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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]

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]

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]

A lower helmet-wearing rate and overspeeding in Pakistan are critical risk behaviors of motorcyclists, causing severe injuries. To explore the differences in the determinants affecting the injury severities among helmeted and non-helmeted motorcyclists in motorcycle crashes caused by overspeeding behavior, single-vehicle motorcycle crash data in Rawalpindi city for 2017–2019 is collected. Considering three possible crash injury severity outcomes of motorcyclists: fatal injury, severe injury and minor injury, the rider, roadway, environmental, and temporal characteristics are estimated. To provide a mathematically simpler framework, the current study introduces parsimonious pooled random parameters logit models. Then, the standard pooled random parameters logit models without considering temporal effects are also simulated for comparison. By comparing the goodness of fit measure and estimation results, the parsimonious pooled random parameters logit model is suitable for capturing the temporal instability. Then, the non-transferability among helmeted and non-helmeted overspeeding motorcycle crashes is illustrated by likelihood ratio tests and out-of-sample prediction, and two types of models provide robust results. The marginal effects are also calculated. Several variables, such as age, cloudy and weekday indicators illustrate temporal instability. Moreover, several variables are observed to only show significance in non-helmeted models, showing non-transferability across helmeted and non-helmeted models. More educational campaigns, regulation and enforcement, and management countermeasures should be organized for non-helmeted motorcyclists and overspeeding behavior. Such findings also provide research reference for the risk-compensating behavior and self-selected group issues under overspeeding riding considering the usage of helmets. [ABSTRACT FROM AUTHOR]

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]

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 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. [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 30 mph 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. [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]

Distraction and overspeed behaviors are acknowledged as two significant contributors to single-vehicle motorcycle crashes, injuries and fatalities resulting from which are severe and critical issues in Pakistan. To explore the temporal instability and differences in the factors determining the injury severities between single-vehicle motorcycle crashes caused by distraction and overspeed behaviors, this study estimated two groups of random parameter logit models with heterogeneity in means and variances. Single-vehicle motorcycle crash data in Rawalpindi city between 2017 and 2019 was used for model estimation, and a wide variety of explanatory variables relating to the rider, roadways, environments, and temporal attributes was simulated in the models. The current study considered three possible crash injury severity outcomes: minor injury, severe injury and fatal injury. Likelihood ratio tests were conducted to explore the temporal instability and non-transferability. Marginal effects were also calculated to further reveal temporal instability of the variables. Except for several variables, the most significant factors reported temporal instability and non-transferability, manifested as the effects varied from year to year and across different crashes. Moreover, out-of-sample prediction was also implemented to capture temporal instability and non-transferability between distraction and overspeed crash observations. The non-transferability between motorcycle crashes caused by distraction and overspeed behaviors provides insights into developing differentiated countermeasures and policies targeted at preventing and mitigating single-vehicle motorcycle crashes caused by the two risk-taking behaviors. [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.

Most existing pavement maintenance and rehabilitation (M&R) decision-making approaches ignore the length constraint of preventive maintenance (PM). This study proposed a practical optimization-based decision-making approach that incorporates the length constraint of PM. For the current popular bottom-up decision-making approach, incorporating the length constraint of PM will cause its solution methods (i.e., evolutionary algorithms) to fail. Therefore, this study developed a sliding-window random repair (SWRR) method based on repair methods and sliding-window methods. The SWRR method was inserted into the evolutionary algorithm (i.e., genetic algorithm) of a two-stage bottom-up approach to solve this dilemma. That is, the proposed decision-making approach is composed of the SWRR method and the two-stage bottom-up approach. A parametric study showed that the M&R decision-making plan recommended by the proposed approach was less expensive than the actual engineering plan, but the achieved performance was 5.7% higher. The results prove that the proposed approach can indeed solve the dilemma caused by the length constraint of PM and produce a better decision-making plan. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explored the effects of contributing factors on crash frequency, by injury severity of all, daytime, and nighttime crashes that occurred on freeways. With three injury severity outcomes classified as light injury, minor injury, and severe injury, the effects of the explanatory variables affecting the crash frequency were examined in terms of the crash, traffic, speed, geometric, and sight characteristics. Regarding the model estimations, the lowest AIC and BIC values (2263.87 and 2379.22, respectively) showed the superiority of the random-parameter multivariate negative binomial (RPMNB) model in terms of the goodness-of-fit measure. Additionally, the RPMNB model indicated the highest R2 (0.25) and predictive accuracy, along with a significantly positive α parameter. Moreover, transferability tests were conducted to confirm the rationality of separating the daytime and nighttime crashes. Based on the RPMNB models, several explanatory variables were observed to exhibit relatively stable effects whereas other variables presented obvious variations. This study can be of certain value in guiding highway design and policies and developing effective safety countermeasures. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Most two-stage optimization bottom-up (TSOBU) approaches for infrastructure maintenance management aim to obtain an optimal maintenance schedule that minimizes the total cost (i.e., the sum of agency and user costs). However, in most developing countries, existing TSOBU approaches are less applicable to actual projects because of the lack of user cost and maintenance experience. Therefore, a practical TSOBU approach with a new optimization objective was proposed to maximize network-level benefit-cost ratios (B/C) and simultaneously minimize network-level risk. The proposed TSOBU approach includes facility-level and network-level optimizations. Each available initial maintenance and rehabilitation (M&R) action is evaluated at the facility level, and an optimal M&R plan is obtained in the network-level optimization. The proposed approach incorporates the influence of infrastructure deterioration uncertainty. An actual example is presented to illustrate the application and rationality of the proposed TSOBU approach, which resulted in two main findings. First, as the risk-aversion coefficients increase, the total M&R costs gradually increase, while the average performance of the road network slowly declines. Second, compared with the actual M&R plan in 2019, the recommended M&R plan from the proposed TSOBU approach achieved a 5.18% higher average performance with lower M&R costs. The first result reveals that the total M&R costs and average performance of road networks are affected by risk-aversion coefficients in the proposed approach. The second result implies that the proposed approach can help maintenance engineers to obtain an economic maintenance plan without user cost values. [ABSTRACT FROM AUTHOR]

Periodic measurements of sight distance on as-built roads and subsequent removal of sight obstructions are important for guaranteeing highway safety. In this paper, an accurate and efficient framework is proposed for automated detection of obstacles restricting sight distance on highways using mobile laser scanning (MLS) data. The developed framework was implemented in MATLAB (version: 2020a) and operates along the mapping trajectory recorded in the MLS data. A linear index-based segmentation technique was used to efficiently segment MLS point clouds; based on this, methods for identifying target points, removing on-road noise, and detecting sight obstacles were then developed. The target points for sight obstacle detection were derived from the pavement surface points, which were identified via a similarity-and-connectivity-based technique. Considering that on-road vehicle noise may adversely affect the detection of sight obstructions, a data-refinement procedure was developed to remove them and to fill the missing point regions they caused. For each sight point in the mapping trajectory, a segmentation-based algorithm was applied to achieve fast sight obstacle detection. Tests on MLS data from two real-world highways in the case study showed that the proposed framework detected sight obstructions on the combined highway alignments in the presence of noise. The procedure detected sight obstacles at each sight point within 0.2 s, with limited computational power. Therefore, it can be applied in real-world projects and will be of interest to researchers and practitioners in this field. [ABSTRACT FROM AUTHOR]

For the optimization analysis of pavement maintenance programs, combinatorial optimization is a pervasive problem. Genetic algorithms (GAs) are widely used to solve combinatorial optimization problems in pavement maintenance programs. However, owing to the stochastic search mechanisms underlying GAs, they are more likely to produce a relatively unsatisfactory solution due to premature convergence. Hence, a binary cuckoo search (BCS) algorithm was implemented to solve the optimization problem. To the best of our knowledge, this is the first time that a BCS algorithm has been applied to pavement maintenance management system. Three hypothetical cases are used to investigate and demonstrate the effectiveness of the BCS algorithm, in which uncertainty-based performance degradation is considered. The results of a comparison between GA and BCS clearly justify the advantages of the search paths underlying the BCS in alleviating premature convergence. Therefore, the BCS algorithm can help decision makers to make more appropriate trade-off decisions for pavement maintenance programs. [ABSTRACT FROM AUTHOR]

Highways provide the basis for safe and efficient driving. Road geometry plays a critical role in dynamic driving systems. Contributing factors such as plane, longitudinal alignment, and traffic volume, as well as drivers' sight characteristics, determine the safe operating speed of cars and trucks. In turn, the operating speed influences the frequency and type of crashes on the highways. Methods. Independent negative binomial and Poisson models are considered as the base approaches to modeling in this study. However, random-parameter models reduce unobserved heterogeneity and obtain higher dimensions. Therefore, we propose the random-parameter multivariate negative binomial (RPMNB) model to analyze the influence of the traffic, speed, road geometry, and sight characteristics on the rear-end, bumping-guardrail, other, noncasualty, and casualty crashes. Subsequently, we compute the goodness-of-fit and predictive measures to confirm the superiority of the proposed model. Finally, we also calculate the elasticity effects to augment the comparison. Results. Among the significant variables, black spots, average annual daily traffic volume (AADT), operating speed of cars, speed difference of cars, and length of the present plane curve positively influence the crash risk, whereas the speed difference of trucks, length of the longitudinal slope corresponding to the minimum grade, and stopping sight distance negatively influence the crash risk. Based on the results, several practical and efficient measures can be taken to promote safety during the road design and operating processes. Moreover, the goodness-of-fit and predictive measures clearly highlight the greater performance of the RPMNB model compared to standard models. The elasticity effects across all the models show comparable performance with the RPMNB model. Thus, the RPMNB model reduces the unobserved heterogeneity and yields better performance in terms of precision, with more consistent explanatory power compared to the traditional models. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: In the last few decades, the growing popularity of e-bikes in China has raised public concerns regarding an increasing number of fatalities and injuries involving e-bikes. Although previous studies have explored the impacts of personality on driving behaviors of automobile drivers and motorcyclists, little attention has been paid to safety-related issues involving e-bikers from the aspect of their personality traits and sociocognitive variables. The aim of this study was to examine the effects of personality on e-bikers' risk-taking behaviors and test the effectiveness of the model proposed by Ulleberg and Rundmo (2003) among e-bikers.Methods: Four hundred and five Chinese e-bikers aged 16 to 61 completed a self-administrated questionnaire, which included questions investigating their demographics, personality traits (anger, altruism, sensation-seeking, normlessness), risk perceptions, safety attitudes, and risky riding behaviors. The reliability and validity of all scales were first examined through reliability analysis and principal component analysis, respectively, and a structural equation model was developed and fitted to test the relationships among e-bikers' personality traits, risk perceptions, safety attitudes, and risky riding.Results: A satisfactory level of reliability and validity was reached for all variables. Anger, altruism, sensation-seeking, and normlessness were all significantly related to e-bikers' risk perceptions and unsafe riding, and only altruism correlated significantly to safety attitude. For 2 sociocognitive variables, safety attitudes was directly and negatively related to respondents' risky riding, and risk perception only exerted impacts on riding behaviors by affecting safety attitudes.Conclusions: Personality traits of e-bikers impacted their riding behaviors both directly and indirectly, and sociocognitive variables played an intermediate role in the personality-behavior relationship. The results revealed the importance of personality traits in influencing e-bikers' risky riding and also verified the applicability of the personality-behavior model proposed by Ulleberg and Rundmo (2003) among e-bikers. The findings of this study may provide an empirical basis for evidence-based safety interventions for e-bikers in China. [ABSTRACT FROM AUTHOR]

Objective: Illegal parking in emergency lanes (paved highway shoulders) is becoming a serious road safety issue in China. The aim of this study was to (1) examine the utility of the theory of planned behavior (TPB) extended with descriptive norms, past behavior, facilitating and deterring circumstances, sensation seeking, and invulnerability in predicting Chinese drivers' intentions toward illegal emergency lane parking; (2) investigate whether respondents' demographic characteristics would impact their views toward the behavior and predictive patterns of intentions; and (3) identify significant predictors of intentions.Methods: In this cross-sectional study, eligible respondents were all qualified Chinese drivers. A self-administered questionnaire was employed to collect data, including demographic information, descriptive norms, past behavior, facilitating and deterring circumstances, sensation-seeking, and scenario-based invulnerability combined with TPB constructs. Descriptive statistics, multivariate analyses of variance (MANOVAs), and a series of hierarchical multiple linear regression analyses were conducted in SPSS.Results: A total of 435 qualified drivers (234 males and 201 females) with a mean age of 35.2 years (SD =10.3) were included in analysis. The descriptive analysis showed that most participants reported weak intentions (M = 2.35) to park illegally in emergency lanes with negative attitude (M = 3.19), low perceived support (M = 2.91), and high control (M = 5.08) over the behavior. The model succeeded in explaining 64% of the variance in intentions for the whole sample, and principal TPB components accounted for 21% of variance in intentions after demographic variables were controlled for. MANOVAs revealed that significant differences of respondents' opinions toward illegal emergency lane parking were only found between better educated drivers (with college education background) and less-educated ones. Separate regression analyses revealed that the predictive pattern of better educated participants also differed significantly from that of less-educated ones.Conclusion: The study revealed that perceived behavioral control, past behavior, facilitating circumstance, and invulnerability emerged as consistently significant predictors of Chinese drivers' intentions to park illegally in emergency lanes. Findings of this study may have some practical implications in developing multifaceted interventions or education processes for illegal emergency lane parking in China. [ABSTRACT FROM AUTHOR]

Vertical alignment, which includes vertical grades and lengths, is a critical aspect of highway design policy that influences safety. A full understanding of the effect of vertical grade and segment length on highway safety can help agencies to evaluate or adjust their design policies regarding vertical alignment design features (grade and length). For this reason, it is useful to assess the current relationships between design policy and safety performance. To address this task, this paper uses data from interstate segments to first establish the relationship between these design features and safety. Safety is expressed in terms of the three different levels of crash severity (fatal, injury, and property damage only). In its analysis, the paper departs from the traditional univariate models (where each crash severity is modeled separately) and instead uses a seemingly unrelated negative binomial (SUNB) technique, a multivariate model that duly accounts for the unobserved shared effects between the different levels of crash severity. In addition, the paper’s models duly recognize and account for the holistic nature of the grade and tangent length effects: the effect of the sum (interaction) of the vertical grade and length is different from the sum of their individual effects. The paper investigates the relationships for rural and urban interstate highway segments. Against the background of the developed models, the paper evaluates current design policies (specifications on vertical alignment grade and length) for similar classes of highways at a number of countries and presents a set of nomograms that feature lines representing points of equal safety performance. These charts can be used by the highway agencies to evaluate and compare their current or possible future highway design policies. [ABSTRACT FROM AUTHOR]

The current method of computing the minimum pedestrian green interval for intersection signal timing assumes that the component variables are deterministic. This paper presents a probabilistic method in which the pedestrian start-up time and walking speed are random variables. To establish pedestrian characteristics, data were collected at 14 intersections in downtown, suburban, and tourist areas. The method is based on a safety margin that is defined as the difference between the supplied and demanded green intervals, where the demanded green interval is a random variable. Relationships for the mean and standard deviation of the safety margin of the demanded green interval are developed on the basis of the first-order second-moment analysis. A closed-form solution for the minimum supplied green interval is then derived as a function of the relevant variables, including the vehicular intergreen interval and its component variables. A procedure for establishing the walk and the flashing 'don't walk' intervals is presented. Graphical aids for determining the minimum pedestrian green interval were developed, and application of the proposed method is illustrated using numerical examples. The sensitivity analysis shows that the minimum pedestrian green interval is much more sensitive to the walking speed than the start-up time or their correlation. [ABSTRACT FROM AUTHOR]

This research investigated the size effect of hydrated lime on the moisture susceptibility of warm-mix-asphalt (WMA) mixtures with selected additives. A superfine hydrated lime used in the study was produced from regular hydrated lime (RHL) by a Los Angeles abrasion machine. The superfine lime was measured to be 660 nm, approximately half of the RHL size of 1.3 µm, a sub-nano-sized hydrated lime (SNHL). Scanning electron microscope (SEM) pictures were taken to observe the difference of the crushed lime in size, shape, and texture. Experiments for antistripping properties, such as indirect tensile strength (ITS), tensile strength ratio (TSR), flow, and toughness were conducted on WMA mixtures containing SNHL and RHL as control samples. A total of 18 mixtures and 108 specimens were prepared from three types of aggregate, three WMA additives, and two types of lime. The results indicated that the microstructures of the SNHL and RHL are irregular: the lime particles have fractured and rough surfaces with sharp angles; some small spherical particles in the nano size were found in SNHL but not in RHL. Also, WMA mixtures using SNHL have greater ITS and TSR than those using RHL; an increases of 8% in ITS and 10% in TSR by using SNHL over RHL were observed for the WMA mixtures investigated. Finally, the antistripping properties of WMA mixtures containing SNHL were affected predominately by aggregate type and the WMA additive. [ABSTRACT FROM AUTHOR]

Generating and updating roadway geometric elements from aerial images is necessary for multiple geospatial information system purposes, which have been addressed through various approaches. However, most existing methods cannot deal with challenges such as differently curved ramp characteristics, whereas measurements of geometric elements are still of low effectiveness and accuracy. This paper presents a new method for the semi-automatic extraction of horizontal parameters of curved highway ramps using Google Earth images. The proposed method first determines a road centerline manually using a graphics editor software; the file is then saved and processed with a program that analyzes and splits the centerline into its basic components. After that, the curvature analysis and linear fitting methods are integrated for automatic PC and PT determination. Finally, at the post-processing stage, the radii of the curves are computed automatically using the least-squares method. The proposed method was tested on four highway ramps and validated by comparison with the obtained design plans. Results show that the proposed method successfully detected the curves' PC/PT and measured their radii with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

Pedestrian safety is alarming worldwide, and it is well validated that distracted walking/crossing involving mobile phone use would significantly compromise pedestrian safety. Some existing studies demonstrated that distracted pedestrians would spend more time to cross street, miss more safe opportunities to cross and pay less attention to the road environment, etc. As a result, they are more likely to be hit by an oncoming vehicle. Specifically, with respect to the distraction results from mobile phone use for communication in road user groups, previous research has examined the relationship between social networks and mobile phone use among drivers and motorcyclists. However, very little similar research was found in the field of pedestrian study. This study performed an online survey to investigate with whom pedestrians were most likely to communicate with while crossing street in a Chinese sample. The association between social networks and self-reported injury/ near miss event was also examined. To provide an insight into the difference in communication pattern between scenarios, the results were compared with the patterns while driving, motorcycling and the general patterns. Results indicate that pedestrians are most likely to communicate with friends (31.2%), followed by spouses (24.5%). Additionally, participants who frequently talk to parents/children have a greater likelihood of being involved in injury/ near miss events than those talk to the others. Compared with the prevalence of mobile phone use among drivers and motorcyclists reported in previous studies, mobile phone use is more prevalent among pedestrians, especially as they are more likely to communicate with colleagues. In sum, the results demonstrate that social networks play an important role in mobile phone use during street crossing, and pedestrians are more likely to communicate with people who are socially closest to them. The effect of social networks on mobile phone use (especially for communication) among pedestrians should be considered in the development of traffic safety countermeasures. [ABSTRACT FROM AUTHOR]

The paper proposes a method supported by MATLAB for detection and measurement of missing point regions (MPR) which may cause severe road information loss in mobile laser scanning (MLS) point clouds. First, the scan-angle thresholds are used to segment the road area for MPR detection. Second, the segmented part is mapped onto a binary image with a pixel size of ε through rasterization. Then, MPR featuring connected 1-pixels are identified and measured via image processing techniques. Finally, the parameters regarding MPR in the image space are reparametrized in relation to the vehicle path recorded in MLS data for a better understanding of MPR properties on the geodetic plane. Tests on two MLS datasets show that the output of the proposed approach can effectively detect and assess MPR in the dataset. The ε parameter exerts a substantial influence on the performance of the method, and it is recommended that its value should be optimized for accurate MPR detections. [ABSTRACT FROM AUTHOR]