Wet Pavement Research Articles

Estimating vehicle braking distance over wet and rutted pavement surface through back-propagation neural network

Due to repeated load cycles and climate impacts, road pavement deteriorates. One of the primary causes of pavement degradation is the formation of rutting under the wheel path on the road surface. Rutting's impact on vehicle performance, especially on rainy days (since the rain would fill the rutting depression), leads to longer braking distances compared to dry conditions. This study developed a MATLAB-based model for calculating vehicle braking distance on wet asphalt pavement affected by rutting, using dynamic skid resistances generated through Back-Propagation Neural Network (BPNN) analysis. This study addresses the worst-case scenario in which rutting is filled with water, then calculates the required vehicle braking distance under various Water Film Thickness (WFT) conditions. The developed model can perform these evaluations for different operational conditions across various input ranges, such as precipitation intensity, number of lanes, lane width, cross slope, average texture depth, rutting depths, and accumulated WFT. As an outcome, the vehicle braking distance can be estimated as a function of Rutting Depth (RD) within a known vehicle speed interval. After validating the proposed model against existing approaches from the literature, several sensitivity analyses are conducted to assess the impact of influencing parameters on the results. Moreover, the study examines the relationship between AASHTO braking distance requirements and the RD threshold levels adopted by several highway agencies. Furthermore, this model is also applicable to real-world case studies, enabling the calculation of vehicle braking distances with varying RDs in the presence of various WFTs on the pavement surface.

Numerical study of the tire hydroplaning behavior of aircraft on grooved concrete pavement.

Safe operation is crucial for civil aviation, and reducing the risk of aircraft tire hydroplaning is essential for civil aviation safety. Here, a new 3D aircraft tire-grooved (smooth) wet pavement model based on the coupled Eulerian-Lagrangian (CEL) algorithm for the A320 aircraft was developed, and the effect of the ground contact area of an aircraft tire on the hydrodynamic pressure and support force of the tire under smooth and grooved wet pavement conditions was investigated. The results indicate that at the same taxiing speed, the ground contact area of the aircraft tire under the grooved wet-pavement condition is reduced by 19.8% compared to the smoothed wet-pavement condition, which is reduced by 6.2%. Similar patterns are observed for the hydrodynamic pressure and the critical hydrodynamic speed during landing and taking-off procedures, with upper and lower limited values obtained through the simulation results. Additionally, the predicted correction factor of the hydroplaning speed at different water film thicknesses is compared with those values obtained via the NASA formula. A comparison shows that the NASA formula underestimates the critical hydroplaning speed during the landing procedure. The corresponding correction factor will be less than 1.0 when the water film thickness reaches a critical value of 7.66 mm.

Severity Predictions for Intercity Bus Crashes on Highway Using a Random Parameter Ordered Probit Model

As intercity buses are a mode that moves large-scale occupancy between regions, it accounts for the mode share-means for mid- to long-distance movement in South Korea. However, the study of intercity bus safety needs to be more extensive, and safety policies are carried out based on traditional probability models without considering the data characteristics of bus accidents. Therefore, in this study, the Random Parameter Ordered Logit model was applied to derive fixed parameter factors that have the same effect on the severity of intercity bus accidents and Random Parameters that consider the heterogeneity of unique attributes by accident. It also analyzed the marginal effect of intercity bus accident severity. As a result of this study, the influencing factors that reflect heterogeneity with random parameters were driver’s condition: drowsiness, vehicle size: medium, crash type: vehicle–pedestrian accident, road condition: wet pavement, and log form of AADT. The random parameter ordered logit model was traditionally found to be more suitable than the ordinal logit model, which only reflects fixed factors and more reliable predictions considering the heterogeneity of accident characteristics for each observation.

Temperature Prediction for Expressway Pavement Icing in Winter Based on XGBoost–LSTNet Variable Weight Combination Model

Ice cover on pavement may reduce the road adhesion coefficient and increase the crash risks, which might result in more traffic crashes. The primary factor utilized to assess whether the wet pavement is icy or not is the pavement temperature. Therefore, forecasting pavement temperature is an effective method to judge road conditions and improve traffic safety. This paper proposes a combination model based on the extreme gradient boosting (XGBoost) model and long- and short-term time-series network (LSTNet) model to predict pavement temperature. Pavement temperature and meteorological data were collected for the cities along the Shandong part of the Beijing-Taipei Expressway (G3). In this study, nine meteorological variables were used. Subsequently, after correlation analysis, five variables, including air temperature, dew point temperature, relative humidity, evaporation, and potential evaporation, were selected for prediction. The method proposed in this study comprises the following steps. First, the XGBoost and the LSTNet models are respectively formulated based on the time-varying characteristics of pavement temperatures. Then, using the preset weight of the variable, the XGBoost model is used for preliminary prediction to add features. Finally, the experimental analysis is performed on the Qihe data set after the two models have been integrated using the inverse variance method. As revealed by the experimental results, the mean absolute error (MAE) and root-mean-square error (RMSE) of the proposed XGBoost-LSTNet model are 0.8235 and 1.2412, respectively. Compared with the long short-term memory (LSTM) model, random forest (RF) model, XGBoost model, and LSTNet model, the XGBoost-LSTNet model proposed in this paper has higher accuracy. The study’s findings can successfully increase wintertime expressway traffic safety and serve as a guide for managing maintenance and preventing icing-related accidents.

An integrated analytical model for friction characteristics of aircraft tire on wet runway pavement

The challenging issues of tire-water-pavement interaction are to determine the deformed tire profile and the effective tire-pavement contact length due to water intrusion. This study aims to develop an integrated analytical model to analyze friction characteristics of an aircraft tire on wet runway pavement. The tire-water-pavement interaction model consists of the Brush model for tire-pavement contact and the flexible ring model for tire deformation with consideration of hydrodynamic force. The feasible contact footprint and the deformed tire profile are solved using an interactive process between two models. The model shows the ability to predict friction coefficients at various tire loading, velocity, slip ratio, and water conditions. The thicker water film depth causes the reduction of tire-pavement contact length and the increases of hydrodynamic force, resulting in poor skid resistance of pavement. The calculated friction coefficients at different velocities are further used to predict hydroplaning speed and calculate braking distance of aircraft tire. The results demonstrate the significant effect of water depth on the risk of aircraft hydroplaning and overrun on runway pavement.

Pothole and Wet Surface Detection Using Pretrained Models and ML Techniques

Abstract: Roads contribute significantly to the economy and serve as a transportation platform. Road potholes are a key source of worry in transportation infrastructure. The purpose of this research is to develop an Artificial Intelligence (AI) model for identifying potholes on asphalt pavement surfaces. Image processing techniques from pretrained models such as efficientnet, resnet50, mobilenet and ML models such as random forest, decision tree, SVC, SVM. Several studies have advocated employing computer vision techniques, including as image processing and object identification algorithms, to automate pothole detection. It is important to digitize the pothole identification process with acceptable accuracy and speed, as well as to deploy the procedure conveniently and affordably. Initially, a smartphone placed on the automobile windscreen captures many photographs of potholes. Later, by downloading pothole photographs from the internet, we expanded the amount and variety of our collection (2400 images with over 900 potholes). Second, to locate potholes in road photos, several object detection methods are used. To compare pothole detection performance, real-time Deep Learning algorithms in various setups are employed. Similarly Wet pavement decreases surface friction dramatically, increasing the likelihood of an accident. As a result, timely understanding of road surface condition is essential for safe driving. This research proposes a unique machine learning model pipeline for detecting pavement moisture based on live photos of highway scenes acquired via accessible to the public traffic cameras. We refined existing state-of-the-art feature extraction baseline models to capture background instance targets, such as pavement, sky, and vegetation, which are frequent in highway scenes, to simplify the learning job.

Mechanism Analysis of Anti-skid Performance Attenuation of Wet Pavement Based on Micro-interface

Mechanism Analysis of Anti-skid Performance Attenuation of Wet Pavement Based on Micro-interface

Research of the Luminance of Asphalt Pavements in Trafficked Areas

A key factor for safe and comfortable driving on roads are properly reflective and well visible pavement surfaces at night. The brightness of the road pavement surface depends on the amount of light falling on it and the reflection properties of the road pavement surface at any point. The luminance of the pavement depends on its physical condition, age and type of pavement, direction of illumination, and observation conditions. Different pavements can have different reflection characteristics that depend on the surface texture, materials, and binder (type and quantity). Experimental research was carried out on the carriageways and bicycle paths of Vilnius city streets, which differ in color and age. The analysis of the research results showed differences between the surface reflectance characteristics of these pavements depending on the color of the pavement, surface conditions, and age. The reflection properties of red asphalt pavements are better than black ones when the pavement surface is wet or moist. The reduced luminance coefficients of the carriageway (asphalt pavement installed in 2021) are about 12% lower than those of the carriageway pavement installed 10 years ago and about 60% lower for wet and moist pavements. The results obtained from the research are significant for street designers when choosing the type of pavement and designing street lighting.

Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions

With the growing rate of automated vehicles (AVs) at the lower level of automation, the experimental tests are also in progress with vehicles at higher levels. In the absence of extended digital infrastructures and deployment of level 5 full automated vehicles, the physical infrastructure is required to maintain a fundamental role to enable their introduction in public roads. This paper focuses on lane support system (LSS) whose operational design domain (ODD) is strongly connected to the road characteristics and conditions. An experimental test was carried out with a state of the art, and LSS and advanced technologies were used for road monitoring on different roads under various environmental conditions including dry, wet pavements and rain. We applied the generalized estimation equation for logistic regression to account within-cluster homogeneity which is induced by repeated measures on the same road sections. Statistical models allow the identification of variables that are significant for the LSS fault probability among various effects of road features including marking, pavement distress, weather conditions, horizontal curvature, and cross section. Results pointed out the relevance of the wet retro-reflection of marking (RLw) and the horizontal curvature in the definition of ODD for LSS. Threshold values have been proposed for the tested LSS. Wet pavement doesn't affect the LSS performance when compared to the dry condition. Rain was shown to be critical even with very good road characteristics. • Open on-road LSS test under various weather conditions such as dry, wet and rain. • Generalized estimating equation to highlight correlation among observations. • Average 3.9% fault probability of LSS with high variability in two-lane rural roads. • Comparatively rain increases the fault probability by a factor of 2.75 than dry weather condition. • Marking RLw and curvature 1/ R are the most relevant road factors.

Analysis of skid resistance and braking distance of aircraft tire landing on grooved runway pavement

ABSTRACT Aircraft landing operations on the wet grooved runway are one of the critical safety concerns for airport agencies. This study aims to predict the friction coefficient between aircraft tires and wet grooved pavement and evaluate the braking distance on the runway. An improved three-dimensional tire-pavement-water interaction model considering squared pavement grooves was developed to evaluate contact force and frictional force at different speeds. The aircraft tire model was validated by the contact footprint and load-deflection curve. The calculated friction coefficients were found to match well with field measurements. The results show that tire wear and water film thickness make significant differences in the friction force between aircraft tires and wet grooved runways. The loss of tread depth degrades drainage capacity between the tire tread and grooved pavement surface. With the increase in speed and water film thickness, the worn tire tends to lose more friction than the new tire. Through the developed approach, the evaluation of hydroplaning speed and braking distance of the landing aircraft during the ground rolling process can be performed. The analysis findings can help airport agencies, aircraft manufacturers, and pilots mitigate the overrun risk of aircraft landing from different aspects.

Anti-Skid Characteristics of Asphalt Pavement Based on Partial Tire Aquaplane Conditions.

This study presented a finite element model of radial tire–asphalt pavement interaction using ABAQUS 6.14 software to investigate the skid resistance properties of asphalt pavement under partial tire aquaplane conditions. Firstly, the pavement profile datum acquired by laser scanning were imported to Finite Element Analysis (FEA) software to conduct the pavement modeling. Secondly, a steady state rolling analysis of a tire on three types of asphalt pavements under drying conditions was carried out. Variation laws of the friction coefficient of the radial tire on different pavements with different pavement textures, tire pressures, and loads on the tire were examined. Subsequently, calculation results of the steady state rolling analysis were transmitted to dynamic explicit analysis, and an aquaplane model of a radial tire on asphalt pavements was built by inputting the flow Euler grids. The tire–pavement adhesive characteristics under partial aquaplane conditions are discussed regarding the aquaplane model. Influences of the thickness of water film, the texture of asphalt pavement, and the rolling speed of the tire on the vertical pavement-tire contact force are analyzed. It is found that the vertical contact force between open graded friction course (OGFC) pavement and tire is the highest, followed by stone mastic asphalt (SMA) pavement and dense graded asphalt concrete (AC) pavement surface. The vertical contact force between tire and pavement will be greatly reduced, even with increasing speed or water film thickness. As tire speed increases from 70 km/h to 130 km/h, the tire–pavement contact force is reduced by about 25%. Moreover, when the thickness of water film increases from 0 (dry condition) to 4 mm and then to 12 mm, the vertical contact force reduced 50% and 15%, respectively, compared with under the dry contact condition. This study provided a key theoretical reference for safe driving on wet pavements.

Investigation of Inlaid Pavement Marker Performance and Safety Effectiveness

The objectives of this study were to assess inlaid pavement marker (IPM) performance and estimate their safety effectiveness in St. Louis, Missouri. IPM performance was evaluated through a count of marker presence and a feedback survey from participants who viewed dry and wet night videos of IPM sections in the St Louis area. The safety effectiveness of IPMs was evaluated using a rigorous state-of-the-art empirical Bayes (EB) before–after crash analysis. The marker presence assessment, unfortunately, did not provide any conclusive trends in IPM performance. Newer IPM sections had higher percentages of missing markers than older sections, and there were too many unknown variables to determine the source of the unexpected differences. The unanimous conclusion of the nighttime video visibility survey was that drivers and passengers traveling on a wet night feel that IPMs are very important to the visibility of the roadway’s lane lines. For the EB analysis, installation and reference site data were used to examine the effects for specific crash types, including total, fatal and injury, wet pavement, nighttime, nighttime wet pavement, lane departure, wet pavement lane departure, and nighttime lane departure. Based on the aggregate results, IPMs, when installed with pavement resurfacing, significantly reduce all crash types examined.

Computational analysis of skid resistance of aircraft tire on wet runway pavement with different groove depths

This study aimed to develop an integrated approach to evaluate the groove depth effect on water depths and skid resistance on runway pavement. Finite element modelling of tire-water-pavement interaction was developed to calculate pavement friction coefficient of aircraft tire. Spatial distribution of water film depth was obtained based on analytical modelling and validated with field measurements. The results show that the thicker water film depth and the smaller friction coefficient are expected with the smaller groove depth. The effect of groove depth becomes more significant in the region close to the centerline of runway. The non-uniform distribution of water film depth would cause different skid resistance encountered by aircraft tires and increase safety risk of landing when aircraft wander happens. The developed simulation approach can be used to determine the threshold of groove depth for maintenance treatment considering rainfall condition, aircraft operation, and geometry of runway.

The Private Investigator

The Private Investigator

Data-Driven Approach to Weather-Responsive Speed Management of Road Tunnel Access Zones

In South Korea, one of the leading causes of motor vehicle crashes occurring at road tunnel entrances is speeding or failure to maintain a safe distance, especially during adverse weather conditions. The South Korean government released a strategic plan to address this issue through weather-responsive speed limit adjustments at tunnel entrances. This study aimed to assist the South Korean government’s plan with a data-driven safety approach. In particular, crash data from freeway tunnel access zones were augmented with real-time traffic speed and weather data, and standard binomial logit regression (BLR) with random forest (RF)-based variable selection was employed. The key variables contributing to crashes at freeway tunnel access zones during adverse weather conditions were identified as: speeding or failure to maintain a safe distance; no existence of a roadside fence; freeway sections with a distance of 100 to 250 m from the tunnel entrance; and interactions of traffic speed with wet pavement, average hourly rainfall of 6 h, visibility, and humidity preceding crash. The current standards for weather-responsive speed limit adjustment and appropriate deployment of variable message signs can be informed by the findings of this study.

Road Adhesion Coefficient Identification Method Based on Vehicle Dynamics Model and Multi-Algorithm Fusion

<div class="section abstract"><div class="htmlview paragraph">As an important input parameter of intelligent vehicle active safety technology, road adhesion coefficient is of great significance in autonomous collision avoidance, emergency braking and collision avoidance, and variable adhesion road motion control. Traditional recognition methods based on vehicle dynamics require large data volume and low solution accuracy. This paper proposes an adhesion coefficient recognition method based on Elman neural network and Kalman filter. By establishing a seven-degree-of-freedom vehicle dynamics model, dynamic parameters such as yaw angular velocity, longitudinal velocity, lateral velocity, and angular velocity of each wheel, which are easy to measure and strongly related to the road adhesion coefficient, are analyzed as the input of the neural network model. The square root cubature Kalman filter algorithm is used to remove the noise of the input of the neural network model, and Q-learning is used to strengthen learning, and the weight coefficient and bias coefficient of the model are continuously rewarded and punished, so that the predicted value does not exceed the normal range of values. The algorithm was pre-trained through CarSim/Simulink co-simulation, 9 sets of simulation conditions were established, and 4 sets of verification schemes were designed for identification and inspection. The average error of the simulation process is 4.93%, and the accuracy is 91.23%. Compared with the traditional Elman neural network, the average recognition error of this method is reduced by 2.23%, and the accuracy rate is increased by 9.83%. Real vehicle experiments were carried out on wet asphalt pavement and dry asphalt pavement, verifying the feasibility of the method. This paper proposes a road adhesion coefficient recognition method, which can improve the applicability of intelligent vehicle active safety systems to complex scenarios.</div></div>

Analysis and mitigation of hydroplaning risk considering spatial-temporal water condition on the pavement surface

ABSTRACT Wet pavement surface on rainy days poses a challenge for traffic safety as it greatly impacts the safe driving speed and increases the hydroplaning risk. This study proposed a new approach to analyse and mitigate hydroplaning risk on roadways based on spatial-temporal water conditions and hydroplaning speeds. A theoretical analysis was conducted to predict the spatial-temporal water film depth under dynamic rainfall profile, considering the effects of roadway slope and pavement surface texture. The tire-water-pavement interaction model was then used to predict hydroplaning speeds of vehicles driving on different traffic lanes. According to the hydroplaning and traffic flow speeds, the spatial and temporal pattern of roadway hydroplaning risk was analysed. Considering traffic flow speeds, rainfall intensity, roadway geometry, and surface condition, the probability of hydroplaning occurrence at different times of rainfall and separate traffic lanes can be estimated using the proposed approach. Finally, the variable speed limits (VSL) approach was proposed to mitigate hydroplaning risk, which can be determined based on the hydroplaning threshold probability and the design speed limit of the roadway section.

Permitted speed decision of single-unit trucks with emergency braking maneuver on horizontal curves under rainy weather.

Under adverse weather conditions, visibility and the available pavement friction are reduced. The improper selection of speed on curved road sections leads to an unreasonable distribution of longitudinal and lateral friction, which is likely to cause rear-end collisions and lateral instability accidents. This study considers the combined braking and turning maneuvers to obtain the permitted vehicle speed under rainy conditions. First, a braking distance computation model was established by simplifying the relationship curve between brake pedal force, vehicle braking deceleration, and braking time. Different from the visibility commonly used in the meteorological field, this paper defines "driver’s sight distance based on real road scenarios" as a threshold to measure the longitudinal safety of the vehicle. Furthermore, the lateral friction and rollover margin is defined to characterize the vehicle’s lateral stability. The corresponding relationship between rainfall intensity-water film thickness-road friction is established to better predict the safe speed based on the information issued by the weather station. It should be noted that since the road friction factor of the wet pavement not only determined the safe vehicle speed but also be determined by the vehicle speed, so we adopt Ferrari’s method to solve the quartic equation about permitted vehicle speed. Finally, the braking and turning maneuvers are considered comprehensively based on the principle of friction ellipse. The results of the TruckSim simulation show that for a single-unit truck, running at the computed permitted speed, both lateral and longitudinal stability meet the requirements. The proposed permitted vehicle speed model on horizontal curves can provide driving guidance for drivers on curves under rainy weather or as a decision-making basis for road managers.

Analytical Optimization Model to Locate and Design Runway-Taxiway Junctions

Aims: Air traffic and airport operations are expected to experience significant growth worldwide in the upcoming years. One of the possible approaches to adapt to this demand-led growth in the sector, while guaranteeing optimal levels of airport services and operations safety, is to maximize the capacities of busy airport infrastructures (in particular runways) by evacuating them in the shortest time possible to be ready for hosting next operations. Background: The main research areas in this field range from statistical risk analyses based on the registered accidents databases to simulation analyses modelling the behaviour of the aircraft during landing operations. Objective: The main objective of this study is to determine precisely the optimal distances of runway-taxiway junctions from the runway’s threshold, according to numerous impact parameters such as airport climate pattern, operating aircraft categories, infrastructure type, and capacity, route connections, operating costs, and associated risks. Methods: The authors developed a mathematical model with the goal of simulating the dynamic behaviour of the aircraft during landing and possible consequences introduced by the presence of contaminants over the pavement surface, by calculating their braking distances, and finally to optimize the use of existing infrastructures, specially runway-taxiway junctions, of a commercial airport. In this regard, the interactions between landing gear, pavement, and fluid were carefully analysed. The dynamic pavement skid resistance values in wet pavement conditions were evaluated for optimizing the required landing distances, which are setting the base for optimizing the location of the taxiway junctions. An Italian international airport was selected as the case study to be simulated by the developed model in order to optimize its runway capacity and maximize its rate of operations. Results: In the process, two different scenarios are simulated with the developed model; a modified design of an existing runway and an alternative design solution for constructing a new runway. The developed model offers improvements for both scenarios with respect to the current runway configurations in terms of reduction in mean rolling distances. The simulation of the selected case study shows that the taxiway modification scenario achieves a reduction of 23% in the mean rolling distance for wet and 25% for dry pavement conditions. While, for designing a new runway, greater reductions of 27% for wet and 39% for dry pavement conditions are obtained due to the higher flexibilities and degrees of freedom in designing a runway from the beginning. Conclusion: The developed model can precisely propose new configurations of the runway-taxiway junctions with lower mean rolling distances, which lower the operation costs and fuel consumption, decrease the runway evacuation times and increase the capacity of the airfield. The main advantage of this model is its ability to cover a wider spectrum of boundary conditions with respect to the existing models and its applicability for designing new runways, plus to optimize the configuration of existing infrastructures in order to satisfy the evolution of the industry.

Estimating the Potential of Skid Incident at Airport Runway – A Case Study

Skid resistance or friction on airport pavement plays an essential role in runway safety to ensure safe stopping distance. It also provides safety margins during take off and landing of aircraft especially in directional control by reducing the occurrence of overrun and veer-off incidents. The friction between pavement and tire is related to multiple factors such as pavement surface characteristics (microtexture and macrotexture), material intruded between tire and pavement surface (water, snow, and contaminants), tire conditions (types, wear, and inflation pressure), and vehicle operating conditions (vehicle speed and whether the wheel is rolling or locked). Skid incidents due to wet pavement surface at airport runway is a major concern especially in regions of wet-tropical climate countries such as Malaysia. Thus, this study was undertaken to measure the potential of skid incident at airport runway by collecting data on pavement surface characteristics. Field data collection on pavement surface microtexture was conducted using the British Pendulum Tester at a local airport runway to obtain data for the estimation of the skid number (SN). Due to constraints, pavement surface macrotexture test was unable to be conducted at site. However, the pavement surface macrotexture was able to be estimated based on previous research. The potential of the skid incident was estimated by calculating the SN using the aircraft’s touch down speed at the airport runway, varies from 50 to 70 knots (57.5 – 80.6 mph or 92.6 – 129.6 km/h). The results of the estimation indicated that the possibility of the skid incident occurrence was high. This study provides useful insights for airport authority to investigate the potential of overrun and veer-off incidents of aircraft at the airport runway.