Truck Crashes Research Articles

Exploring truck driver-injury severity at intersections considering heterogeneity in latent classes: A case study of North Carolina

Abstract The fatal rate of truck-involved crashes is increasing and crashes become more severe than passenger vehicles in recent years. Much research has been dedicated to exploring the truck crash factors while scarce research focused on the intersection scenarios. This study investigates the factors that affect the severity level of truck-involved crashes at cross- and T-intersections. Due to the unobserved heterogeneity inherent in crash data, latent class analysis is firstly conducted to divide the crash dataset into relatively homogeneous clusters. Considering the ordinal feature of the severities, general ordered logit models are subsequently developed to further explore the specific factors within each cluster. This study uses the North Carolina’s truck-involved crash at intersection data during 2005 to 2017 from the Highway Safety Information System (HSIS). The estimated parameters and associated marginal effects are combined to interpret the impact of the significant variables within specific clusters. Many factors are found to contribute to the severities, and T-intersection is found to be safer than cross-intersection. For driving behaviors, followed too closely, disregarded signs, disregarded signals, failed to yield, and exceeded speed are found to be top five factors that increase the crash severity at intersections. These results indicate that distraction and speed limits violation always result in severe injury for humans involved in the truck crashes at the intersections. The results of this research provide more reliable analysis for the impact factors of truck-involved crashes at intersections to engineering practitioners and researchers.

Methods for Identifying Truck Crash Hotspots

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.

Analysis of Factors Contributing to the Severity of Large Truck Crashes

Crashes that involved large trucks often result in immense human, economic, and social losses. To prevent and mitigate severe large truck crashes, factors contributing to the severity of these crashes need to be identified before appropriate countermeasures can be explored. In this research, we applied three tree-based machine learning (ML) techniques, i.e., random forest (RF), gradient boost decision tree (GBDT), and adaptive boosting (AdaBoost), to analyze the factors contributing to the severity of large truck crashes. Besides, a mixed logit model was developed as a baseline model to compare with the factors identified by the ML models. The analysis was performed based on the crash data collected from the Texas Crash Records Information System (CRIS) from 2011 to 2015. The results of this research demonstrated that the GBDT model outperforms other ML methods in terms of its prediction accuracy and its capability in identifying more contributing factors that were also identified by the mixed logit model as significant factors. Besides, the GBDT method can effectively identify both categorical and numerical factors, and the directions and magnitudes of the impacts of the factors identified by the GBDT model are all reasonable and explainable. Among the identified factors, driving under the influence of drugs, alcohol, and fatigue are the most important factors contributing to the severity of large truck crashes. In addition, the exists of curbs and medians and lanes and shoulders with sufficient width can prevent severe large truck crashes.

Determinant of injury severities in large truck crashes: A weekly instability analysis

In the past, many attempts have been made to study the effects of large trucks on the safety of roadway users. However, the exact effects of weekdays and weekends on the injury-severity of these types of crashes are lacking. In this study, crash data from Los Angeles was used to investigate the transferability of the large-truck crash injury-severity determinants across weekdays and weekends. Crash-injury severities were estimated using random parameters logit models while considering three categories of injury-severity levels. The model estimation was done by considering several parameters that could potentially affect the crash injury severities such as truck’s characteristics, drivers’ attributes, driver actions, weather conditions, crash time, and roadway attributes. The transferability of the model estimation results across weekdays and weekends was assessed using likelihood ratio tests. Moreover, the stability of the explanatory variables was investigated using the calculated marginal effects. With a high confidence level, the estimation results disclose that weekday/weekend must be modeled separately as the contributing factors to each model are different. Still, some factors such as young drivers, at fault drivers, rear-end crashes, colliding fixed objects, intersection related crashes, and crash time are affecting injury severity in both models. The findings of this research could be used by trucking companies and decision makers to better regulate the traffic or trucking rules for weekdays and weekends.

JA:2021-33. Logging Truck Crashes in the SW Agricultural Region: Examining Severity and Associated Factors

ABSTRACT Purpose: The rate for fatal injuries in the agriculture, forestry, and fishing (AFF) sector far exceeds the rate for all sectors combined in the United States, based on the National Census of Fatal Occupational Injury (CFOI). Data from CFOI also illustrate that transportation crashes are among the top causes of fatal occupational injuries in the United States. However, little is known about transportation crashes involving AFF vehicles, specifically logging trucks in the southwest (SW) region (Arkansas, Louisiana, New Mexico, Oklahoma, and Texas). The purpose of the ongoing SW AgCrash project is to develop an integrated database of AFF crashes that occurred on roadways in the SW region. These data are being assessed to determine their usefulness for supporting injury surveillance and the identification of injury risk factors. Methods: Crash records covering approximately the period of 2010 to 2018 were obtained from the department of transportation in each state. Logging truck vehicles were identified based on coded data fields pertaining to vehicle and cargo body styles. Logging truck-related crashes were characterized over time using descriptive statistics and geospatial plots. Multiple and multinomial logistic regression is being used to identify factors associated with more versus less severe crashes in Texas. Results/Findings: Louisiana, followed by Texas, and Arkansas recorded the largest numbers of logging truck-related crashes overall, which corresponds to their greater logging activity. Crash rates per total vehicle miles travelled were highest in Arkansas and Louisiana. Based on preliminary analyses, factors associated with more severe logging truck related crashes in Texas included overturned vehicle, failure to yield right of way, lack of restraint, and speeding. Practical Application: Although crash records have limitations, they can be a valuable source of data for monitoring the frequency of crashes involving logging trucks, as well as for identifying potential risk factors and countermeasures. Products constructed through the SW AgCrash project support targeted outreach and education for reducing crash risk.

Uncovering Deep Structure of Determinants in Large Truck Fatal Crashes

The number of fatalities and severe injuries in large truck-related crashes has significantly increased since 2009. According to the safety experts, the recent increase in large truck-related crashes can be explained by the significant growth in freight tonnage all over the U.S. over the past few years. This notable freight-haul growth has allowed continuous day–night movement of freight on roads and highways, exposing the trucks to a greater number of potential crashes or near-crash scenarios. There are many ongoing research efforts that aim to identify the different factors that influence large truck crashes; however, further research with innovative approaches is still needed to better understand the relationship between crash-related factors. In this study, the project team applied taxicab correspondence analysis (TCA), a data-mining method known for dimension reduction, to large truck fatal crash data to investigate the complex interaction between multiple factors under a two-dimensional map. For this study, 6 years (2010–2015) of large truck fatal crash data from the Fatality Analysis Reporting System (FARS) were used. The study found five clusters of attributes that show patterns of association between different crash attributes such as two-lane undivided roadways, intersection types, posted speed limit, crash types, number of vehicles, driver impairment, and weather. The findings of this study will help the safety professionals, trucking industry, and policymakers to make decisions for safer road design, and improvement in truck driver training, and education.

Exploration of Hazardous Material Truck Crashes on Wyoming’s Interstate Roads using a Novel Hamiltonian Monte Carlo Markov Chain Bayesian Inference

Crash severity of a hazardous material (HAZMAT) transporting truck increases manyfold compared with normal truck crash because of the possible exposure to dangerous substances. Crashes which involve a HAZMAT truck might result in a catastrophic incident causing horrendous damage to individuals involved in the crash. In-transit HAZMAT crashes in Wyoming caused a total damage of $3.1 million from 2015 to 2018. HAZMAT crashes on interstate roads represented 22% of the total HAZMAT crashes causing a total damage of $2.2 million, representing 71% of the cost of total damage. Previous studies in Wyoming investigated all vehicle crashes, including large truck crashes, but none has analyzed HAZMAT-related crashes or accounted for its type as a contributing factor. This study fills the gap by analyzing crash injury severity of HAZMAT-related crashes on all interstate freeways in Wyoming. Furthermore, the study introduces the No-U-Turn (NUT) Hamiltonian Monte Carlo (HMC) method of hierarchical Bayesian analysis into HAZMAT crash injury severity analysis. In recent developments, NUT HMC has been proven to be the most efficient Markov Chain Monte Carlo (MCMC) sampling method. The results showed that 30% of the unobserved heterogeneity arises from variation in summer and winter crashes which justifies the use of hierarchical model. Among the other covariates investigated, the population-averaged effects showed that number of trucks involved, hit-and-run crashes, animal-vehicle crashes, work-zone-related crashes, collision type, percentage of females involved, drivers’ drug/alcohol use, seat-belt use, crash location, roadway curves, and surface conditions significantly impact HAZMAT crash injury severity.

Application of association rules mining algorithm for hazardous materials transportation crashes on expressway

Although crashes involving hazardous material (HAZMAT) vehicles on expressways do not occur frequently compared with other types of vehicles, the number of lives lost and social damage is very high when a HAZMAT vehicle-involved crash occurs. Therefore, it is essential to identify the leading causes of crashes involving HAZMAT vehicles and make specific countermeasures to improve the safety of expressways. This study aims to employ the association rules mining (ARM) approach to discover the contributory crash-risk factors of HAZMAT vehicle-involved crashes on expressways. A case study is conducted using crash data obtained from the Korea Expressway Corporation crash database from 2008 to 2017. ARM was conducted using the Apriori algorithm, and a total of 855 interesting rules were generated. With appropriate support, confidence, and lift values, we found hidden patterns in the HAZMAT crash characteristics. The results indicate that HAZMAT vehicle-involved crashes are highly associated with male drivers, single vehicle-involved crashes, clear weather conditions, daytime, and mainline segments. Also, we found that HAZMAT tank-lorry and cargo truck crashes, single vehicle-involved crashes, and crashes on mainline segments of expressways had independent and unique association rules. The finding from this study demonstrates that ARM is a plausible data mining technique that can be employed to draw relationships between HAZMAT vehicle-involved crashes and significant crash-risk factors, and has the potential of providing more easy-to-understand results and relevant insights for the safety improvement of expressways.

Identifying the Factors Contributing to the Severity of Truck-Involved Crashes in Shanghai River-Crossing Tunnel.

The impact that trucks have on crash severity has long been a concern in crash analysis literature. Furthermore, if a truck crash happens in a tunnel, this would result in more serious casualties due to closure and the complexity of the tunnel. However, no studies have been reported to analyze traffic crashes that happened in tunnels and develop crash databases and statistical models to explore the influence of contributing factors on tunnel truck crashes. This paper summarizes a study that aims to examine the impact of risk factors such as driver factor, environmental factor, vehicle factor, and tunnel factor on truck crashes injury propensity based on tunnel crashes data obtained from Shanghai, China. An ordered logit model was developed to analyze injury crashes and property damage only crashes. The driver factor, environmental factor, vehicle factor, and tunnel factor were explored to identify the relationship between these factors and crashes and the severity of crashes. Results show that increased injury severity is associated with driver factors, such as male drivers, older drivers, fatigue driving, drunkenness, safety belt used improperly, and unfamiliarity with vehicles. Late night (00:00–06:59) and afternoon rushing hours (16:30–18:59), weekdays, snow or icy road conditions, combination truck, overload, and single vehicle were also found to significantly increase the probability of injury severity. In addition, tunnel factors including two lanes, high speed limits (≥80 km/h), zone 3, extra-long tunnels (over 3000 m) are also significantly associated with a higher risk of severe injury. So, the gender, age of driver, mid-night to dawn and afternoon peak hours, weekdays, snowy or icy road conditions, the interior zone of a tunnel, the combination truck, overloaded trucks, and extra-long tunnels are associated with higher crash severity. Identification of these contributing factors for tunnel truck crashes can provide valuable information to help with new and improved tunnel safety control measures.

Statistical modeling of cargo tank truck crashes: Rollover and release of hazardous materials

Introduction: Cargo Tank Trucks (CTTs) are a primary surface transportation carrier of hazardous materials (hazmat) in the United States and CTT rollover crashes are the leading cause of injuries and fatalities from hazmat transportation incidents. CTTs are susceptible to rollover crashes because of their size, distribution of weight, a higher center of gravity, and the surging and sloshing of liquid cargo during transportation. This study identified and quantified the effects of various factors on the probability of rollover and release of hazmat in traffic crashes where a CTT was involved. Method: Bayesian Model Averaging (BMA)-based logistic regression models were estimated with rollover and hazmat release as the binary response variables, and crash, truck, roadway, environment, and driver characteristics as the explanatory variables. 2010–2016 police-reported CTT-involved crash data from Nebraska and Kansas was utilized. Receiver Operating Characteristic (ROC) curves confirmed appropriateness of the modeling approach for inference and prediction on the crash dataset. Results: CTTs are more likely to rollover in crashes while turning and changing lanes relative to going straight; side impacts (side collisions) and severe crosswinds increased the likelihood of rollovers; tractor and semi-trailer body style decreased the probability of rollover, while truck tractors are more prone to rollovers; collisions with fixed objects and higher posted speeds increased the rollover probability; rollovers and intersection crash locations increased the likelihood of hazmat release. Conclusions: The findings can assist stakeholders (policy-makers, private shippers, and CTT drivers) in restricting CTTs’ operations for safety; scheduling, routing, and fleet planning; and low-level decision-making (e.g., emergency stopping or local routing). Practical Applications: This study identified and quantified the effects of different factors on the conditional probability of rollover and release of hazmat in CTT-involved crashes. The findings may assist stakeholders in decision-making towards safe operations of CTTs for transportation of hazmat.

Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes

As two of the main modes of hazmat surface transportation, quantifying conditional probability of release of hazmat from trains in rail incidents and Cargo Tank Trucks (CTTs) in highway crashes is an important component of risk assessment. The objective of this study was identifying computational tools with reliable performance for quantifying probability of hazmat release in train incidents and CTT crashes, based on the type of the decision-making problem. Events of release of hazmat were probabilistically classified by statistical and machine learning methods (Mixed Logistic Regression, Naïve Bayes, Random Forests, and Support Vector Machines) using relevant explanatory variables. The datasets were Federal Railroad Administration rail equipment incident data, and combined Nebraska and Kansas police-reported traffic crash data. Given the rarity of these events, the classification performance of different methods was compared based on precision, recall and area under ROC curves (AUC). Random Forests had the best performance in classifying hazmat release for trains and railcars, based on different criteria. For CTTs, Support Vector Machines and Random Forests had the highest precision, while logistic regression and naïve Bayes performed better based on recall and AUC. The research provides recommendations regarding usage of the methods depending on the purpose of analysis.

Investigating the injury severity of single-vehicle truck crashes in a developing country

Trucking plays a vital role in economic development in every country, especially countries where it serves as the backbone of the economy. The fast growth of economy in Iran as a developing country has also been accompanied by an alarming situation in terms of fatalities in truck-involved crashes, among the drivers and passengers of the trucks as well as the other vehicles involved. Despite the sizable efforts to investigate the truck-involved crashes, very little is known about the safety of truck movements in developing countries, and about the single-truck crashes worldwide. Thus, this study aims to uncover significant factors associated with injury severities sustained by truck drivers in single-vehicle truck crashes in Iran. The explanatory factors tested in the models include the characteristics of drivers, vehicles, and roadways. A random threshold random parameters hierarchical ordered probit model is utilized to consider heterogeneity across observations. Several variables turned out to be significant in the model, including driver’s education, advanced braking system deployment, presence of curves on roadways, and high speed-limit. Using those results, we propose safety countermeasures in three categories of 1) educational, 2) technological, and 3) road engineering to mitigate the severity of single-vehicle truck crashes.

Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

This study aims to discover hidden patterns and potential relationships in risk factors in freight truck crash data. Existing studies mainly used parametric models to analyze the causes of freight vehicle crashes. However, predetermined assumptions and underlying relationships between independent and dependent variables have been cited as its limitations. To overcome these limitations and provide a better understanding of factors that lead to truck crashes on the expressways, we applied the Association Rules Mining (ARM) technique, which is a nonparametric method. ARM quantifies the interrelationships between the antecedents and consequents of truck-involved crashes and provides researchers with the most influential set of factors that leads to crashes. We utilized a freight vehicle-involved crash data consisting of 19,038 crashes that occurred on the Korean expressways from 2008 to 2017 for this investigation. From the data, 90,951 association rules were generated through ARM employing the Apriori algorithm. The lift values estimated by the Apriori algorithm showed the strength of association between risk factors, and based on the estimated lift values, we identified key crash contributory factors that lead to truck-involved crashes at various segment types, under different weather conditions, considering the driver’s age, crash type, driver’s faults, vehicle size, and roadway geometry type. From the generated rules, we demonstrated that overspeeding with medium-weight trucks was highly associated with crashes during the rainy weather, whereas drowsy driving during the evening was correlated with crashes during fine weather. Segment-related crashes were mainly associated with driver’s faults and roadway geometry. Our results present useful insights and suggestions that can be used by transport stakeholders, including policymakers and researchers, to create relevant policies that will help reduce freight truck crashes on the expressways.

Integrating GIS and statistical approaches to enhance allocation of highway patrol resources

Large truck crashes undermine the contribution of trucks to the U.S. economy due to the economic costs of the crashes. Wyoming has the highest truck crash rate and the lowest budget contribution for traffic enforcement in the USA. Because of the state’s intensive truck corridors, the Wyoming Highway Patrol (WHP) might not be able to use their resources efficiently. Previous studies have indicated that WHP performed better when they allocate their resources efficiently at the right locations and towards the right enforcements. This study used 4-year historical crash and enforcement data along Interstate 80 (I-80), which has the highest truck-related crash rate in Wyoming. Crash data were filtered to include truck crashes only. However, both truck and no-truck enforcements were included in the data because both could be at fault in truck crashes. This study used two approaches to help state policy-makers improve traffic safety on I-80. First, a statistical method was used to identify geometric variables contributing to allocated enforcement and truck crashes. Second, truck crashes and related enforcements were visually assessed using Geographical Information Systems (GIS) mapping. Crash data were disaggregated into the main driver actions of no improper driving, following too closely, improper lane change and driving too fast for the conditions. These driver actions accounted for more than 70% of all truck crashes on I-80. Related enforcements were also identified and disaggregated by driver actions. Disaggregated enforcements and crashes were visualized along the I-80 corridor using GIS maps to see if WHP allocated their resources efficiently. Cluster index, enforcement spatial coverage and mean density are some of the parameters used for the analyses. This study aimed to contribute to research on police effectiveness in reducing truck crashes, police innovation and the use of GIS applications in enforcement. This methodology can be used by other agencies to better allocate resources to improve traffic safety in most efficient ways.

Predicting injury severity and crash frequency: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Abstract Road deaths, injuries and property damage places a huge burden on the economy of most nations. Wyoming has one of the highest truck-related fatality rates among the states in the US. The high crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on truck drivers in terms of requiring higher levels of alertness and driving skills. The difficult geometry of roads characteristic of mountainous terrain in terms of steep grade lengths adds extra risks of fatalities or injuries occurring as a result of a crash. These risks are more pronounced for truck-related crashes due to their weight and sizes. As part of the measures to reduce the incidence of truck-related crashes on mountainous areas, the Wyoming Department of Transportation (WYDOT) initiated a study to investigate causes of truck crashes on downgrade areas of Wyoming. Several studies have investigated the contributory factors to severe injury crashes but the focus has mostly been on level sections. This study analyzed the contributory geometric factors of truck crashes on downgrades by estimating three crash prediction negative binomial models. These models took into account the injury severity of the crashes. The results indicate that downgrade length, shoulder width, horizontal curve length, number of lanes, number of access points and truck traffic on the highway all impact truck-related crashes and injury frequencies on downgrades in Wyoming. The results of this study will be helpful to future downgrade road design policy aimed at reducing downgrade truck related crashes.

Investigating the relationship between crash severity, traffic barrier type, and vehicle type in crashes involving traffic barrier

Abstract Roadside safety is one of the important components of highway systems due to its considerable rate of high-severity crashes. Traffic barriers play a key role in reducing the crash severity and saving more lives on roadsides but choosing an inappropriate traffic barrier could decrease traffic barriers’ efficiency. This paper investigated the variables affecting crash severity in different traffic barrier types (cable, guardrail, and rigid) and vehicle types (truck, and non-truck). For this purpose, an ordinal logistic regression approach was conducted on the crash data collected between 2007 and 2016 in Wyoming. According to the results, different traffic barrier types show different relationships to crash severity based on posted speed limit at the site. In guardrail segments, a high-severity crash is more likely when the posted speed limit is more than 55 mph; however, cable barrier crashes were less severe in high-speed limit (>55 mph) areas. While light vehicles were more vulnerable to crashes hitting rigid barriers, no significant difference was found between performances of cable and rigid barriers in terms of truck crash severity. The analytical models proposed in this study would make a clear view for designers and decision-makers in selecting the most appropriate traffic barrier. Also, the effective factors introduced in each model could be used to rank the risk posed by existing traffic barrier segments based on traffic barrier type, truck volume percentage, and other highway characteristics.

Injury Severity and Contributing Driver Actions in Passenger Vehicle-Truck Collisions.

Large-scale truck-involved crashes attract great attention due to their increasingly severe injuries. The majority of those crashes are passenger vehicle–truck collisions. This study intends to investigate the critical relationship between truck/passenger vehicle driver’s intentional or unintentional actions and the associated injury severity in passenger vehicle–truck crashes. A random-parameter model was developed to estimate the complicated associations between the risk factors and injury severity by using a comprehensive Virginia crash dataset. The model explored the unobserved heterogeneity while controlling for the driver, vehicle, and roadway factors. Compared with truck passengers, occupants in passenger vehicles are six times and ten times more likely to suffer minor injuries and serious/fatal injuries, respectively. Importantly, regardless of whether passenger vehicle drivers undertook intentional or unintentional actions, the crashes are more likely to associate with more severe injury outcomes. In addition, crashes occurring late at night and in early mornings are often correlated with more severe injuries. Such associations between explanatory factors and injury severity are found to vary across the passenger vehicle–truck crashes, and such significant variations of estimated parameters further confirmed the validity of applying the random-parameter model. More implications based on the results and suggestions in terms of safe driving are discussed.

ACTIVATED CARBON BASED SOLUTIONS FOR SPILLS IN REMOTE AND DIFFICULT AREAS

INTRODUCTION Environmental calamities such as tanker truck crashes and pipeline leaks do not always occur in well populated areas. Tanker truck crashes can occur on remote mountain passes or lonely stretches of highway. Pipeline leaks can occur in distant areas of open range or other isolated locations. Treatment of these spills requires the ability to clean up the contamination with very few standard resources. Often these sites will not have access to electricity or cellular service and just getting to the location of the spill can require a long drive from the nearest town. These restrictions can often limit treatment options to those that can be completed quickly and without needing long term access to the site. The remediation of petroleum fuels spilled during several tanker truck roll-overs will be discussed below, along with the obstacles presented by each site and the innovations needed to complete the remediation.

Analysis on the Characteristics and Management Countermeasures of Truck Crashes on Zhejiang Expressways

Highway freight transport plays a very important role in China’s transportation industry. Therefore, the characteristics and laws of highway cashes are analyzed, and targeted preventive measures and suggestions for improvement are put forward, which can be very good guidance for highway management in other regions.

Rare but conceivable: Determining the likelihood of meteors and other infrequent events

AbstractIn the aftermath of a rare but devastating disaster, there is a tendency to overestimate how frequent such an event may be, hence a tendency to overestimate the risk associated with such an event. Those concerned with process safety will acknowledge that though rare, events like meteors, hurricanes, and airplane crashes that can impact chemical plants are conceivable. Typically, though, they have nothing more than feelings to decide whether these events are likely enough to be considered “credible.” This compiles data from the literature for several “meteors,” both natural and man‐made, that often become a cause of general concern after such an event occurs: meteors, earthquakes, and tsunamis; tornadoes and hurricanes; and airplane crashes, pipeline ruptures, train derailments, and truck crashes. It provides data and describes approaches to estimating the frequency of such events at a specific facility based on its location. With these frequencies, process risk analysts can then determine which, if any, of these events is sufficiently likely to warrant further attention.