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Expressway crashes in interchange areas are a critical concern in China, posing significant economic and social challenges. Utilizing three years of crash data from the Beijing-Shanghai Expressway, this study investigates the transferability and heterogeneity of crash characteristics between interchange and noninterchange areas, as well as the temporal shifts in factors influencing injury severity levels. The research employs four series of random parameters logit models to estimate three potential crash injury severity outcomes of severe injury, minor injury, and no injury (based on the most severely injured individual in each crash) and to identify key determinants, encompassing driver, vehicle, roadway, environmental, temporal, traffic, and crash attributes. Likelihood ratio tests and out-of-sample predictions are utilized to assess the temporal stability and transferability of crash area characteristics. Additionally, the marginal effects of various determinants are calculated to understand their influence across different year periods and crash types. Key variables such as overspeed, single-vehicle, AADT (annual average daily traffic volume), Ls[sub.min], and other crash type indicators are identified as significant random parameters, demonstrating heterogeneity in means and variances. Notable distinctions are observed between interchange and noninterchange crashes, indicating nontransferability, with most significant indicators revealing temporal instabilities. Furthermore, factors such as multivehicle involvement, buses, and nighttime conditions are identified as risk indicators, notably increasing the likelihood of severe injuries. These insights are invaluable for expressway designers and decision-makers, aiding in understanding the contributing mechanisms of various elements. This study suggests that stricter enforcement measures are crucial to prevent random lane changes, particularly at interchange entrances and exits. Additionally, effective management strategies and enforcement countermeasures should be implemented to mitigate crash injury outcomes in both interchange and noninterchange areas., Author(s): Fang Wang [1]; Chenzhu Wang (corresponding author) [2]; Said M. Easa [3]; Siqi Lian [4]; Fei Chen [2]; Jianchuan Cheng [2] 1. Introduction In China, 211,074 motor vehicle crashes [...]

Rear-end crashes constitute the predominant type of crashes on highways and may lead to severe injuries and high property damage. Available statistical models primarily focus on injury severity and analyze potential factors that affect it. However, rear-end crashes may also be potentially correlated to vehicle, roadway, environmental, temporal, spatial, traffic, and crash characteristics. Additionally, unobserved heterogeneity regarding the effects may be present, which may be different in different crashes. In this context, multiple generalized estimating equation (GEE)-based models, developed using different working matrices and distributions, are proposed in this study to examine factors that affect injury severity. The proposed models account for both crash-related correlations and unobserved heterogeneity, thereby outperforming traditional models in terms of prediction accuracy. Among the explanatory variables considered in this study, the passenger car, minibus, curvature ratio, rainy weather, foggy weather, early morning, Thursday, autumn, winter, and average annual daily traffic volume were identified as contributing factors. However, significant differences were observed between the elasticity effects measured by different models, especially in terms of minibus and foggy weather. Thus, this study verifies that GEE-based models account for a greater amount of unobserved heterogeneity, yield better performance in terms of precision, and exhibit more consistent explanatory power compared to traditional models. Key words: rear-end crash, injury severity, generalized estimating equation, elasticity effect Les collisions par l'arriere constituent le type de collisions predominant sur les routes et peuvent entrainer des blessures graves et des dommages materiels importants. Les modeles statistiques disponibles sont axes principalement sur la gravite des blessures et analysent les facteurs potentiels qui influent sur celle-ci. Toutefois, les collisions par l'arriere peuvent aussi etre potentiellement correlees aux caracteristiques des vehicules, des routes, de l'environnement, du temps, de l'espace, de la circulation et des collisions. En outre, une heterogeneite non observee concernant les effets peut etre presente, ce qui peut etre different dans differentes collisions. Dans ce contexte, plusieurs modeles fondes sur l'equation d'estimation generalisee (EEG), elabores a l'aide de differentes matrices et distributions, sont proposes dans cette etude pour examiner les facteurs qui influent sur la gravite des blessures. Les modeles proposes tiennent compte a la fois des correlations liees aux collisions et de l'heterogeneite non observee, surpassant ainsi les modeles traditionnels en termes d'exactitude des previsions. Parmi les variables explicatives prises en compte dans cette etude, l'automobile, le minibus, le rapport de courbure, le temps pluvieux, le temps brumeux, tot le matin, le jeudi, l'automne, l'hiver, le debit journalier moyen annuel (DJMA) ont ete identifies comme facteurs contributifs. Cependant, des differences significatives ont ete observees entre les effets d'elasticite mesures par differents modeles, en particulier au niveau des minibus et du temps brumeux. Ainsi, cette etude verifie que les modeles fondes sur l'EEG representent une plus grande quantite d'heterogeneite non observee, produisent de meilleures performances en termes de precision, et presentent une puissance explicative plus coherente par rapport aux modeles traditionnels. [Traduit par la Redaction] Mots-cles : collision arriere, gravite des blessures, equation d'estimation generalisee, effet d'elasticite, 1. Introduction With constant development in the volume and modes of transportation and the progressive increase in the cumulative distance traveled throughout the world, instances of injury, death, and damage [...]

The low-pressure and low-oxygen environment of plateaus results in a greater workload for drivers, which contributes to the serious road safety situation in plateau areas in China. This study conducted four hazard perception experiments in Nanjing (50 m above sea level (asl)), Nyingchi (2995 m asl), Lhasa (3650 m asl), Nagqu (4460 m asl), and Yanghu Scenic Spot (4998 m asl) using UC-WIN/ROAD driving simulation software. A total of 31 drivers (23 males, 8 females) were recruited in this study, with a mean age of 28.0 years old and mean driving experience of 6.5 years. A bivariate correlation test was adopted to analyze the impacts of the elevation, age, acclimation period, gender, and driving experience on the perception-reaction time. Then, the drivers were divided into three groups (Good, Medium, and Poor) using K-means clustering. Finally, the marginal effects of the linear regression models for the three groups were calculated to augment the comparison. As expected, the elevation showed a positive correlation with the perception-reaction time but indicated a variance in influence effects in the three groups. Consistent with previous evidence, experienced drivers had better perception ability in the plateau. These findings could help in the design of high-elevation roads and guide the definition of suitability to drive in the plateau, thereby improving driving safety. Key words: hazard perception, experienced drivers, plateau area. La basse pression et un environnement faible en oxygene caracteristiques des plateaux entrainent une charge de travail accrue pour les conducteurs, ce qui contribue a une grave situation de securite routiere dans les zones de plateau en Chine. Dans le cadre de cette etude, on a mene quatre experiences de perception des risques a Nanjing (50 m), Nyingchi (2995 m), Lhasa (3650 m), Nagqu (4460 m) et Yanghu Scenic Spot (4998 m) a partir du logiciel de simulation de conduite UC-WIN/ROAD. Au total, 31 conducteurs (23 hommes et 8 femmes) ont ete recrutes dans le cadre de cette etude, avec un age moyen de 28 ans et une experience de conduite moyenne de 6,5 ans. Un test de correlation simple a ete adopte pour analyser les effets de l'elevation, de l'age, de la periode d'acclimatation, du sexe et de l'experience de conduite sur le temps entre la perception et la reaction (perception-reaction). Ensuite, les conducteurs ont ete divises en trois groupes (bon, moyen, et faible) en utilisant la classification automatique a K moyennes. Enfin, les effets marginaux des modeles de regression lineaire pour les trois groupes ont ete calcules pour completer la comparaison. Comme prevu, l'elevation a montre une correlation positive avec le temps de perception-reaction, mais a indique une variance des effets d'influence chez les trois groupes. Conformement aux donnees probantes anterieures, les conducteurs experimentes avaient une meilleure capacite de perception dans le plateau. Ces resultats pourraient aider a la conception des routes a haute elevation et guider la definition de l'aptitude a la conduite sur un plateau, ameliorant ainsi la securite routiere. [Traduit par la Redaction] Mots-cles : perception des risques, conducteurs experimentes, zone de plateau., 1. Introduction Plateau area accounts for almost 26% of the total land area in China (Zha et al. 2016). With elevation averaging 4000 m above sea level in the Qinghai-Tibet [...]

Existing optimization decision-making approaches for pavement maintenance and rehabilitation (M&R) ignore the construction length of preventive maintenance (PM) projects, and its negative effects are difficult to be transformed into cost. To address this issue, this study proposes a bi-objective decision-making model that incorporates the problem as the second objective into the two-stage bottom-up approach. The proposed model contains selection of performance indicators, Bayesian neural network-based probabilistic deterioration model, evaluation of initial M&R actions on a segment level, and bi-objective decision-making. It is solved by the enumeration method and the non-dominated sorting genetic algorithm II. Finally, the Pareto solutions are obtained. A solution is an M&R plan, where an initial action (treatment type) is selected for a pavement segment. Among the Pareto solutions, the one with the second objective greater than or equal to and closest to the shortest construction length, is the optimal M&R plan. In addition, compared with the model that converts the problem into a constraint, the proposed model recommends a better plan that can achieve higher performance at lower cost, which validates the strength of the proposed model. Decision-makers can adopt the proposed model to optimize pavement M&R plans that consider the construction length of PM projects. [ABSTRACT FROM AUTHOR]

This study proposes random-parameters multinomial logit models, with heterogeneity in means and variances, to explore the differences in the factors influencing injury severities of drivers involved in different types of two-vehicle crashes. The models are verified using crash data from the United Kingdom (UK) over three years (2016-2018). Three types of crashes are separately identified (car-car, car-truck, and truck-truck crashes). In this study, a wide variety of potential variables, including the driver, vehicle, road, and environmental characteristics, are considered, with two possible injury-severity outcomes: severe and slight injury. The results show that unobserved heterogeneity existed for young drivers in both car-car and truck-truck crash models and the 30mph speed limit in the three separate models. Remarkably variations are observed in crashes involving different types of vehicles. The driver's age and gender, speeding, sideswipes, presence of junctions, weekdays, unlit, and weather conditions significantly impact driver-injury severities in various types of vehicle crashes. These findings are expected to help policymakers seek to improve highway safety and implement proper safety countermeasures., Author(s): Qiang Wu [1]; Dongdong Song (corresponding author) [2]; Chenzhu Wang (corresponding author) [3]; Fei Chen [3]; Jianchuan Cheng [3]; Said M. Easa [4]; Yitao Yang [5]; Wenchen Yang [6,7] [...]

High fatality rates in frequent rear-end crashes have underscored significant safety concerns in China. This study aims to explore the mechanisms and determinants of rear-end crashes, with a particular focus on the factors influencing crash severity during weekdays and weekends (W-W). Employing the Random Parameter Logit Model (RPLM) to account for variability in data, we analyzed W-W rear-end crashes on the Beijing-Shanghai Expressway in Jiangsu province from 2017 to 2019, considering three severity levels: no injury, minor injury, and severe injury. Our comprehensive analysis covered variables from temporal, roadway, vehicle, crash, and environmental categories, alongside calculating the marginal effects of each significant variable on crash severity. Findings reveal temporal instability over the three-year period and notable differences in W-W crash severity. Out of all variables, four displayed random parameter characteristics, indicating potential interactions that influence crash outcomes. Specifically, our results indicate that rear-end crashes involving three or more vehicles on bridges are more likely to result in casualties. Interchange segments typically saw no injuries in two-vehicle crashes. Speeding during winter or on sunny days significantly increases the risk of injuries and fatalities. Furthermore, rear-end crashes in interchange areas during winter are particularly prone to causing injuries. These findings offer guidance for the development of effective safety countermeasures targed at different pediods. [ABSTRACT FROM AUTHOR]

Due to the effects of low-pressure and hypoxic environments at high altitudes, drivers in high-altitude areas exhibit increased perceived reaction times, leading to challenges in accurate speed estimation and handling judgment. This study aims to quantitatively analyze the impact of plateau environments on operating speeds at interchange exit ramps. Utilizing a UC/win-road simulator, six scenarios of expressway exit ramps were constructed. The simulation experiments involved 50 participants (35 males, 15 females) from Nanjing, China (altitude of 50 m) and 50 participants (36 males, 14 females) from Lhasa, China (altitude of 3,650 m). This research focused on examining the influence of the plateau environment on drivers' operating speeds, investigating variations in speed between drivers in plain and plateau areas, across genders, and during different acclimation periods. It also aimed to predict operating speeds at the midpoint and exit of the curve on the exit ramp for drivers in both plain and plateau areas. Based on these predictions, the study elucidated the trend of operating speed as influenced by the low-pressure and hypoxic conditions of the plateau, as well as the characteristics of the exit ramp's horizontal curve. Additionally, the research uncovered the internal correlations and potential reasons linking operating speed to drivers' perception and response abilities, physiological and visual load levels, and driving styles. [ABSTRACT FROM AUTHOR]

More collisions occur at the exit ramps of expressways due to frequent lane-changing behavior and interweaving between vehicles. Young drivers with shorter driving mileage and driving experience, radical driving styles, and worse behavior prediction are likelier to be involved in collisions at the exit ramps. This paper focuses on the correlation analysis between young drivers' characteristics and their visual and physiological attributes at expressway exit ramps. First, the driver's gender, driving experience, and mileage are classified. Then, seven expressway exit models are established using the UC/Win road modeling software. The driver's driving plane vision is divided into four areas using the K-means clustering algorithm. In addition, the driver's visual and heart rate attributes were analyzed at 500 m, 300 m, 200 m, and 100 m away from an expressway exit. The results show that the visual attributes, gender, and driving mileage of young drivers strongly correlate with the fixation times and average saccade amplitude. In contrast, the driving experience has almost no correlation with the fixation behavior of young drivers. Young drivers' driving experience and mileage strongly correlate with cardiac physiological attributes, but there is virtually no correlation with gender. The practical implications of these results should be helpful to highway planners and designers. [ABSTRACT FROM AUTHOR]

The purpose of this study is twofold: to construct a model for measuring dynamic available passing sight distance (APSD) during overtaking using light detection and ranging (LiDAR) data, and to investigate the effects of several critical variables on dynamic APSD. The analysis considers a single passed vehicle with several variables, including vehicle dimensions [D.sub.p], initial headway [I.sub.H], and speed differential [S.sub.D]. The proposed model simulates the passing process and dynamically estimates APSD considering two types of obstacles: (1) obstacles related to the road environment and (2) passed-vehicle obstacle which is critical especially on or near right-turn horizontal curves. Through multiple tests, the variation of dynamic APSD along with the passing maneuver is established. The results show that large [D.sub.p] or long [I.sub.H] may increase the adverse effect of the passed vehicle on dynamic APSD. Either too large or too small [S.sub.D] may lead to a relatively high risk of head-on collisions. Key words: passing sight distance, dynamic model, road safety, visualization, LiDAR data, application. Le but de cette etude est en deux volets : construire un modele pour mesurer la distance de visibilite de depassement disponible (DVDD) dynamique pendant le depassement en utilisant la detection de la lumiere et les donnees de telemetrie (LiDAR), et d'etudier les effets de plusieurs variables critiques sur la DVDD dynamique. L'analyse considere un seul vehicule depasse avec plusieurs variables, y compris les dimensions du vehicule [D.sub.p], l'espacement initial entre les vehicules [I.sub.H], et le differentiel de vitesse [S.sub.D]. Le modele propose simule le processus de depassement et estime dynamiquement la DVDD en tenant compte de deux types d'obstacles: (i) les obstacles lies a l'environnement routier et (ii) l'obstacle cause par le vehicule depasse qui est critique en particulier sur ou pres d'une courbe a droite horizontale. Grace a de multiples tests, on a etabli la variation de la DVDD dynamique ainsi que la manoeuvre de depassement. Les resultats montrent que des grandes [D.sub.p] ou un long [I.sub.H] peut amplifier l'effet negatif de la presence du vehicule depasse sur la DVDD dynamique. Un [S.sub.D] trop grand ou trop petit peut entrainer un risque relativement eleve de collision frontale. [Traduit par la Redaction] Mots-cles: distance de visibilite pour depassement, modele dynamique, securite routiere, visualisation, donnees LiDAR, application., Introduction Highway available sight distance (ASD) is the distance visible to a driver which is measured along the traveled path from the driver's eye to the closest position of an [...]

This study explores the temporal instability and non-transferability of the determinants affecting injury severities of pedestrians struck by motorcycles and non-motorcycles. Using the pedestrian-vehicle crash data in Rawalpindi, Pakistan, over three years (2017–2019), three possible crash injury severity categories (minor injury, severe injury, and fatal injury) are estimated using alternative models to account for unobserved heterogeneity. These are a random-parameters multinomial logit (RP-ML) model with heterogeneity in means and variances and a latent-class multinomial logit (LC-ML) model with class probability functions. Temporal instability and non-transferability in the effects of explanatory variables are confirmed using a series of likelihood ratio tests based on the two alternative models. Various variables are observed to determine pedestrian-injury severities, and the estimation results show significant temporal instability and non-transferability in both RP-ML and LC-ML models. However, several explanatory variables produce relatively temporally stable and transferable effects, providing valuable insights to implement effective countermeasures from a long-term perspective. Moreover, out-of-sample predictions are simulated to confirm the temporal instability and non-transferability. At the same time, the LC-ML models produce higher differences for temporal instability and lower differences for non-transferability compared to the RP-ML model. Understanding and depth comparing the estimation results, likelihood ratio tests, and out-of-sample predictions using alternative models is a promising direction for future research to explore how the observed and unobserved heterogeneity can be estimated in terms of temporal instability and non-transferability. [ABSTRACT FROM AUTHOR]

Understanding the factors contributing to crash severity, along with their influence degrees across different times of day, can assist in better highway design and in developing effective countermeasures for ameliorating highway safety (especially during nighttime). This study examines the influences of risk factors on crash severity, based on comparisons of nighttime and daytime crashes. By using a random parameter approach to account for unobserved heterogeneity, multivariate logit (RPML) models are proposed to analyze the crash severity based on the explanatory factors in terms of the crash, traffic, speed, road geometry, and sight characteristics. The goodness-of-fit and predictive measures highlight the better performance of the proposed models relative to standard models, as the proposed models reduce the unobserved heterogeneity and yield higher precision. In addition, the elasticity effects of the factors are calculated to investigate and compare their impact degrees in daytime and nighttime crashes. The findings could potentially be utilized to guide highway design and policies and to develop specific safety countermeasures., Author(s): Chenzhu Wang [1]; Ping Zhang [2]; Fei Chen (corresponding author) [1]; Jianchuan Cheng [1] 1. Introduction In 2015, the World Health Organization reported 217711 deaths in traffic crashes in [...]

Single and multi-vehicle (SMV) crashes remain a significant issue, causing serious safety and economic concerns, and therefore deserve more attention. Using crash data in the Beijing-Shanghai and Changchun-Shenzhen freeways over the five years (2015–2019), this paper explored the transferability and heterogeneity for crash type (single-vehicle versus multi-vehicle crashes) and spatiotemporal stability of determinants affecting the injury severity. The random parameters logit approach with heterogeneity in means and variances was used to model three possible crash injury severity outcomes (measured by the most severely injured individual in the crash) of no injury, minor injury, and severe injury and identify the determinants in terms of driver, vehicle, roadway, environment, temporal, spatial, traffic, and crash characteristics. Remarkable differences were observed in the SMV crashes, and the contributing factors also reported considerable temporal and (or) spatial instabilities. The insights of this study should be valuable to help freeway designers and decision-makers understand the contributing mechanism of the factors and develop the proper management strategies and enforcement countermeasures. [ABSTRACT FROM AUTHOR]

It is crucial for drivers to conduct rapid and effective risk perception and response processes when faced with hazardous driving situations. The low pressure and oxygen environment in the plateau results in a greater workload of drivers, contributing to a significant decline in perception and response ability. This study proposes a graph construction approach to model drivers' hazard response modes (HRMs) in plateau areas. A total of 31 drivers (23 males) aged 21 to 55 years (M [age] = 28.0 years, M [driving experience] = 6.5 years) were recruited to participate in four hazard perception experiments using a UC-WIN/ROAD driving simulator. The experiments were successively conducted in five cities with different altitudes, including Nanjing (50 m), Nyingchi (2,995 m), Lhasa (3,650 m), Nagqu (4,460 m), and Yanghu Scenic Spot (4,998 m). Then, according to the graph construction approach, four HRMs for drivers were extracted. In addition, two series of generalized linear models were proposed to analyze the relationships between the perception reaction time (PRT), HRM, altitude, age, acclimation period, gender, and driving experience. The effects of significant variables, including scenario types, altitude, acclimation period, driving experience, and gender, were used in the construction of HRM and risk perception ability of plateau drivers. These results showed that constructing HRMs to model the driving styles of plateau drivers is feasible and effective, enabling future driving assistance systems to be better customized for drivers in such a particular condition. [ABSTRACT FROM AUTHOR]

The process of pavement deterioration involves uncertainties, and neural networks have been widely used in pavement performance prediction due to their high accuracy. However, the overwhelming majority of current performance prediction models based on neural networks are deterministic. Therefore, this study combined Bayesian theory and neural networks to establish a Bayesian neural network (BNN)-based probabilistic model for predicting pavement deterioration. The proposed model was built on the pavement data in Shanxi Province, China. This study first refined data using the K-Nearest Neighbour and empirical methods, and then selected input features based on correlation coefficient methods. Using the refined data, the deterministic neural network model was established to obtain the prior probability distribution of weights, and then the BNN-based probabilistic model was developed. Compared with the sole neural network model, the BNN-based model not only retains comparable prediction accuracy to the neural network model, but also incorporates uncertainties. The BNN-based model is also theoretically superior to the Markov-based probabilistic model because the former can incorporate all factors and does not need to classify performance values into states. The BNN-based model shall provide more reliable prediction results of pavement deterioration and help engineers make more reasonable maintenance decisions. [ABSTRACT FROM AUTHOR]

To examine the difference in contributing factors of rear-end crashes of different injury severity involving different types of vehicles, this paper proposed random-parameters multinomial logit models with heterogeneity in means and variances. A three-year (2017–2019) rear-end crash data collected from Beijing-Shanghai Highways in China was used to calibrate the models. The rear-end crashes were classified as five types (Car-Car, Car-Truck, Truck-Truck, Truck-Car, Others). With two possible injury severity outcomes of medium/severe injury and light injury, a wide range of possible variables including crash, traffic, speed, geometric, and sight characteristics were considered in this study. Likelihood ratio tests revealed the rationality of adopting merged models using the data across three-year periods. Remarkably significant differences were shown in crashes involving different types of vehicles. The results accounting for the possible heterogeneity could be of value to roadway designers and traffic management departments seeking to promote highway safety and raise accurate safety countermeasures. [ABSTRACT FROM AUTHOR]

A new technology Unmanned Aerial Vehicles (UAV), is increasingly used by land surveyors for various applications. CV software for processing drone images has made great strides in making it easier to use and reducing the need for human intervention. CV method relies on tie point extraction from a set of overlapping images, which used to generate a model for the area of interest. However, using this technique to process large areas at an acceptable resolution requires huge photos and significant computer resources. Therefore, this study aims to assess the measurement accuracy that can be obtained from a single UAV image, taking into account the accuracy and time consumption. Firstly, the multirotor UAV was used to capture the ground at a certain altitude. Then the data were processed using two software packages. The outcomes of both software were compared against actual data for accuracy assessment. The results show that both processing methods provide excellent accuracy result; the ground resolution is within the range of 0.2~3.7cm\pixel, which comply with international standards. In conclusion, this study demonstrates the feasibility of using high accuracy UAV image to extract the measurement of a flat area with reliable accuracy in a short time.