Occurrence Of Collisions Research Articles

Effects of Inclement Weather Events on Road Surface Conditions and Traffic Safety: An Event-Based Empirical Analysis Framework

Pavement friction has been widely recognized as an important winter road maintenance performance indicator for objectively assessing the level of service required to maintain safe driving conditions during inclement weather events. Despite significant efforts being put forth by road agencies, prevailing road surface conditions during snowstorms can yield negative consequences that compromise the safety of the traveling public. Acknowledging the vast road network that needs to be monitored and the uncertainty associated with the randomness of hazardous road weather conditions, this paper presents a novel event-based framework aimed at investigating the magnitude of the effect of varying pavement friction levels in urban environments on traffic safety during snowstorms. Negative binomial safety performance functions developed using hourly weather datasets and road surface conditions information found a strong statistically significant relationship between pavement friction and traffic safety. This meant that, with the accumulation of snow and ice during snowstorms, road surface conditions were found to deteriorate thereby increasing the likelihood of collision occurrence. The event-based models developed also suggested that the risks of driving during snowstorms varied dramatically depending on the varying surface states, represented by friction coefficient ranges. Collisions were expected to significantly decrease whenever the pavement friction was above 0.6; under conditions where pavement friction was below 0.35, collisions were predicted to significantly increase. Additionally, arterial roads were shown to experience a significantly higher number of collisions than collectors, further justifying why arterials should be prioritized in snow clearing policies that most cities adopt.

Ship Strike Risk for Fin Whales (Balaenoptera physalus) Off the Garraf coast, Northwest Mediterranean Sea

Ship strikes are a widespread conservation issue for many cetacean species globally. Population level impacts depend on the occurrence and severity of collisions, which may lead to life altering injuries or fatalities. Such impacts are a major concern for large, long-lived, and reproductively slow species like the fin whale. Since 2014, a seasonal feeding aggregation of fin whales has been monitored from February to June off the Catalan coast (Spain), in the northwest Mediterranean Sea. Oceanographical factors influence the occurrence and high density of krill within submarine canyons along the continental shelf, resulting in high whale abundance within a small spatial area. The study area extends 37 km offshore across a 1,944 km2marine strip situated between the towns of Torredembarra and Castelldefels. This fin whale feeding ground is exposed to high density marine vessel traffic, given its location between the northern Mediterranean shipping lane, which links Barcelona and Tarragona Ports to the Atlantic Ocean and wider Mediterranean Basin. Ship strikes represent the greatest conservation threat for fin whales in the Mediterranean Sea. At least four fin whales have been found dead in Barcelona Port since 1986 due to ship strikes and seven live whales have been documented with injuries in the study area since 2018. Fin whale distribution was mapped with known high-risk marine vessels’ (cargo, tanker and passenger vessels) shipping lanes. Vessel density and shipping lanes characterised by speed were considered. Collision risk was estimated monthly based on the predicted fin whale occurrence and traffic density. Several shipping lanes crossed the fin whale feeding habitat every month with an average speed of 15 kn. Cargo vessels displayed the highest ship-strike risk during April, overlapping with the peak of fin whale sightings in the critical feeding area. Slower vessel speeds (8 kn) in waters <200 m depth or along the continental shelf should be implemented along the Catalan coast, during the whale season. These suggestions should be applied into the Barcelona Port transport separation scheme. Ship strike risk for this species will persist unless active management plans are adapted in the region to mitigate its risk.

Concept of Center Pillarless Car Body and Dual Sliding Door

<div class="section abstract"><div class="htmlview paragraph">To prepare for the future car market, concept cars based on eco-friendly vehicles with autonomous driving systems are being developed, such as BEV (battery electric vehicle) and HFCEV (hydrogen fuel cell electric vehicle). These concept cars adopt a new body structure to improve passenger convenience, and the CTR-PLRless (center pillarless) body structure is one of the key structures that can improve the passenger convenience. However, the CTR-PLRless body structure has a disadvantage in that it cannot connect the load between the upper parts and the lower parts of the body structure, as well as that it provides limited protection to the passengers from the occurrence of a side collision because the main members on the side of the body are removed. Therefore, the aim of this paper is to introduce a door opening and closing mechanism and body structure design concept that applies the CTR-PLRless structure to general CUV vehicles, and to suggest a door reinforcement method using AHSS (advanced high strength steel). The door opening and closing mechanism was implemented as a dual sliding method, and the performance verification of the door reinforcement structure was performed with stiffness analysis and crash analysis. The removed CTR-PLR was modified and fastened to the door to reinforce the door, and it was developed with the concept of minimizing deformation by applying hot-stamping process with PHS (press hardened steel).</div></div>

What leads to severe multi-vehicle crashes on mountainous expressways in Western China?

This paper investigates the occurrence and severity of collisions involving multiple vehicles on mountain expressways (MMEs) in Western China. A total of 1 521 crash samples occurring on one typical mountain expressway in Shaanxi, China, between 2012 and 2017, were analysed through a partially constrained generalised ordered logit to identify the significant risk factors contributing to the severity of such crashes. Elasticity analysis was performed to quantify the effects of each independent explanatory variable on the collision severity outcomes. Fourteen total explanatory variables were found to have a significant and pronounced influence on the likelihood of MME crashes. These include the type of collision, the at-fault driver's age, driving while fatigued, cell phone use while driving, alcohol-impaired driving, speeding, risky following and dangerous overtaking behaviour, sharp curves in the roadway and slippery pavement conditions, seasons, day of the week, time of day, and adverse weather (rain/snow/fog). The impacts of the variables on the collision severity were also explored. Taken together, the findings may serve as a useful guide for developing legislation and technical countermeasures to ensure traffic safety on mountain expressways in Western China.

Analysis of Driver Behavior and Intervehicular Collision: A Data-Based Traffic Modeling and Simulation Approach

The emergence of intelligent connected vehicles (ICVs) is expected to contribute to resolving traffic congestion and safety problems; however, it is inevitable that ICV safety issues in mixed traffic (involving ICVs and human driven vehicles) will be a critical challenge. The numerical simulation of scenarios involving a mix of different driving profiles is expected to be an important safety assessment tool in the process of testing and validating ICVs, especially regarding extreme scenarios, including car collisions, which are rarely captured in real-world datasets. In this study, we propose a novel approach for car collision generation in numerical simulations based on the assumption that car collision occurrences are mostly associated with certain specific driver profiles. Using a dataset provided by the Next Generation Simulation (NGSIM) project, NGSIM 101 dataset, we identify three different driver profiles: aggressive, inattentive, and normal drivers. We then replicate car collision occurrences by varying the percentages of these three driver profiles in the simulated environment, allowing us to establish a relationship between driver profiles and car collision occurrences. We also investigate the severity of car collisions and classify them with respect to the driver profiles of the cars involved in the collisions. Our approach of replicating car collision occurrences in numerical simulations will facilitate the testing and validation of ICVs in the future, especially regarding the testing of ICV functionalities in dealing with traffic accidents.

Modelling injury severity of victims in collisions involving public transit in Dhaka, Bangladesh

Collisions involving public transit (PT) have increased significantly in the recent times. In developing countries, the scenario is much worse as the PT collision occurrence and injury severity are higher by manifold. This study investigates the injury severity of victims of PT involved crashes in Dhaka, Bangladesh. Victims include PT occupants and counterpart road users such as pedestrians who are more vulnerable. A latent segmentation-based ordered logit (LSOL) model is developed using police-reported crash records of Dhaka. The LSOL model addresses the ordinality of injury severity and captures unobserved heterogeneity by allocating victims into discrete latent segments. The LSOL model is estimated for two segments: segment two is high-risk segment which includes unconventional vehicle occupants and pedestrians, whereas segment one is low-risk segment. The injury severity model results suggest that person profile, environmental factors and built environment characteristics significantly influence injury severity. For example, female and elderly victims have higher likelihood to sustain severe injury. The model confirms the existence of significant heterogeneity. For instance, mid-block collisions are more likely to yield severe injury in the high-risk segment, and less likely to result in severe injury in the low-risk segment. Similar heterogeneity is confirmed for variables representing impaired driver, three-way intersection, and police-controlled intersection. The elasticity effect analysis suggests that female victims, younger and adult victims, unconventional vehicle occupants, pedestrians, and mid-block crashes have substantial positive impact on fatality. The identified influential factors and heterogeneity captured in the study need to be accommodated in road safety plans and policies.

A study on resonant collision in the two-dimensional multi-component long-wave–short-wave resonance system

This study focuses on the occurrence and dynamics of resonant collisions of breathers among themselves and with rational solitary waves in a two-dimensional multi-component long-wave–short-wave resonance system. These nonlinear coherent structures are described by a family of semi-rational solutions encompassing almost all known solitary waves with non-zero boundary conditions. In the case of resonant collision of parallel breathers, the original breathers split up into several new breathers or combine into a new breather. They also exhibit a mixed behaviour displaying a mixture of splitting and recombination processes. The lumps and line rogue waves—two distinct forms of rational solitary waves—can undergo partial and complete resonant collisions with breathers. In the partial resonant collisions, the lumps do not exit well before interaction but suddenly emerge from the breathers and finally keep existing for an infinite time after interaction, or they alter from existence to annihilation as one moves fromt→−∞tot→+∞. The line rogue waves arise and decay along ray waves instead of line waves. In the complete resonant collisions, the lumps first detach from breathers and then fuse into other breathers, while the line rogue waves appear and disappear along line segment waves of finite length, namely, both of the lumps and rogue waves are doubly localized in two-dimensional space as well as in time.

An Approach for Intersection Prevention in Transmission Channel Using Correlation Matrix and Tree Construction Model in V2X Framework

Typically, the delivery of information in vehicular ad hoc networks (VANETs) is regarded as a demanding one because of high mobility and invariable topological difference. One major concern in VANET framework is the occurrence of simulated intersection at which the packet reception probability become complicated for predicting and the model turn out to be complex highly. So as to overcome this dispute, a new framework is introduced which in turn prevents the intersection among the transmission path. Initially, the system model is initialized and the availability of channel is estimated. Based on the strength and size of data to be transmitted, the availability of channel is computed. After that, the best forwarding zone computation is made for minimizing the redundant data packets flow. To check the priority of packet and occurrence of packet collision, tree construction based data strength transmission is employed at last, the prevention of intersection or collision between the transmission channel is checked by means of correlation matrix based intersection prevention approach by monitoring the neighbor node. Then, the data packets were forwarded in an efficient manner without any intersection between the frames. The performance analysis is estimated in terms of network lifetime, packet delivery ratio, packet collision; inter node collision, throughput, and end-to-end delay.

Modelling the Relative Abundance of Roe Deer (Capreolus capreolus L.) along a Climate and Land-Use Gradient.

Simple SummaryThe European roe deer is the most abundant ungulate in Europe. Information on the number of animals and the factors that influence this are essential for the effective management of this species. We developed a model to estimate the pellet group density using transect surveys of dung pellets in the federal state of Bavaria, south-eastern Germany. We used the pellet group density as a proxy for roe deer relative abundance. Our results show that climate, habitat type and wildlife management approach determine the relative number of roe deer. Influential factors differed between seasons and were expected due to changes in food and shelter availability. Although recognized as a woodland species, the majority of roe deer are expected in agriculture-dominated landscapes, which shows their ability to adapt to a wide range of landscape types, especially those with high food availability. Higher numbers were also predicted in regions with intermediate temperatures. Estimates of relative number do not give the actual absolute number of animals but are useful in determining which conditions will have more or fewer animals and can provide information for broad-scale management recommendations. Our results also provide insights into possible future changes in the distribution of relative numbers of roe deer due to climate and land-use change.European roe deer (Capreolus capreolus L.) are important given their economic, recreational and ecological value. However, uncontrolled roe deer numbers can result in negative impacts on forest regeneration and agricultural crops, disease transmission and occurrences of deer-vehicle collisions. Information on the abundance and distribution is needed for effective management. We combined distance sampling (DS) of roe deer dung pellet groups with multiple variables to develop a density surface model (DSM) in the federal state of Bavaria in south-eastern Germany. We used the estimates of pellet group density as a proxy for roe deer relative abundance. We extrapolated our best DSM, conducted a quantitative evaluation and contrasted relative abundance along climate and land-use gradients. Relative abundance of roe deer was influenced by a combination of habitat type, climate and wildlife management variables, which differed between seasons and which reflected changes in food and shelter availability. At the landscape scale, the highest abundance was observed in agriculture-dominated areas and the lowest in urban areas. Higher abundance was also observed in areas with intermediate temperatures compared to the warmest areas. Our results provide information on possible future changes in the distribution of relative abundance due to changes in climate and land-use.

Charge-Exchange Byproduct Cold Protons in the Earth’s Magnetosphere

Owing to the spatial overlap of the ion plasma sheet (ring current) with the Earth’s neutral-hydrogen geocorona, there is a significant rate of occurrence of charge-exchange collisions in the dipolar portion of the Earth’s magnetosphere. During a charge-exchange collision between an energetic proton and a low-energy hydrogen atom, a low-energy proton is produced. These “byproduct” cold protons are trapped in the Earth’s magnetic field where they advect via E×B drift. In this report, the number density and behavior of this cold-proton population are assessed. Estimates of the rate of production of byproduct cold protons from charge exchange are in the vicinity of 1.14 cm−3 per day at geosynchronous orbit or about 5 tons per day for the entire dipolar magnetosphere. The production rate of cold protons owing to electron-impact ionization of the geocorona by the electron plasma sheet at geosynchronous orbit is about 12% of the charge-exchange production rate, but the production rate by solar photoionization of the neutral geocorona is comparable or larger than the charge-exchange production rate. The byproduct-ion production rates are smaller than observed early time refilling rates for the outer plasmasphere. Numerical simulations of the production and transport of cold charge-exchange byproduct protons find that they have very low densities on the nightside of geosynchronous orbit, and they can have densities of 0.2–0.3 cm−3 at geosynchronous orbit on the dayside. These dayside byproduct-proton densities might play a role in shortening the early phase of plasmaspheric refilling.

Pothole and Plain Road Classification Using Adaptive Mutation Dipper Throated Optimization and Transfer Learning for Self Driving Cars

Self-driving car plays a crucial role in implementing traffic intelligence. Road smoothness in front of self-driving cars has a significant impact on the car’s driving safety and comfort. Having potholes on the road may lead to several problems, including car damage and the occurrence of collisions. Therefore, self-driving cars should be able to change their driving behavior based on the real-time detection of road potholes. Various methods are followed to address this problem, including reporting to authorities, employing vibration-based sensors, and 3D laser imaging. However, limitations, such as expensive setup costs and the danger of discovery, affected these methods. Therefore, it is necessary to automate the process of potholes identification with sufficient precision and speed. A novel method based on adaptive mutation and dipper throated optimization (AMDTO) for feature selection and optimization of the random forest (RF) classifier is presented in this paper. In addition, we propose a new adaptive method for dataset balancing, referred to as optimized hashing SMOTE, to boost the performance of the optimized model. Data on potholes in different weather conditions and circumstances were collected and augmented before training the proposed model. The effectiveness of the proposed method is shown in experiments in classifying road potholes accurately. Eleven feature selection methods, including WOA, GWO, and PSO, and three machine learning classifiers were included in the conducted experiments to measure the superiority of the proposed method. The proposed method, AMDTO+RF, achieved a pothole classification accuracy of (99.795%), which outperforms the accuracy achieved by the other approaches, WOA+RF of 97.5%, GWO+RF of 98.6%, PSO+RF of 98.1%, and transfer learning approaches, AlexNet of 86.8%, VGG-19 of 87.3%, GoogLeNet of 90.4%, and ResNet-50 of 93.8%. In addition, an in-depth statistical analysis is performed on the recorded results to study the significance and stability of the proposed method.

東京湾におけるAIS非搭載船舶の動静解析と衝突原因確率の推定

To reduce the number of collisions between ships, it is useful to estimate areas where collisions between ships are likely to occur. In recent years, many studies on the estimation have been conducted using Automatic Identification System (AIS) data, which makes it relatively easy to analyze ship movements. However, accidents involving non-AIS ships occur frequently, and thus their movements should be included to estimate the areas. In this study, we analyzed the movements of non-AIS ships in Tokyo Bay using data observed by radars, which are installed on shore around Tokyo Bay. Additionally, we estimated collision caution probability, which is one of the indexes representing the likelihood of a collision occurrence, for ships including non-AIS ships. The results were compared with the probability based only on AIS data and conventional estimates, and trends were clarified corresponding to current traffic situations.

Influences of dynamic safe headway on car-following behavior

In car following states, the reaction of drivers is closely related to the space headway at given speed difference. Unlike the constant velocity difference reaction coefficient in traditional traffic model, this work designed a dynamic function, which can realize the switch between models according to the dynamic headway between vehicles, and developed a new car-following model to investigate the influences of dynamic safe headway (DSH) on car-following behavior. First, the following behavior of vehicle with DSH under urgent situation is simulated to demonstrate that DSH has the ability to prevent the occurrence of collision. Then, the prediction of delay time of car motion and the kinematic wave speed is reasonable and the evolution from free flow to congested flow is well reproduced. In addition, the result of excitation simulation shows that DSH can absorb the disturbance and suppress the propagation of traffic oscillations effectively, which is hard to achieve for traditional traffic model. Finally, the numerical experiment under period condition demonstrate that the new model is conducive to improving driving performance and further to smooth traffic flow. In addition, calibration and validation results show that the empirical data can be modeled with a quantitative agreement.

Examining non-linear built environment effects on injurious traffic collisions: A gradient boosting decision tree analysis

IntroductionDuring rapid urbanization, the optimization of a built environment (BE) in metropolises to reduce the frequency of traffic collisions can considerably promote traffic safety. Previous studies have seldom discussed the non-linearity and threshold effects of the BE on injurious collisions, and they propose few evidence-based synergistic effects for policy development. MethodsWith the injurious collision frequency as the dependent variable, this study established a BE indicator system including travel demand, roadway design, non-motorized infrastructure, land use, traffic control and disadvantage neighborhoods as explanatory variables. The gradient boosting decision tree (GBDT) model based on machine learning was used to explore the relative importance and the non-linearity of each variable. Furthermore, bivariate partial dependence plots were applied to reflect the synergistic effects of intersection density and other high-contribution BE variables on injurious collision frequency. Results(1) The riding volume, intersection density, and distance from CBDs are the top three contributing variables, which should attract more attention when planning. (2) Several thresholds were found: controling the width of arterial roads within 25 m, limiting the average vehicle speed in 36–38 km/h, setting subway stations within 1 km away from key city locations, adjusting the land use mix below 0.6, regulating the density of floating population around 3000 people/km2and setting the greenway density at 2 km/km2are all beneficial to reduce the injurious collision frequency. (3) Retaining the above thresholds from univariate PDPs, the synergistic effects not only causes high collision frequency, but also improves the risk growth rate. ConclusionAiming at the variables with high contribution rates and exerting these threshold effects in transportation plans will effectively reduce the occurrence of injurious collisions.

Raindrop size distribution characteristic differences during the dry and wet seasons in South China

Raindrop size distribution (DSD) is a basic feature of precipitation microphysics, and it plays a vital role in improving radar quantitative precipitation estimation and forecast accuracy. In South China, due to the influence of atmospheric circulation and weather systems, precipitation is frequent, and the types of precipitation and their temporal and spatial distributions are complex. It has become essential to study the types of precipitation in this area and their seasonal variation characteristics. Therefore, in this study, the DSDs in the dry and wet seasons of southern China are analyzed using measurements from seven ground-based two-dimensional video disdrometers (2DVDs) over the course of four years (2016–2019). The dynamic and microphysical characteristics of precipitation in the dry and wet seasons are established for South China, combined with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth-generation Reanalysis (ERA5) data. Compared to the dry season results, the concentration of small raindrops (diameters <1 mm) in wet season is similar, while the concentration of midsized raindrops (diameters between 1 and 3 mm) and large raindrops (diameters >3 mm) in the wet season are significantly greater. Furthermore, taking the contributions to precipitation into account, the rainfall in the dry season is dominated by stratiform rain, which accounts for 59.3% of the total rainfall. Considering precipitation in the wet season, convective rain accounts for 77.9% of total the rainfall. The obtained relations of the shape parameter (μ) versus the slope parameter (Λ) (μ-Λ) and the radar reflectivity factor (Z) versus the rainfall rate (R) (Z-R) have obvious differences for different type rainfall in the two seasons. In the wet season, the water vapor is sufficient and the upward convection activity is intense in South China, causing the frequent occurrence of raindrop collisions and coalescence. This is also the reason that the Dm and R values in the wet season are greater than those in the dry season, while the wet season log10Nw is relatively small. This not only deepens the understanding of the seasonal DSD variation in South China, but also provides theoretical support for improving numerical weather prediction models.

The effects of road safety education on the occurrence of motorcycle violations and accidents for novice riders: An analysis of population-based data

The prevalence of motorcycle riding among novice riders in most Southeast Asia countries presents an alarming rate of traffic violations and fatal accidents. Since 2013, Taiwan's government has gradually required a road safety class (RSC) for the rider's licensing process. The RSC consisted of watching videotapes of motorcycle-involved crashes followed by lectures on safety measures. Our study tried to see whether a compulsory RSC could lower the likelihood and frequency of road accidents and traffic violations among novice riders. To avoid self-selection bias, we selected 480,114 novice riders aged 18–20 years, licensed one year before starting the trial period and one year after full implementation of RSC. Using the 2012–2018 data from the Taiwan Ministry of Transportation and Communication (MOTC), we applied the logistic model to evaluate RSC effects on the risk of violations and accidents. Then, we used the negative binomial regression to model their frequency in response to RSC exposure. Following the novice drivers 1–3 years after licensing, our results showed that the RSC has a short-term effect in lowering their traffic violations' likelihood by 12%∼17% and their frequency by 11%; however, the RSC effects only last two years in reducing the counts of motorcycle-involved offenses and accidents. The RSC reduction effect was lower for the tendency of accidents than the violations, probably because committing traffic violations was self-determined; in contrast, the collision occurrence was more or less related to the riders' own or other road users' carelessness. The RSC could be more effective if a certification test for road safety education were required or if a penalty is imposed on distracted learners during the training.

Exploring the associations between winter maintenance operations, weather variables, surface condition, and road safety: A path analysis approach

This paper aims to assess the effects of snow and ice control operations by investigating the interdependency between weather variables, maintenance operations, pavement friction, and collisions. Using a disaggregated event-based and location-specific framework, and employing the statistical techniques of Structural Equation Modeling and Path Analysis, all the significant direct and indirect effects of weather variables and maintenance operations on pavement friction and collision occurrence during snowstorms have been identified. It was revealed that precipitation, extremely low temperatures, and the potential of black ice formation all had significant negative direct effects on pavement friction and significant indirect negative effects on traffic safety. Moreover, the application of anti-icing agents and plowing operations have been shown to significantly improve pavement friction and in return improve traffic safety indirectly. To illustrate how the maintenance operations improve traffic safety, a hypothetical snowstorm example was considered. According to the model, anti-icing application was associated with a 14% reduction in collisions, plowing operations resulted in a 33% reduction in collisions, and combining the two tools reduced collisions per snowstorm by 42%. The findings of this paper can help transportation agencies make more informed decisions to promote an efficient mobilization of the existing winter road maintenance services and resources while improving the safety of the traveling public during the winter months.

Probability-based extensive quantitative risk analysis: collision and grounding case studies for bulk carrier and general cargo ships

ABSTRACT In this study, the risk of collision and grounding accidents on bulk carrier and general cargo ships was investigated. A comprehensive risk analysis consisting of past accident reports, fault tree analysis (FTA) and Cut sets importance measure (CS-I) methods was carried out. The failures effective in the occurrence of collision and grounding accidents were determined by examining past accident reports. In the study, the probability for each failure was derived by using real accident data. Using FTA, various potential accident occurrence combinations/scenarios, also known as minimal cut sets (MCSs), for both accident types were obtained. In addition, prioritization was conducted for the MCSs of both types of accidents using CS-I sensitivity analyses. The faults that increase the risk of collision accidents the most were found to be interpretation failure of the Convention on the International Regulations for Preventing Collisions (COLREG) and lack of situational awareness. In addition, the top failures that lead to groundings were determined to be lack of communication in bridge resource management (BRM), fatigue/sleeplessness and inappropriate voyage plan. Recommendations are made for eliminating potential risk factors, preventing the recurrence of such accidents and establishing safer maritime transport.

Predicting the Collisions of Heavy Vehicle Drivers in Iran by Investigating the Effective Human Factors

Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.

Dynamic derailment behaviour of urban tram subjected to lateral collision

ABSTRACT The urban tram is theoretically likely to be laterally collided by the automobile during operation due to their shared right of roadway. The collision occurrence is fortuitous, but the consequence is commonly disastrous induced by the collision and subsequent derailment accidents. This article presents an investigation into the derailment performance of the tram when the automobile impacts laterally onto the tram flank. A comprehensive dynamic model to simulate derailment behaviours of the tram under this collision scenario is built, in which the complicated contact relation of wheel/rail system is treated. The contribution of several affecting factorsto the derailment response is reported. It is shown that the guardrails of the grooved rail track play a key role in preventing the tram derailments due to low-speed collision. The violent lateral and rolling motions of the tram body caused by the great lateral impact is the root cause of the derailments.