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Truck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck's condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively. [ABSTRACT FROM AUTHOR]

Crashes involving large trucks have a significant association with increasing fatalities or serious injuries. Identifying the factors affecting large trucks crashes can be helpful in reducing the number of crashes and corresponded consequences. The purpose of this review was to appraise the published papers on factors affecting large truckinvolved crashes. Data were compiled through searching Google Scholar, Scopus, Science Direct, and Web of Science using keywords included "large truck and accident severity", "large truck and accident", "large truck and crash severity", "large truck and crash", "large truck and fatal accident", "large truck and fatal crash", "large truck and injury", and "large truck and collide". The accomplishment process had two steps: excluding unrelated studies and reviewing related ones to select papers meeting the inclusion criteria. According to data collected, 31 papers achieved the inclusion criteria. The results indicated that speed, driver age, location type, collision type, seat belt wearing, light condition, vehicle technical condition, time, road surface condition, and accident type were common factors in the included papers. From the perspective of our study, speed, location type, and light condition seem to be the most frequent factors in crashes involving large trucks. The present study helps to access the studies on factors affecting the large truck-involved crashes. Thereby, the experts can perform measures to decrease the probability and severity of traffic accidents. [ABSTRACT FROM AUTHOR]

Truck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck’s condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively.

This study develops a differential variable speed limit (DVSL) which assigns different speed limits for car and truck, and varies speed limits based on traffic conditions. The proposed DVSL algorithm changes speed limits in real time based on truck percentage and occupancy immediately upstream of the ramp and the average speed of the control road sections upstream of the ramp. DVSL algorithm also considers spatial coordination of speeds, which gradually changes the speed limits in successive road sections upstreamof the ramp when the severe congestion occurs. The study tested the impacts of DVSL and three other speed limit strategies on delay and safety for a section of the Gardiner Expressway in Toronto, Canada using the VISSIM traffic simulation model. The other strategies are 1) uniform speed limit (USL), 2) differential speed limit for car and truck (DSL), and 3) USL & DSL (U&D) e i.e., USL at low truck percentage and DSL at high truck percentage. It was found that DVSL showed the lowest delays for both car and truck among the four strategies. This is mainly because DVSL increased the spacing between vehicles in the right lane upstream of the on-ramp and facilitated vehicles' merging into the mainline freeway. It was also found that DVSL showed the lowest likelihood of rear-end crash between the lead and following vehicles among the four strategies. This study demonstrates that the proposed DVSL algorithm can better control car and truck speeds to reduce delay and improve safety of car-truck mixed traffic flow on freeways. [ABSTRACT FROM AUTHOR]

A validated computational model is used to investigate the response of concrete barriers subjected to impact by single-unit trucks and tractor-semitrailers with different weights and approach speeds. The computational results are used to formulate a performance-based design (PBD) framework in terms of impact demand and barrier capacity. Impact demands for both truck categories are expressed as a function of truck speed and weight, whereas barrier capacity is judged in terms of flexural rotation at the base of the barrier. Based on the computational data, an argument is made that adopting the proposed PBD methodology for impact-resistant barrier design can allow bridge designers to meet predetermined performance objectives for given vehicular impact scenarios. [ABSTRACT FROM AUTHOR]

International crash data indicate that roadside characteristics contribute to more than half of all roadside accidents involving serious injury or death. Therefore, research on roadside safety is urgently needed. Based on the vehicle departure speed, pavement height (i.e., the difference between pavement elevation and ground elevation), slope gradient, and horizontal curve radius, this study uses PC-Crash simulation software to carry out tests of trucks and cars exiting a road. A chi-squared automatic interaction detection (CHAID) decision tree is used to explore the causative mechanism of vehicle rollover, and the concept of a "safe slope" to ensure that vehicles do not roll over is proposed. Aiming at straight and curved sections, discriminant functions of vehicle rollover and nonrollover are fitted through Bayesian discriminant analysis, and safe slope calculation models for trucks and cars are then constructed. Based on the obtained safe slope models, calculation methods for the safe slope and the roadside clear zone width involving different traffic compositions are proposed by calibrating the lateral distance from the final position of nonrollover vehicles to the road edge. The results show that the factors affecting vehicle rollover are, in descending order of importance, the slope gradient, pavement height, vehicle type, departure speed, and horizontal curve radius. For a section with a large proportion of cars, the slope gradient should not be steeper than 1:3.5. The horizontal curve radius should not be less than 600 m for a section with a large proportion of trucks and a slope gradient steeper than 1:3.5 or shallower than 1:2.5. Additionally, for a section with a pavement higher than 0.5 m and a slope gradient steeper than 1:2.5, the operating speed limit should be lower than 60 km/h. These research results have theoretical value and practical significance to improve the driving safety level and reducing the risk of roadside accidents. [ABSTRACT FROM AUTHOR]

• The role of at-fault party in large truck-involved crashes has been investigated. • Support Vector Machine and Random Parameter Logit Model were utilized for analysis. • Fatigue and deviation to left in the at-fault truck-driver crashes led to fatality. Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers' working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes. [ABSTRACT FROM AUTHOR]

• We modeled crash severity in run-off-the-road crashes. • Two mixed logit models were developed. • Results confirmed the contribution of roadway geometric design features to injuries. • The study provides valuable information to improve roadside design policies. Roadway departure (RwD) crashes, comprising run-off-road (ROR) and cross-median/centerline head-on collisions, are one of the most lethal crash types. According to the FHWA, between 2015 and 2017, an average of 52 percent of motor vehicle traffic fatalities occurred each year due to roadway departure crashes. An avoidance maneuver, inattention or fatigue, or traveling too fast with respect to weather or geometric road conditions are among the most common reasons a driver leaves the travel lane. Roadway and roadside geometric design features such as clear zones play a significant role in whether human error results in a crash. Method : In this paper, we used mixed-logit models to investigate the contributing factors on injury severity of single-vehicle ROR crashes. To that end, we obtained five years' (2010–2014) of crash data related to roadway departures (i.e., overturn and fixed-object crashes) from the Federal Highway Administration's Highway Safety Information System Database. Results : The results indicate that factors such as driver conditions (e.g., age), environmental conditions (e.g., weather conditions), roadway geometric design features (e.g., shoulder width), and vehicle conditions significantly contributed to the severity of ROR crashes. Conclusions : Our results provide valuable information for traffic design and management agencies to improve roadside design policies and implementing appropriately forgiving roadsides for errant vehicles. Practical applications : Our results show that increasing shoulder width and keeping fences at the road can reduce ROR crash severity significantly. Also, increasing road friction by innovative materials and raising awareness campaigns for careful driving at daylight can decrease the ROR crash severity. [ABSTRACT FROM AUTHOR]

• This study uncovered the behavioral pathways in bicycle-motor vehicle crashes. • This study offers a methodological framework to quantify the behavioral pathways in crashes. • A path analysis was performed to link contributing factors, pre-crash actions and injury severities. • The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina. • The results highlighted factors related to risky pre-crash actions that lead to severe injuries. Introduction: This study performed a path analysis to uncover the behavioral pathways (from contributing factors, pre-crash actions to injury severities) in bicycle-motor vehicle crashes. Method: The analysis investigated more than 7,000 bicycle-motor vehicle crashes in North Carolina between 2007 and 2014. Pre-crash actions discussed in this study are actions of cyclists and motorists prior to the event of a crash, including "bicyclist failed to yield," "motorist failed to yield," "bicyclist overtaking motorist," and "motorist overtaking bicyclist." Results: Model results show significant correlates of pre-crash actions and bicyclist injury severity. For example, young bicyclists (18 years old or younger) are 23.5% more likely to fail to yield to motor traffic prior to the event of a crash than elder bicyclists. The "bicyclist failed to yield" action is associated with increased bicyclist injury severity than other actions, as this behavior is associated with an increase of 5.88 percentage points in probability of a bicyclist being at least evidently injured. The path analysis can highlight contributing factors related to risky pre-crash actions that lead to severe injuries. For example, bicyclists traveling on regular vehicle travel lanes are found to be more likely to involve the "bicyclist failed to yield" action, which resulted in a total 44.38% (7.04% direct effect + 37.34% indirect effect) higher likelihood of evident or severe injuries. The path analysis can also identify factors (e.g., intersection) that are not directly but indirectly correlated with injury severity through pre-crash actions. Practical Applications: This study offers a methodological framework to quantify the behavioral pathways in bicycle-motor vehicle crashes. The findings are useful for cycling safety improvements from the perspective of bicyclist behavior, such as the educational program for cyclists. [ABSTRACT FROM AUTHOR]

• European Commission encourages a more efficient and safer transport logistics chain. • Productivity gains would result from cross-border traffic of larger trucks/megatrucks. • This paper closes the gap on the potential safety effects of megatruck circulation. • Fewer accidents but more fatalities in European countries where megatrucks circulate. • A multi-approach policy is recommended to minimize negative impacts. Introduction: The European Union (EU) has developed different strategies to internalize the costs of excessive motor traffic in the road freight transport sector. One of these is a relaxation of restrictions on the size and load capacity of trucks that circulate between member States and a proposal has been made for Longer and Heavier Vehicles (LHVs) to be allowed to circulate across borders. LHVs are the so-called "megatrucks" (i.e., trucks with a length of 25 meters and a weight of 60 tonnes). Megatrucks have allowed to circulate for decades in some European countries such as Norway, Finland, and Sweden, world leaders in traffic accident prevention, although the impact that cross-border traffic would have on road safety is still unknown. Methods: This article provides an econometric analysis of the potential impact on road safety of allowing the circulation of "megatrucks" throughout the EU. Results: The findings show that countries that currently allow megatrucks to circulate present lower traffic accident and fatality levels, on average. Conclusions: The circulation of this type of vehicle is only advisable in countries where there is a certain degree of maturity and demonstrated achievements in the field of road safety. Practical applications: European countries that have allowed megatruck circulation obtaining better road safety outcomes in terms of accidents, although the accident lethality rate seems to be higher. Consequently, introducing megatruck circulation requires a prior proper preparation and examination. [ABSTRACT FROM AUTHOR]

• The relationships between crash severity and contributory factors are explored. • A two-level binary logistic model approach is applied to address higher-level variations. • A similar model is established for crash severity on general freeway as a benchmark. • Uniqueness of crash severity in tunnels is concluded in a comparative term. • Influential factors contribution to severe crash are identified and policy implications are made. Introduction: With the rapid development of transportation infrastructures in precipitous areas, the mileage of freeway tunnels in China has been mounting during the past decade. Provided the semi-constrained space and the monotonous driving environment of freeway tunnels, safety concerns still remain. This study aims to investigate the uniqueness of the relationships between crash severity in freeway tunnels and various contributory factors. Method: The information of 10,081 crashes in the entire freeway network of Guizhou Province, China in 2018 is adopted, from which a subset of 591 crashes in tunnels is extracted. To address spatial variations across various road segments, a two-level binary logistic approach is applied to model crash severity in freeway tunnels. A similar model is also established for crash severity on general freeways as a benchmark. Results: The uniqueness of crash severity in tunnels mainly includes three aspects: (a) the road-segment-level effects are quantifiable with the environmental factors for crash severity in tunnels, but only exist in the random effects for general freeways; (b) tunnel has a significantly higher propensity to cause severe injury in a crash than other locations of a freeway; and (c) different influential factors and levels of contributions are found to crash severity in tunnels compared with on general freeways. Factors including speed limit, tunnel length, truck involvement, rear-end crash, rainy and foggy weather and sequential crash have positive contributions to crash severity in freeway tunnels. Practical applications: Policy implications for traffic control and management are advised to improve traffic safety level in freeway tunnels. [ABSTRACT FROM AUTHOR]

Injury severity is a key aspect in road safety, especially at railway crossings. Traditional statistical methods such as ordered probit or ordered logit models provide adequate approaches to these analyses when plenty of covariates are available. However, these traditional approaches can be combined with other techniques, for instance, spatial methods and Bayesian analyses to provide better estimations by including the effects of covariates that are not explicitly included in the models, among other benefits. Bayesian analyses are useful when low crash frequencies and small sample sizes are present in the data, which is common for railway crashes. A case from San José, Costa Rica is used to demonstrate the use of the proposed methodology. Spatial analyses were implemented through conditional autoregressive (CAR) models up to third-ordered neighbors. A Bayesian approach was used to obtain more accurate estimations for the model coefficients and parameters. The results show how this methodology provides better overall results than a non-spatial ordered probit model. The first-order CAR model was determined to be the best overall model and therefore is preferred. [ABSTRACT FROM AUTHOR]

Objective: Forward collision warning (FCW) and automatic emergency braking (AEB) have the potential to prevent or mitigate many large truck crashes. While these systems are known to be effective in passenger vehicles, less is known about their effectiveness in large trucks. The objective of this study was to estimate the effectiveness of these systems in reducing real-world crash rates of large trucks.Methods: Data on Class 8 trucks operating on limited-access highways during 2017-2019 were obtained from SmartDrive Systems. Detailed data on exposure measures and crash circumstances were extracted from video footage by both automated means and manual coding. Crash rates were compared by front crash prevention technology (FCW, AEB, neither), both for all police-reportable crashes overall and for relevant crash types.Results: FCW was associated with a statistically significant 22% reduction in the rate of police-reportable crashes per vehicle miles traveled, and a significant 44% reduction in the rear-end crash rate of large trucks. AEB also was associated with significant reductions-12% overall and 41% for rear-end crashes. Warnings were issued in 31% of rear-end crashes for FCW-equipped trucks. AEB intervened in 43% of rear-end crashes; about two thirds of these interventions involved autobrake activations. On average, speed was reduced by over half between the time of the intervention and impact for both systems. Observed reductions in same-direction sideswipe and roadway departure crashes per mile traveled were smaller in magnitude than those of rear-end crashes; these were consistent with other crash avoidance technologies suspected to be bundled with FCW/AEB in some cases, and very few front crash prevention interventions occurred in these types of crashes.Conclusions: FCW and AEB are effective countermeasures for crashes in which large trucks rear-end other vehicles. Large truck safety is expected to improve as new trucks are increasingly equipped with these systems. FCW has the advantage that some of these systems can be retrofitted to existing trucks, so benefits can be realized sooner and with less investment. [ABSTRACT FROM AUTHOR]

There have been efforts to restrict older trucks in many jurisdictions all over the world. The primary goal of the restrictions is to minimize greenhouse gas emissions. In addition to the environmental benefits, it is also possible that the truck age restriction could contribute to the enhancement of traffic safety. Older trucks are subject to longer travel-miles than newer trucks and tend to have higher mechanical failure rates. Extremely few studies have been done to explore the impact of trucks' age on their crash occurrence due to mechanical problems. This study aims to investigate the association between the truck crashes due to mechanical issues and the truck age. Two approaches are adopted to achieve the objective. First, a chi-square test reveals that the proportions of the mechanical failures among older trucks are higher than those among newer ones ( χ 2 = 256.199, p < 0.0001). Second, the modeling results indicate that the number of truck crashes due to mechanical failures is significantly increased by the truck age. The findings suggest that policies restricting older trucks should consider not only environmental effects but also traffic safety benefits. [ABSTRACT FROM AUTHOR]

Featured Application: This work can support the scientific community and the waste sector in the identification of the most suitable treatments for end-of-life tyres. Waste tyres and their accumulation is a global environmental concern; they are not biodegradable, and, globally, an estimated 1.5 billion are generated annually. Every year around 350,000 tons of end-of-life tyres (ELT) are managed in Italy, collected from cars, two-wheeled vehicles, trucks, up to large quarry vehicles and agricultural vehicles. ELTs are collected and sent for material or energy recovery, in line with the circular economy principles. This paper investigates the environmental impacts of two common scenarios of ELT treatments. Specifically, it is analysed the recycling of crumb rubber (CR, deriving from the tyre shredding) for the composition of bituminous mixtures for the wearing course of roads. This scenario is compared with the energy recovery route in a dedicated incinerator. To this aim the standardised methodology of Life Cycle Assessment (ISO 14040-44) is employed. Results shows that for most part of the impact categories analysed, the material recovery presents higher environmental benefits if compared with energy recovery. [ABSTRACT FROM AUTHOR]

Wyoming has one of the highest truck cash rates in the United States. Three safety agencies in the state, theWyoming Department of Transportation (WYDOT), the Wyoming Highway Patrol (WHP), and the Wyoming Trucking Association (WTA), coordinated to identify three zones throughout Wyoming with high truck traffic with the objective of improving road safety in the state. The literature review indicated that the previous violations can be used to predict future offenses and crashes. Therefore, violation data, in addition to crash data, were used to identify the performance of WHP in identification of the groups at higher risks of future offenses and crashes. Descriptive and statistical analyses were performed across different temporal, environmental, driver, and roadway characteristics to identify the areas where the three organizations should allocate or shift their resources. The results of the descriptive analyses showed that adverse weather condition variables are some of the areas where the agencies should emphasize their resources. The results of statistical modeling indicated that weather and road conditions, gender, and age are some of the factors that contribute to truck-related crashes. Although much research has been conducted by using crash data to identify the underlying contributory factors to truck crashes, little has been done in identifying preventive measures that could be taken by the safety agencies based upon crash and violation data. The study methodology can be implemented by other states to inform governing entities regarding a potentially more efficient use of resources in particular areas. [ABSTRACT FROM AUTHOR]

The Marmara is the most highly trafficked region in Turkey in terms of seaway and highway transport congestion. The transport mode-based CO2 emissions budget for the region needs to take more environmentally friendly measures. In this study, 13 ro-ro and ferry lines (RFLs) are evaluated in the Sea of Marmara to compare ship-generated CO2 emissions with road transport as if the carried vehicles had used the highway instead of transport by RFL. Additionally, this study revealed the management strategies for CO2 emissions reduction for both transport modes under current conditions. The total CO2 emissions budget of 13 RFLs in the Sea of Marmara is higher than the potential CO2 emissions of their carried vehicles. Using the methods of Entec UK Ltd and Trozzi and Vaccaro, the amount of CO2 emissions generated by the RFLs is 204,470.99 and 170,459.85 t/year, respectively. The potential CO2 emissions of road vehicles carried in 2017, 2018 and 2019 are computed by applying Tier 1 methods as 121,690.54, 106,844.89, and 100,921.95 t/year, respectively. It is observed that shifting trucks from the highway to the seaway contributes to a reduction of the yearly CO2 emissions budget generated from the transport sector to provide sustainable transportation management in the region. [ABSTRACT FROM AUTHOR]

• E-scooter riders in Washington, DC, were most frequently injured on sidewalks. • Injuries were most severe on the road. • Injuries were also most severe among frequent riders. Introduction: E-scooter rider injuries have been growing, but little is known about how trip and incident characteristics contribute to their severity. Method: We enrolled 105 adults injured while riding e-scooters who presented to an emergency department in Washington, DC, during 2019. Enrolled participants completed an interview during the emergency department visit, and their charts were abstracted to document their injuries and treatment. Logistic regression examined the association of incident location and circumstances with the likelihood of sustaining an injury on the Abbreviated Injury Scale (AIS) ≥ 2, while controlling for rider characteristics. Results: The most common locations of e-scooter injuries in our study sample occurred on the sidewalk (58%) or road (23%). Accounting for other trip and rider attributes, e-scooter riders injured on the road were about twice as likely as those injured elsewhere to sustain AIS ≥ 2 injuries (RR, 1.96; 95% CI, 1.23–2.36) and those who rode at least weekly more often sustained AIS ≥ 2 injuries compared with less frequent riders (RR, 1.86; 95% CI, 1.11–2.32). Conclusions: Greater injury severity for riders injured on the road may reflect higher travel speeds. Practical applications: Injury severity associated with riding in the road is one factor that jurisdictions can consider when setting policy on where e-scooters should be encouraged to ride, but the risk of any crash or fall associated with facilities should also be examined. Although injuries are of lower severity on sidewalks, sharing sidewalks with slower moving pedestrians could potentially lead to more conflicts. [ABSTRACT FROM AUTHOR]

• This paper considers unobserved or latent features when modeling injury severity. • Latent class analysis is used to segment crash data to reduce heterogeneity. • Partial proportional odds models are developed to explore bicyclist injury severity. • Some variables affect bicyclist injury severity differently between various clusters. • Policy-related recommendations and advices on traffic management are provided. Introduction: Bicyclists are more vulnerable compared to other road users. Therefore, it is critical to investigate the contributing factors to bicyclist injury severity to help provide better biking environment and improve biking safety. According to the data provided by National Highway Traffic Safety Administration (NHTSA), a total of 8,028 bicyclists were killed in bicycle-vehicle crashes from 2007 to 2017. The number of fatal bicyclists had increased rapidly by approximately 11.70% during the past 10 years (NHTSA, 2019). Methods: This paper conducts a latent class clustering analysis based on the police reported bicycle-vehicle crash data collected from 2007 to 2014 in North Carolina to identify the heterogeneity inherent in the crash data. First, the most appropriate number of clusters is determined in which each cluster has been characterized by the distribution of the featured variables. Then, partial proportional odds models are developed for each cluster to further analyze the impacts on bicyclist injury severity for specific crash patterns. Results: Marginal effects are calculated and used to evaluate and interpret the effect of each significant explanatory variable. The model results reveal that variables could have different influence on the bicyclist injury severity between clusters, and that some variables only have significant impacts on particular clusters. Conclusions: The results clearly indicate that it is essential to conduct latent class clustering analysis to investigate the impact of explanatory variables on bicyclist injury severity considering unobserved or latent features. In addition, the latent class clustering is found to be able to provide more accurate and insightful information on the bicyclist injury severity analysis. Practical Applications: In order to improve biking safety, regulations need to be established to prevent drinking and lights need to be provided since alcohol and lighting condition are significant factors in severe injuries according to the modeling results. [ABSTRACT FROM AUTHOR]

• Crash prediction by injury severity and vehicle damage was conducted and compared. • Multivariate Poisson-Lognormal (MVPLN) models were estimated. • Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) models were estimated. • Unobserved heterogeneity was accommodated by both models. • A surrogate measure for injury severity was evaluated. Introduction: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. Methods: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. Results: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. Conclusions: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity. [ABSTRACT FROM AUTHOR]

• Truck drivers are classified into three groups by using latent class clustering. • Some risk factors affecting crash severity have different impacts on distinct groups. • More driver-related risk factors are associated with the high-risk driver group. Introduction: Fatal crashes that include at least one fatality of an occupant within 30 days of the crash cause large numbers of injured persons and property losses, especially when a truck is involved. Method: To better understand the underlying effects of truck-driver-related characteristics in fatal crashes, a five-year (from 2012 to 2016) dataset from the Fatality Analysis Reporting System (FARS) was used for analysis. Based on demographic attributes, driving violation behavior, crash histories, and conviction records of truck drivers, a latent class clustering analysis was applied to classify truck drivers into three groups, namely, "middle-aged and elderly drivers with low risk of driving violations and high historical crash records," "drivers with high risk of driving violations and high historical crash records," and "middle-aged drivers with no driving violations and conviction records." Next, equivalent fatalities were used to scale fatal crash severities into three levels. Subsequently, a partial proportional odds (PPO) model for each driver group was developed to identify the risk factors associated with the crash severity. Results' Conclusions: The model estimation results showed that the risk factors, as well as their impacts on different driver groups, were different. Adverse weather conditions, rural areas, curved alignments, tractor-trailer units, heavier weights and various collision manners were significantly associated with the crash severities in all driver groups, whereas driving violation behaviors such as driving under the influence of alcohol or drugs, fatigue, or carelessness were significantly associated with the high-risk group only, and fewer risk factors and minor marginal effects were identified for the low-risk groups. Practical Applications: Corresponding countermeasures for specific truck driver groups are proposed. And drivers with high risk of driving violations and high historical crash records should be more concerned. [ABSTRACT FROM AUTHOR]

• Pedestrian-injury severities at pedestrian-vehicle crash hotspots with an annual upward trend are modeled. • Spatiotemporal trend analysis is used to identify spatial aggregated pedestrian-vehicle crash hotspots with annual uptrends. • Average nearest neighbor and spatial autocorrelation tests are used to give a reference for grid and neighborhood distances. • Latent class random parameter logit models are utilized to explore the heterogeneity within and between the subgroups. • Contributing factors to the pedestrian-injury severity are identified and analyzed with specific countermeasures. Introduction: With the increasing trend of pedestrian deaths among all traffic fatalities in the past decade, there is an urgent need for identifying and investigating hotspots of pedestrian-vehicle crashes with an upward trend. Method: To identify pedestrian-vehicle crash locations with aggregated spatial pattern and upward temporal pattern (i.e., hotspots with an upward trend), this paper first uses the average nearest neighbor and the spatial autocorrelation tests to determine the grid distance and the neighborhood distance for hotspots, respectively. Then, the spatiotemporal analyses with the Getis-Ord Gi* index and the Mann-Kendall trend test are utilized to identify the pedestrian-vehicle crash hotspots with an annual upward trend in North Carolina from 2007 to 2018. Considering the unobserved heterogeneity of the crash data, a latent class model with random parameters within class is proposed to identify specific contributing factors for each class and explore the heterogeneity within classes. Significant factors of the pedestrian, vehicle, crash type, locality, roadway, environment, time, and traffic control characteristics are detected and analyzed based on the marginal effects. Results: The heterogeneous results between classes and the random parameter variables detected within classes further indicate the superiority of latent class random parameter model. Practical Applications: This paper provides a framework for researchers and engineers to identify crash hotspots considering spatiotemporal patterns and contribution factors to crashes considering unobserved heterogeneity. Also, the result provides specific guidance to developing countermeasures for mitigating pedestrian-injury at pedestrian-vehicle crash hotspots with an upward trend. [ABSTRACT FROM AUTHOR]

• Commercial motor vehicle safety and security is of paramount interest. • Physical and operational characteristics of commercial motor vehicles affect driving activities. • Lane changing poses a major potential hazard for many drivers. • Increase in the number of travel lanes decrease the severity of lane change crashes. Introduction: One of the challenging tasks for drivers is the ability to change lanes around large commercial motor vehicles. Lane changing is often characterized by speed, and crashes that occur due to unsafe lane changes can have serious consequences. Considering the economic importance of commercial trucks, ensuring the safety, security, and resilience of freight transportation is of paramount concern to the United States Department of Transportation and other stakeholders. Method: In this study, a mixed (random parameters) logit model was developed to better understand the relationship between crash factors and associated injury severities of commercial vehicle crashes involving lane change on interstate highways. The study was based on 2009–2016 crash data from Alabama. Results: Preliminary data analysis showed that about 4% of the observed crashes were major injury crashes and drivers of commercial motor vehicles were at-fault in more than half of the crashes. Acknowledging potential crash data limitations, the model estimation results reveal that there is increased probability of major injury when lane change crashes occurred on dark unlit portions of interstates and involve older drivers, at-fault commercial vehicle drivers, and female drivers. The results further show that lane change crashes that occurred on interstates with higher number of travel lanes were less likely to have major injury outcomes. Practical Applications: These findings can help policy makers and state transportation agencies increase awareness on the hazards of changing lanes in the immediate vicinity and driving in the blind spots of large commercial motor vehicles. Additionally, law enforcement efforts may be intensified during times and locations of increased unsafe lane changing activities. These findings may also be useful in commercial vehicle driver training and driver licensing programs. [ABSTRACT FROM AUTHOR]

Cyclist injury severity in bicycle-vehicle crashes is influenced by numerous factors that are related to driver, vehicle, cyclist, crash, roadway, temporal, and environmental characteristics. Results indicate that differences remain between cyclist injury severities sustained in crashes occurred at different locations (intersections and nonintersection locations). However, few research efforts have been made to examine and compare different contributing factors to cyclist injury severity at such locations. Therefore, this study aims to identify and analyze the impact of various factors on cyclist injury severity at intersection and nonintersection locations. Police-reported data from 2007 to 2014 in North Carolina are used in this study. Three mixed-logit models are developed based on the preestimated multinomial logit models. Market segmentation is conducted to analyze cyclist injury severity at separate locations. An ordered probit model is also developed and compared with the mixed-logit model at both locations. Furthermore, cyclists drinking alcohol; driving van, bus, or single-unit truck; motorists' fault; inclement weather; dusk or dawn are found to have a significant impact on cyclist injury severity at intersections. Cyclist gender, drivers drinking alcohol, vehicle speed, speeding, rural or urban areas, traffic control, curved road are found to have a significant impact at nonintersection locations. [ABSTRACT FROM AUTHOR]

Although the probability of vehicle collision with bridge piers is relatively low, it may cause the pier to fracture and even the entire bridge to collapse, resulting in casualties and huge economic losses. Therefore, bridge piers should be properly designed against vehicle collision. This paper aims to make a state-of-the-art review of the research on vehicle collision with bridge piers, to summarize the achievements and current limitations in this field, and to give some suggestions for future research. It is organized within a framework of performance-based design, which is divided into four types of problems, that is, hazard analysis, structural analysis, damage analysis, and loss analysis. Studies show that reinforced concrete (RC) piers under vehicle impact generally exhibit three damage modes, that is, local damage, shear damage, and flexural damage. When a large truck hits a pier, the engine and container (cargo) may contribute to a two-stage impact characteristic, which has a great influence on the response and damage of the pier. The vehicular impact (force) models and nonlinear response models of RC members under impact loads have been developed respectively, which can be combined to quickly analyze the nonlinear response of RC piers under vehicle impact. Deformation-based methods should be developed to quantify the damage level of RC piers under different damage modes. Current codes still mainly adopt the equivalent static force design method, but some provisions regarding probabilistic (reliability) analysis have appeared. More attention should be paid to the statistical analysis of roadside crashes and vehicle-related parameters in order to obtain a more realistic probabilistic analysis model, and further establish a performance-based design method for RC piers subjected to vehicle collision. [ABSTRACT FROM AUTHOR]

To understand heavy truck crash severity, a total of 1,818 sample crashes occurring over a six-year period (2011-2016) were recorded on four typical expressway segments in mountainous regions of Jiangxi and Shaanxi, China. The crashes were investigated using an ordered logit approach to determine the significant variables contributing to truck crash severity. Elasticity analysis was then used to measure the pseudoelasticity of each independent variable. A total of 12 explanatory variables, including less than three years driving experience of truck driver, type of commercial transport task of the vehicle, brake failure, speeding behavior, overloading condition, steep downhill grade of the road and slippery surface condition, hours between midnight and dawn, winter season, adverse weather, and head-on and rear-end collision types, were found to be statistically significantly correlated with the severity of truck crashes occurring on Jiangxi and Shaanxi mountainous expressways. Only truck drivers older than 55 years of age, presence of tunnel and runoff collision type were identified to have a significant and positive effect on the severity of truck crashes on Shaanxi expressways. Taken together, these findings provide useful guidance to develop safer design standards, stricter regulations and more efficient technical countermeasures in order to reduce or prevent the occurrence of serious heavy-truck crashes on mountainous expressways in China. [ABSTRACT FROM AUTHOR]

Express lanes (ELs) provide an alternative way for improving the capacity of the existing freeway network without considerably expanding the roadway footprint. Although much research has been done to explore factors contributing to crashes on these facilities, there is not much discussion on factors influencing their injury severity. This study explored factors influencing the injury severity of crashes on EL facilities. A Support Vector Machine (SVM) model trained by the Firefly Algorithm was used to identify factors influencing the injury severity of crashes on EL facilities. The analysis was based on three years of crash data (2012–2014) from four EL facilities in California, totaling 61 miles. The results indicated that the following factors increased the probability of an injury or a fatality: concrete barriers, high average annual daily traffic, rolling or mountainous terrain, weekend, adverse road surface condition, and nighttime condition. Moreover, wide right and left shoulder widths decreased the probability of having an injury or a fatality. The results provide insights into the influence of different geometric characteristics and crash-related factors on the severity of crashes on EL facilities. The study findings may assist agencies to better understand the impacts of factors contributing to injury and fatal crashes on EL facilities and implement strategies to reduce the severity of these crashes. [ABSTRACT FROM AUTHOR]

Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver's attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads. [ABSTRACT FROM AUTHOR]

Traffic barriers are in widespread use all around the world as measures for decreasing the severity of roadside crashes. Considering a comprehensive set of more than 160 variables, this article presents the investigation of the variables affecting the severity of crashes involving traffic barriers. An ordinal logistic regression approach was applied to 10-year crash data collected in Wyoming. The crash data included a wide range of various parameters related to traffic, drivers, and road geometry conditions. The results show that the type of vehicles and traffic barriers has the highest effect on the severity of crashes involved with traffic barriers. The rigid barrier might be the best barrier type for reducing trucks' crash severity, whereas guardrail barrier did not provide the same level of protection for trucks on high truck traffic-volume roads. The presented analytical model is more practical for rural states similar to the Rocky Mountains Region due to its similar conditions. The conclusions of the study will help transportation agencies by (1) guiding the designers when selecting the proper type of traffic barriers based on different road conditions and (2) ranking the risk of various traffic barrier segments for prioritizing the most hazardous ones for future improvements. [ABSTRACT FROM AUTHOR]

• Geometric features of 137 miles of traffic barriers were collected on non-interstate highways in Wyoming. • The safety performance of traffic barriers was analyzed in terms of crash frequency, and crash severity. • Box beam barriers with a height of 27–29 inches were more likely to be associated with lower severity in crashes compared to other traffic barrier types. • Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 reduced the probability of fatalities and severe injuries in crashes. • In terms of crash frequency, flare traffic barriers decreased the possibility of crashes compared to parallel traffic barriers. Introduction: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. Method: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. Results: From the results, box beam barriers with a height of 27–29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes. [ABSTRACT FROM AUTHOR]

The article focuses on rise of the Intelligent Transportation Systems (ITS) and implementation of technologies in vehicles and infrastructures, various types of detectors. sensors, and cameras are being installed in cars and roadway systems.

Nowadays, trucks are widely used in the freight transport. Every accident involving a truck as well as any longer disruption of its traffic flow causes great losses. The considered disturbance in traffic flow occurred for a truck with dual tires on its rear axle resulted from the damage to the power transmission system from the engine to the drive wheels of the track. Investigating the cause of one such damage, in the form of rupture of the rear driveshaft, was the purpose of this article. The parameters of the analyzed truck, its wheels, its engine characteristics, the scheme of its cornering, the model of its rear-axle shaft, and the model of loading the bearing set of its rear dual wheel were included in the paper. Additionally, the probable cause of the truck rear axle shaft breakage was established. [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]

We propose an approach for floating bridge installation and operation. A floating bridge aims to carry heavy vehicles, trucks, and people over a body of water. However, bridge installation and operation are mainly performed by humans regardless of adverse conditions, such as active combat or disaster occurrence. For installing and operating floating bridges under such conditions, we devised a solution based on control system design and automatic installation. The floating bridge system is controlled and positioned by a power propulsion system that is attached to floating units of the bridge. An optimal control system based on state estimator, which is designed using a robust control framework, is applied to install and operate the bridge. A simulation analysis and experiments demonstrate the effectiveness of proposed method on a bridge system model comprising five floating units. [ABSTRACT FROM AUTHOR]

In the US, weight limits on interstate highways are established by the federal government while weight limits on other roadways are established by state or local governments. State weight limits for log trucks exceed federal interstate highway weight limits in all major timber-producing states. This disparity discourages log trucks from traveling loaded on interstate highways. This study compared the safety and efficiency of log truck transportation on current routes to routes utilizing interstate highways in eight wood baskets in the US South. Timber harvest and delivery locations were collected from logging businesses, log truck owners, and forest landowners. GPS truck tracking software and ArcGIS were used to estimate travel distance and travel time on optimal interstate routes and current routes that avoided interstate highways. The sample included 907 unique routes from 257 harvest sites. An average of 39% of routes would benefit from interstate highway access and 66% of harvest sites had at least one load that would benefit from interstate access. Evidence suggests that interstate routes would be safer than current routes because log trucks would encounter 41% fewer intersections, 33% fewer stop signals, and approximately one fewer school zone per route. Despite no significant change in average haul distance, interstate highway access would result in estimated cumulative annual travel cost savings of $7.4 million (USD) in the wood baskets analyzed. Findings suggest that allowing state-legal, loaded log trucks to operate on interstate highways would improve the safety and efficiency of timber transportation in the US South. [ABSTRACT FROM AUTHOR]

The goal of this study was to develop a new method for identifying the actual risky spots by using the geographic information system (GIS). For this purpose, in this study, three different methods for detecting hotspots are developed, i.e., (1) the annual average daily traffic (AADT) normalization method, (2) AK crashes (A is the incapacitating crash, and K is the fatal crash) percentage method, and (3) distribution difference method. To evaluate the performances of these three hotspot detection methods along with a baseline method that only considered the frequency of crashes, we applied these three methods to identify the top 20 hotspots for truck crashes in two representative areas in Texas. The results indicated that (1) all three proposed methods produced more reasonable results than the baseline method, and (2) the "distribution difference" method outperformed the other methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to identify factors such as: roadway operational characteristic, crash characteristic, surrounded environment, vehicle type, driver information, severity level of driver, and temporal information, affecting driver injury severity involving in single-vehicle run off road accident occurred on 2-lanes highways and 4-lanes highway using Multinomial Logit model. The analyses used secondary data obtained from police accident record (extracted from Highway Accident Information Management System (HAIMS)).The variables were found to increase chance of fatality are driver older than 55-year-old, driver under influence of alcohol, drowsiness driver, run off road on straight and curve, accident on highways with depressed median and accident on concrete pavement. The variables were found to mitigate severity are adult driver 25- 35-year-old, using seat belt, accident on highway with raised median and hit fixed object accident. The contributions of this study were drawn: Thailand related authorities such as Department of Highway or Royal Thai Police should emphasize their effort on education campaigns on road safety for all road users, especially old drivers, enforce the law on drunk driving and seatbelt; and for road design perspective: monitor and build roadside safety features such as safety barrier alongside the highway particularly run off road black spot and curve roads. The study also mentions the safety benefit of asphalt pavement over concrete pavement and safety planner should consider implementing raised median in urban area for safety purpose. [ABSTRACT FROM AUTHOR]

An actual test of medium-duty truck collision with twin anti-ram bollards of steel tube is analysed and simulated with different mass-spring-damper models to study bollard design requirements. Test data is obtained from test report of a medium-duty truck crashed into two fixed twin bollards at speed 78.3 km/h. Maximum impact load and impact height at that time is important in the analysis. Bollard height should be close to or larger than the vehicle's centre of gravity height to avoid climbing of the truck on the bollard. However, increasing impact height yields also increase in failure risk of bollard. Foundation is also critical in success of the bollard in successfully stopping the vehicle. The bollard should be fixed to the frame embedded in the concrete foundation so that the deformation in concrete be minimised. The bollard should be so stiff to stop the vehicle while most of the impact energy is absorbed by the vehicle through deformation of its frontal sections. A single-degree freedom linear mass-spring-damper model is the simplest model, but its results are not in line with test data. Single-degree non-linear model simulates the peak load but not the load history. However, using engine mass instead of truck mass in the single-degree model provides acceptable impact force data for the bollard. Two-degree freedom mass-spring damper linear model seems to simulate both truck's and bollard's deformation in a good manner. Non-linear analysis simulates the collision in a more realistic way, but it requires more data to be determined with testing. [ABSTRACT FROM AUTHOR]

A risk-based method implemented within a performance-based design framework is proposed for protecting bridge piers subject to truck impact. Monte Carlo simulations are performed to calculate the probability of damage exceedance of impacted piers based on demand-to-capacity ratios. The demand is computed from the base shear of the pier under a calibrated pulse loading, which represents impact by a single-unit truck or a tractor-semitrailer. Four different highway classes are considered in this study with different ratios of truck types in the traffic volume, including single-unit trucks and tractor-semitrailers. Based on the simulation results, the probabilities of exceedance for different damage levels of typical or critical bridges, given a collision event, are provided for each highway class as a function of the posted speed limit and shear capacity of the impacted pier. The proposed methodology is verified through high fidelity simulations of truck collision with a full-bridge model and formatted in a manner that facilitates its direct adoption into current specifications. [ABSTRACT FROM AUTHOR]

• Over 70% of injuries to bus passengers were resulted from non-collision incidents in Hong Kong. • Inconsistences were found between severity models of non-collision injuries and collision injuries. • Ignoring unobserved heterogeneity resulted in biased estimations. • A 3A strategy was proposed to mitigate non-collision injuries to bus passengers. Introduction : Although public buses have been demonstrated as a relatively safe mode of transport, the number of injuries to public bus passengers is far from negligible. Existing studies of public bus safety have focused primarily on injuries caused by collisions. Surprisingly, limited effort has been devoted to identifying factors that increase the severity of passenger injuries in non-collision incidents. Method : Our study therefore investigated the injury risk of public bus passengers involved in collision incidents and non-collision incidents comparatively, based on a police-reported dataset of 17,383 passengers injured on franchised public buses over a 10-year period in Hong Kong. A random parameters logistic model was established to estimate the likelihood of fatal and severe injuries to passengers as a function of various factors. Results : Our results indicated substantial inconsistences in the effects of risk factors between models of non-collision injuries and collision injuries. The severity of passenger injuries tended to increase significantly when non-collision incidents occurred due to excessive speed of bus drivers, on double-decker buses, in less urbanized areas, in winter, in heavy rains, during daytime, and at night without street lighting. Elderly female passengers were also found more likely to be fatally or severely injured in non-collision incidents if they lost their balance while boarding, alighting from, or standing on a bus. In comparison, the following factors were associated with a greater likelihood of fatal or severe injuries in collision incidents: elderly female passengers, standing passengers who lost balance, buses out of driver control, double-decker buses, collisions with vehicles or objects, and less urbanized areas. Practical Applications: Based on our comparative analysis, more targeted countermeasures, namely "4E" (engineering, enforcement, emergency, and education) and "3A" (awareness, appreciation, and assistance), were recommended to mitigate collision injuries and non-collision injuries to public bus passengers, respectively. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) has attracted many attentions in both academic and industrial sectors due to its promising characteristics compared with other conventional joining techniques. However, it still needs some improvements to become reliable and cost-effective for industrial applications. In this study, the aim is to develop a right angle friction stir welding (RAFSW) technique to assemble large side panels of truck boxes so that the technique be industrially reliable and cost-effective. For this purpose, Taguchi method and artificial neural network (ANN) modeling were used to design the experiments and optimize the welding process parameters. The efficient working window of welding process parameters was obtained at traverse speeds as high as 1700 mm/min. Then, the technique was developed to operate in force control mode using a spring load tool holder and a newly designed tool with a tapered entry. It is shown that the developed technique could tolerate high levels of joint fit-up disturbances like big gaps between the sheets before welding. Moreover, it was demonstrated that the developed technique could be operated using large, low-cost, CNC machining routers and with clamping only at the corners of panels. The use of weld tags prior to welding was beneficial in order to reduce clamping and distortion while maintaining a high strength level along the joints. NX-NASTRAN Finite Element software and various experiments including static and fatigue tests of the panels were used to design and evaluate the welded assembly. It was found that the developed RAFSW technique has the potential to be used for truck panel assembly. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this study was to investigate the prevalence and behavioral and psychosocial correlates of road traffic injuries (RTIs) among Chinese university students.Methods: A cross-sectional survey was conducted among fifty universities in China, using a multi-stage sampling methodology. The participants were asked to report their RTIs in the past year. The chi-square test and binary logistic regression analysis were utilized to identify factors associated with RTIs, including specific types of RTIs.Results: Among the 11,770 participants, a total of 1,482 university students reported at least one RTI yielding an overall weighted injury prevalence of 12.96% over the past year. Estimated weighted prevalence by type was 6.10%, 5.94%, 5.12%, and 5.35% for automobile (car, truck, or bus), bicycle, motorcycle, and pedestrian injuries, respectively. Logistic regression analysis found that students who studied at low-level universities, smoked cigarettes, drank alcohol, slept less than 7 hours, went to bed after 12:00 am, or students with psychological distress were more likely to experience overall and four types of RTIs. Students who studied in the eastern universities had a higher likelihood of automobile injury, motorcycle injury and pedestrian injury than those who studied in western universities.Conclusions: Several critical factors associated with RTIs were identified. These findings have implications for the design and implementation of RTI prevention and interventions programs targeted at university students. [ABSTRACT FROM AUTHOR]

• The study uncovers non-stationary correlates of bicyclist injury severity in traffic crashes. • The modeling approach accounts for the spatial heterogeneity in traffic crashes. • The study explored geo-referenced bicycle-motor vehicle crashes in North Carolina. • Results show correlates of bicyclist injury severity vary substantially across North Carolina. • Modeling results can be used to localize bicycling safety countermeasures. Introduction: Bicyclists are among vulnerable road users with their safety a key concern. This study generates new knowledge about their safety by applying a spatial modeling approach to uncover non-stationary correlates of bicyclist injury severity in traffic crashes. Method: The approach is Geographically Weighted Ordinal Logistic Regression (GWOLR), extended from the regular Ordered Logistic Regression (OLR) by incorporating the spatial perspective of traffic crashes. The GWOLR modeling approach allows the relationships between injury severity and its contributing factors to vary across the spatial domain, to account for the spatial heterogeneity. This approach makes use of geo-referenced data. This study explored more than 7,000 geo-referenced bicycle--motor-vehicle crashes in North Carolina. Results: This study performed a series of non-stationarity tests to identify local relationships that vary substantially across the spatial domain. These local relationships are related to the bicyclist (bicyclist age, bicyclist behavior, bicyclist intoxication, bicycle direction, bicycle position), motorist (driver age, driver intoxication, driver behavior, vehicle speed, vehicle type) and traffic (traffic volume). Conclusions: Results from the regular OLR are in general consistent with previous findings. For example, an increased bicyclist injury severity is associated with older bicyclists, bicyclist being intoxicated, and higher motor-vehicle speeds. Results from the GWOLR show local (rather than global) relationships between contributing factors and bicyclist injury severity. Practical Applications: Researchers and practitioners may use GWOLR to prioritize cycling safety countermeasures for specific regions. For example, GWOLR modeling estimates in the study highlighted the west part (from Charlotte to Asheville) of North Carolina for increased bicyclist injury severity due to the intoxication of road users including both bicyclists and drivers. Therefore, if a countermeasure is concerned with the road user intoxication, there may be a priority for the region from Charlotte to Asheville (relative to other areas in North Carolina). [ABSTRACT FROM AUTHOR]

Objective: The objective of this paper is to identify the list of crash severity contributing factors and evaluate their impact on multiple-vehicle crashes on two high use Trans-European interurban, freight corridors in Spain (southern Europe): Madrid - Irùn and Barcelona - Almerìa.Methods: We have used both logistic regression and random forests to identify crash severity predictors and estimate their impacts on crash outcomes. Although both statistical methods can provide useful information to help explain the safety implications of highway crashes, using both methods may further enable a more comprehensive understanding of this phenomenon. For this effort, we disaggregated the crash data into different crash types (i.e., head-on, angle, sideswipe and rear-end) and analyzed this data using roadway design elements, driver characteristics, and environmental factors. To identify the most important predictors of crash severity, we used the random forests data mining approach. We then used ordered logit models to estimate the effect of external factors on the severity of each crash type. Finally, we assessed the accuracy of the model estimates using bootstrap sampling.Results: The results of data mining analyses indicated that roadway design factors such as horizontal and vertical curvature, super elevation, and lane and shoulder width are among the most important factors associated with crash severity. The results of logistic regression show that the impact of the selected roadway element on the crash outcome is conditional on the crash type and the direction of the effects is not always consistent.Conclusions: The contribution of this paper to the existing literature is two-fold: the first important contribution of the paper is related to the safety analysis of two of the most important freight corridors in Spain and southern Europe. The second contribution of this paper is to address the existing gap in the literature relating to the comparison and compatibility of data mining and the logistic regression model. [ABSTRACT FROM AUTHOR]

Fire events due to tanker truck accidents have caused significant damage to numerous steel girder overpass bridges over the last several decades. Due to the limited availability of funding for public infrastructure, engineers must tailor hazard mitigation according to vulnerability and consequence, thereby maximising the cost/benefit of these investments. The authors have developed a fast-running analytical framework to evaluate steel-supported bridges for fire exposure. In this article, that computationally efficient tool is leveraged to conduct a large set of analyses over a complex highway interchange for a realistic and randomised series of fire scenarios. Fire hazard input parameters (e.g. combustion properties, spill geometry, etc) as well as the fire location within the interchange are randomly selected based on user-defined probability functions. Traffic data are used to determine which bridge spans may be most susceptible to fire and which pose the greatest consequences if they fail or lose functionality. The results of these analyses are used to develop a performance envelope that indicates the risk of fire-induced damage, as well as the effectiveness of potential fire protection strategies. [ABSTRACT FROM AUTHOR]

Truck weight data is needed for a wide range of applications including but not limited to pavement design, weight enforcement, traffic monitoring, and freight transportation planning. Unfortunately, the low spatial resolution of weight sensors along the transportation network can limit these and other potential applications. The main contribution of this paper is a methodology to estimate gross vehicle weight (GVW) distributions at traffic count sites, which collect traffic volumes but currently do not have the ability to directly measure vehicle weight. This paper presents a method for estimating GVW distributions of five-axle tractor-trailers ("3-S2") at traffic count sites by fuzing weight data from weigh-in-motion (WIM) sites with position data from global positioning system (GPS) equipped trucks. Truck travel patterns derived from a truck GPS database were used to determine the degree to which a WIM and traffic count site are spatially related. A GVW distribution was then estimated by combining Gaussian mixture models (GMM) estimated at WIM sites defined to be spatially related to the traffic count site. A leave-one-out cross validation framework allowed for comparisons of estimated and measured GVW distributions at each WIM site. Coincidence ratios and two-sample Kolmogorov-Smirnov (KS) tests were used as comparison metrics for a case study of 112 WIM sites in California. The proposed methodology provided better goodness-of-fit between observed and estimated GVW distributions compared to a baseline approach which defined the spatial relation between sites using great circle distances (GCD). [ABSTRACT FROM AUTHOR]

The tracking and behavior recognition of heavy-duty trucks on roadways are keys for the development of automated heavy-duty trucks and an advanced driver assistance system. The spatiotemporal information of trucks from trajectory tracking and motions learnt from behavior analysis can be employed to predict possible driving risks and generate safe motion to avoid roadway accidents. This article presents a unified tracking and behavior recognition algorithm that can model the mobility of heavy-duty trucks on long inclined roadways. Random noise within the sampled elevation data is addressed by time-based segmentation to extract time-continuous samples at geographical locations. A Kalman filter is first used to distinguish error offsets from random noise and to estimate the distribution of truck elevations for different time intervals. A Markov chain Monte Carlo model is then applied to classify truck behaviors based on the change in elevation between two geographical locations. A heavy-duty truck mobility (HVMove) model is constructed based on the map information to apply the roadway geometry to the tracking and behavior recognition algorithm. We develop an extended Metropolis–Hastings algorithm to tune the parameters of the HVMove model. The proposed model is verified and evaluated through extensive experiments based on a real-world trajectory dataset covering sections of an expressway and national and provincial highways. From the experimental results, we conclude that the HVMove model provides sufficient accuracy and efficiency for automated heavy-duty trucks and advanced driver assistance system applications. In addition, HVMove can generate maps with the elevation information marked automatically. [ABSTRACT FROM AUTHOR]