design of carbonaceous mudstone slopes. Full article

Truck skidding crashes on horizontal curves pose a significant road safety concern, with improper braking being the primary cause. A data- and model-integrated driven method is proposed to investigate the mechanism and recommend the maximum safe braking deceleration rates without skidding (abbreviated as MSBDRs) for trucks on horizontal curves. Firstly, a comprehensive road–vehicle interaction model was developed, considering dynamic changes in brake force distribution, vertical tire load, and longitudinal and side friction during braking. Secondly, leveraging the “HighD” data set and employing cluster analysis principles, parameter data were extracted using Python and Matlab. Finally, through parameterizing model inputs, the transient dynamic response of trucks was examined, the potential of truck skidding was predicted, and the MSBDRs were recommended. The results indicate the following. (1) There is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. (2) The MSBDR is 4.5 m/s² on a limit-minimum-radius horizontal curve; however, when combined with steep slopes, an overspeed exceeding 20%, and extremely wet road conditions, respectively, the MSBDRs decrease to 4 m/s², 3 m/s², and 2 m/s². These results provide a theoretical foundation for braking strategies in autonomous vehicles. Full article

Due to poor road design, challenging terrain, and difficult geological conditions, traffic accidents on mountainous two-lane roads are more frequent and severe. This study aims to address the lack of understanding of key factors affecting accident severity with the goal of improving mountainous traffic safety, thereby contributing to sustainable transportation systems. The focus of this study is to compare the interpretability of model performances with three statistical models (Ordered Logit, Partial Proportional Odds Model, and Multinomial Logit) and six machine learning models (Decision Tree, Random Forest, Gradient Boosting, Extra Trees, AdaBoost, and XGBoost) on two-lane mountain roads in Yunnan Province, China. Additionally, we assessed the ability of these models to uncover underlying causal relationships, particularly how accident causes affect severity. Using the SHapley Additive exPlanations (SHAP) method, we interpreted the influence of risk factors in the machine learning models. Our findings indicate that machine learning models, especially XGBoost, outperform statistical models in predicting accident severity. The results highlight that accident patterns are the most significant determinants of severity, followed by road-related factors and the type of colliding vehicles. Environmental factors like weather, however, have minimal impact. Notably, vehicle falling, head-on collisions, and longitudinal slope sections are linked to more severe accidents, while minor accidents are more frequent on horizontal curve sections and areas that combine curves and slopes. These insights can help traffic management agencies develop targeted measures to reduce accident rates and enhance road safety, which is critical for promoting sustainable transportation in mountainous regions. Full article

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety. Full article

This paper investigated the surface length of roads from both horizontal and vertical perspectives using the theory of fractal dimension of surfaces and curves. Three progressive experiments were conducted. The first demonstrated the magnitude of the differences between the planar road length and the DTM-derived surface road length and assessed its correlation with the DTM-calculated road slope. The second investigated the road distance complexity through the fractal dimension in both planar and vertical dimensions. The third related the vertical with the horizontal fractal dimension of roads across a range of distinct physiographic regions. The study contributed theoretically by linking the planimetric complexity to vertical complexity, with clear applications for advanced transportation studies and network analyses. The core methodology used geographic information systems (GIS) to integrate a high resolution (1 × 1 m) digital terrain model (DTM) with a road network layer. A novel concept, the vertical fractal dimension of roads was introduced. Both the vertical and horizontal fractal dimensions of the roads were calculated using the box-counting methodology. We conducted an investigation into the relationship between the two fractal dimensions using fourteen study areas within four distinct physiographic regions across Slovenia. We found that the average slope of a three-dimensional (3D) road was directly related to the length difference between 3D and two-dimensional (2D) roads. The calculated values for the vertical fractal dimension in the study areas were only slightly above 1, while the maximum horizontal fractal dimension of 1.1837 reflected the more sinuous properties of the road in plan. Variations in the vertical and horizontal fractal dimensions of the roads varied between the different physiographic regions. Full article

China has a large vehicle base, uneven road conditions, and the highest rate of traffic accidents in the world. Particularly on the long downhill sections of expressway tunnels in mountainous areas with harsh geographical conditions, traffic accidents are densely distributed, and once a traffic accident occurs, the consequences are serious, which poses a large threat to people’s lives and property. This paper mined and analyzed the traffic accident data collected by the project on the Baoding section of Zhangshi Expressway. SPSS software was used to analyze the traffic accident data characteristics of the long downhill tunnel of the mountain expressways. The time, space, accident form, vehicle type, and road alignment distribution characteristics of the traffic accident in the long downhill tunnel section of mountain expressways were obtained. The decision tree algorithm was used to construct the cause analysis model of traffic accidents in the long downhill tunnel of mountain expressways, and the five primary influencing factors were obtained: horizontal curve radius, week, slope length, time, and cart ratio. The improved cumulative frequency curve method was used to study the accident-prone points of mountain expressways, and the accident-prone points and potential accident-prone points were obtained. Full article

Both the calculation of ground slopes at points on the map and the elevation estimation for these points bear significant importance and also have applications in various domains, such as civil engineering, road and railway design. The paper presents two methods that use level curves: one that is fast and approximate and another which is slower, but more precise. The running speed of the two proposed methods and their results are compared by performing 100 million experiments. The paper also presents how these methods can be applied to optimize embankments. An accurate method to calculate the horizontal plane of the excavation/filling when building a new house is also presented. Full article

This study aims to evaluate the permanent deformation of high-modulus asphalt pavement in special road using viscoelastic theory. Based on the creep test, the Prony series representation of Burgers model parameters for different asphalt mixtures were obtained and used in the deformation simulation of a high-modulus asphalt pavement situated in a horizontally curved ramp. The orthogonal design method was used to show the effect of different factors on the deformation. Results reveal that rutting in curved ramp was greater than in straightaway. Further, evident upheaval was found on the downhill pavement surface and outer pavement parts of the curve due to longitudinal friction force and sideway force. In addition, the upper and middle asphalt courses in such road seemed more crucial to pavement anti-rutting performance, since inclusion of shear force changed pavement deformation characteristic and the potential rutting area tended to move up. Finally, a preliminary equation to predict rutting in sloped and curved road with widely accepted pavement structure in China was proposed. Full article