Truck-involved Crashes Research Articles

Investigating the impact of fatigue level on visual attention of heavy-duty truck driver: An eye tracking study

Fatigue driving is considered one of the main risk factors leading to truck-involved crashes. A significant correlation between fatigue driving and visual behavior has been established in previous studies. However, the relationship between visual behavior and fatigue levels, especially among truck drivers, has been seldom explored. Therefore, this study conducted a field driving test on heavy-duty truck drivers to assess their visual attention distribution when affected by fatigue. Fatigue levels were measured based on the Percentage of Eye Closure (PERCLOS), and the impact on eye movement was evaluated. The study found that an increase in PERCLOS led to a decrease in the percentage of glances toward the left-wing mirror and the right-hand side of the windshield. Similarly, the number of glances toward the left-hand side of the windshield and left-wing mirror also decreased with the increase in PERCLOS. These findings suggest that a higher level of fatigue results in reduced attention toward the left-wing mirror and windshield, with truck drivers’ focus remaining at the front for longer periods, indicating a reduced alertness to road conditions. These insights could be valuable in monitoring fatigued drivers by examining the visual attention of heavy-duty truck drivers.

Safety evaluation for regulation changes on commercial vehicle operation using multilevel Bayesian methods

Truck-involved crashes cause serious social and economic losses, and the crash severity is higher compared to ordinary vehicles. Driver fatigue is a major crash causation, especially have a larger impact on truck drivers. Therefore, mandating a truck rest-break is one of the road safety management strategies for reducing both truck-involved crash frequency and severity. This paper analyses the change in safety performance according to the revision of the truck rest-break time on highways. Because spatial heterogeneity and homogeneity should be considered to estimate safety effects precisely after executing crash countermeasures, this study estimates the safety effect after the revision of the Trucking Transport Business Act, by developing a model based on the Multilevel Full-Bayes before and after the study. The safety performance function was developed in different section units and distributions, and the crash modification factor was calculated for the most suitable model. As a result of the analysis, the performance of the Multilevel Bayesian Poisson-gamma model was the highest, and it was found that there was a crash reduction effect after the revision of the truck break time. The results of this study can be referred to when preparing measures for road safety regulations.

Modeling of single-vehicle and multi-vehicle truck-involved crashes injury severities: A comparative and temporal analysis in a developing country

Truck-involved crashes persist as a significant concern, yielding noteworthy human casualties and causing economic ramifications, particularly in developing countries. This paper aims to undertake a comprehensive analysis of the associated factors influencing injury severity in truck-involved crashes, with a particular emphasis on discerning variations between single-vehicle and multi-vehicle incidents, as well as accounting for heterogeneity and temporal stability. The data analysis involves a meticulous examination of crash data spanning the entirety of Thailand from 2017 to 2020. Employing three distinct levels of injury severities, namely PDO injury, moderate injury, and severe injury, the study employs a series of mixed logit models that account for unobserved heterogeneity in both means and variances. Results revealed significant instability in injury risk determinants over time among both single and multi-vehicle events. Aligning predictive assessments further spotlighted fluctuations in projected burdens across models and years – collectively underscoring the imperative to integrate temporal considerations into modeling and prevention. Several crash-type distinctions and priorities emerged. For single-truck events, key risks included roadway alignments and geometry, speeding, fatigue, and lighting conditions. However multi-truck collisions concentrated around exposure factors like highway traits, sightline limitations, and vulnerable road users. Ultimately, the technique permitted responsive countermeasure targeting and recalibration opportunities keyed to each crash form’s evolving landscapes. While it is indeed noteworthy that several variables have exhibited instability in their effects, it is equally important to acknowledge the existence of certain variables that maintain a relative degree of temporal stability. This underscores their pivotal role in shaping the foundation of enduring strategies aimed at enhancing traffic safety in the long run. The multifaceted investigation constitutes an invaluable reference for diverse transportation stakeholders seeking to curb rising truck fatalities through evidence-based improvements in policy, engineering, usage protocols, and technologies. It provides a blueprint for nimble safety planning within complex modernizing road systems.

Crash Contributing Factors and Patterns Associated with Fatal Truck-Involved Crashes in Bangladesh: Findings from the Text Mining Approach

Despite extensive research on traffic injury severities, relatively little is known about the factors contributing to truck-involved crashes in developing countries, especially in the context of Bangladesh. Because of the unavailability of authentic crash data sources, this study collected data from alternative sources such as online English news media reports. The current study prepared a database of 144 truck-involved fatal crash reports during the period of 12 months (January 2021 to December 2021). The crash reports contain a bag of 15,300 words. Several state-of-the-art text mining tools were utilized to identify crash patterns, including word cloud analysis, word frequency analysis, word co-occurrence network analysis, rapid automatic keyword extraction, and topic modeling. The analysis revealed several important crash contributing factors, such as the type of vehicle involved (auto-rickshaw, bus, van, motorcycle), the manner of collision (head-on), the time of the day (morning, night), driver behavior (speeding, overtaking, wrong-way driving), and environmental factors (dense fog). In addition, “coming from opposite direction” and “head-on collision” are two important sequences of events in truck-involved crashes. Truck drivers are also involved in crashes with trains at rail crossings. The findings of this research can assist policymakers in identifying crash avoidance strategies to lower truck-related crashes in Bangladesh.

Bivariate joint analysis of injury severity of drivers in truck-car crashes accommodating multilayer unobserved heterogeneity

Truck-involved crashes, especially truck-car crashes, are associated with serious and even fatal injuries, thus necessitating an in-depth analysis. Prior research focused solely on examining the injury severity of truck drivers or developed separate performance models for truck and car drivers. However, the severity of injuries to both drivers in the same truck-car crash may be interrelated, and influencing factors of injury severities sustained by the two parties may differ. To address these concerns, a random parameter bivariate probit model with heterogeneity in means (RPBPHM) is applied to examine factors affecting the injury severity of both drivers in the same truck-car crash and how these factors change over the years. Using truck-car crash data from 2017 to 2019 in the UK, the dependent variable is defined as slight injury and serious injury or fatality. Factors such as driver, vehicle, road, and environmental characteristics are statistically analyzed in this study. According to the findings, the RPBPHM model demonstrated a remarkable statistical fit, and a positive correlation was observed between the two drivers' injury severity in truck-car crashes. More importantly, the effects of the explanatory factors showing relatively temporal stability vary across different types of vehicle crashes. For example, car driver improper actions and lane changing by trucks, have a significant interactive effect on the severity of injuries sustained by drivers involved collisions between trucks and cars. Male truck drivers, young truck drivers, older truck drivers, and truck drivers' improper actions, elevate the estimated odds of only truck drivers; while older car and unsignalized crossing increase the possibility of injury severity of only car drivers. Finally, due to shared unobserved crash-specific factors, the 30-mph speed limit, dark no lights, and head-on collision, significantly affect the severity of injuries sustained by drivers involved in collisions between trucks and cars. The modeling approach provides a novel framework for jointly analyzing truck-involved crash injury severities. The findings will help policymakers take the necessary actions to reduce truck-car crashes by implementing appropriate and accurate safety countermeasures.

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Analysis of Injury Severity of Work Zone Truck-Involved Crashes in South Carolina for Interstates and Non-Interstates

This study investigates factors contributing to the injury severity of truck-involved work zones crashes in South Carolina (SC). The outcome of interest is injury or property damage only crashes, and the explanatory factors examined include the occupant, vehicle, collision, roadway, temporal, and environmental characteristics. Two mixed (random parameter) logit models are developed, one for non-interstates with speed limits less than 60 miles per hour (mph) and one for interstates with speed limits greater than or equal to 60 mph, using South Carolina statewide truck-involved work zone crash data from 2014 to 2020. Results of log-likelihood ratio tests indicate that separate speed models are warranted. The factors that were found to contribute to injury at the 90% confidence level in both models (interstate and non-interstate) are (1) dark lighting conditions, (2) female (at-fault) drivers, and (3) driving too fast for roadway conditions. Significant factors that apply only to non-interstates are SC or US primary roadways, activity area of the work zone, at-fault drivers under 35, sideswipe collision, presence of workers in the work zone, and collision with fixed objects. Significant factors that apply only to interstates are three or more vehicles, rear-end collision, location before the first work zone sign, and weekdays.

Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances

The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016–2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-of-state large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-of-state model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers’ training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.

Analysis of Temporal Stability of Contributing Factors to Truck-Involved Crashes at Work Zones in South Carolina

This study examines factors that contributed to truck-involved work zone crash injury in South Carolina, a state which experienced a rise in the number of injury crashes from 2014 to 2018. The outcome of interest is injury or property damage only (PDO) crashes. A binary logit model is developed using South Carolina statewide work zone crash data from 2014 to 2018. It considers various factors, including vehicle, crash, roadway, environment, day and time, and driver-related characteristics. Of particular interest to this study is the temporal stability of the contributing factors from year to year so that a long-term mitigating strategy can be developed. To this end, the test for parameter transferability is used, and it confirms that separate models should be used for each year, except for 2014. The only factor found to be temporally stable across all years (i.e., statistically significant in 2015–2018 models) is airbag deployment. Given that nearly all the factors are temporally unstable, an importance measure is introduced to enable transportation agencies to rank factors and develop countermeasures that target persistent contributing factors. Based on the importance measure, the top three ranked factors that contributed to work zone truck-involved crash injury in South Carolina from 2015 to 2018 are: (i) airbags deployed, (ii) tie between rear-end crashes and crashes on primary roadways, and (iii) crashes in dark conditions.

Injury Severity Analysis for Large Truck-Involved Crashes: Accounting for Heterogeneity

This study explores the crash injury severity of large truck-involved crashes, where the truck driver was identified as the at-fault driver. The paper focuses on vehicle-in-motion crashes that occurred on Florida’s state highways between 2007 and 2016. A random parameter ordered logit (RPOL) model was developed to identify random parameters and interaction effects. Results indicated that not using restraint systems, running a red light, wrong-way driving, failing to yield the right of way, tire or brake defects, and dark conditions had positive associations with higher levels of crash injury severity. The random variables—straight alignment, paved shoulders, and unpaved shoulders—showed significant random effects among the observations. For straight alignment, running red lights, following too closely, vision obstruction caused by fixed objects, and vision obstruction caused by fog were the sources of heterogeneity. Unpaved shoulders, running red lights, wrong-way driving, and the presence of parked or stopped vehicles were found as interaction effects. Results showed that accounting for heterogeneity and interaction effects significantly improved the goodness of fit of the model. This study provides more comprehensive knowledge of the influencing factors of large truck crashes by considering the role of heterogeneity and its potential sources in crash injury severity.

Assessment of commercial truck driver injury severity based on truck configuration along a mountainous roadway using hierarchical Bayesian random intercept approach

For the last decade, disaggregate modeling approach has been frequently practiced to analyze truck-involved crash injury severity. This included truck-involved crashes based on single and multi-vehicles, rural and urban locations, time of day variations, roadway classification, lighting, and weather conditions. However, analyzing commercial truck driver injury severity based on truck configuration is still missing. This paper aims to fill this knowledge gap by undertaking an extensive assessment of truck driver injury severity in truck-involved crashes based on various truck configurations (i.e. single-unit truck with two or more axles, single-unit truck pulling a trailer, semi-trailer/tractor, and double trailer/tractor) using ten years (2007–2016) of Wyoming crash data through hierarchical Bayesian random intercept approach. The log-likelihood ratio tests were conducted to justify that separate models by various truck configurations are warranted. The results obtained from the individual models demonstrate considerable differences among the four truck configuration models. The age, gender, and residency of the truck driver, multi-vehicles involvement, license restriction, runoff road, work zones, presence of junctions, and median type were found to have significantly different impacts on the driver injury severity. These differences in both the combination and the magnitude of the impact of variables justified the importance of examining truck driver injury severity for different truck configuration types. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (24%–42%) of intra-crash correlation (effects of the common crash-specific unobserved factors) in driver injury severity within the same crash. Finally, based on the findings of this study, several potential countermeasures are suggested.

Temporal instability of truck volume composition on non-truck-involved crash severity using uncorrelated and correlated grouped random parameters binary logit models with space-time variations

With the growing demand for inter-city freight transport, proportion of trucks in the freeway traffic has been increasing in China and worldwide in the past decade. There have been serious safety concerns for the prevalence of truck in freeway traffic given its size and weight, and the interferences of unsafe maneuvers of trucks on other traffic. However, existing literatures mainly focus on the crash severity of truck-involved crashes only. It is rare that the effects of the presence of trucks (of various classes) on the crash severity of non-truck-involved crashes (i.e. not involving any truck) are studied. To account for the effect of unobserved heterogeneity, random parameters discrete outcome models have been deployed to model the crash severity. However, the possible correlations between random parameters are seldom considered. This study aims to investigate the role of the presence and prevalence of trucks of various classes on the severity of non-truck involved crashes, with which both the uncorrelated and correlated panel-level space–time-varying heterogeneities are considered. To account for possible temporal instability, the uncorrelated and correlated grouped random parameters binary logit models are established for each separate year, based on the comprehensive crash and traffic data of eight freeway segments in Shandong of China during the period from 2016 to 2019. 4,008 crashes are extracted and grouped with space (i.e. road segment)-time (i.e. time of the day) variations considered. Results indicate that the proposed correlated grouped random parameters model is superior to the uncorrelated grouped random parameters logit model in three out of the four years. Also, correlation in the heterogeneous effects between super-large truck volume and average speed is significant. Moreover, temporal instability is revealed for majority of the factors, to different extents, including the heterogenous ones and volumes of all truck types. Nevertheless, this study addresses and discusses the fundamental issues of the temporally unstable correlation in unobserved heterogeneity between possible factors in crash severity analysis.

Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

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.

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.

Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach

Earlier research on injury severity of truck-involved crashes focused primarily on single-truck and multi-vehicle crashes with truck involvement, or investigated truck-involved injury severity based on rural and urban locations, time of day variations, lighting conditions, roadway classification, and weather conditions. However, the impact of different vehicle-truck collisions on corresponding occupant injury severity is lacking. Therefore, this paper advances the current research by undertaking an extensive assessment of the occupant injury severity in truck-involved crashes based on vehicle types (i.e., single-truck, truck-car, truck-SUV/pickup, and truck-truck), and identifies the major occupant-, crash-, and geometric-related contributing factors. A series of log-likelihood ratio tests were conducted to justify that separate model by vehicle and occupant types are warranted. Injury severity models were developed using 10 years of crash data (2007–2016) on I-80 in Wyoming through binary logistic modeling with a Bayesian inference approach. The modeling results indicated that there were significant differences between the influences of a variety of variables on the injury severities when the truck-involved crashes are broken down by vehicle types and separated by occupant types. The age and gender of occupants, truck driver occupation, driver residency, sideswipes, presence of junctions, downgrades, curves, and weather conditions were found to have significantly different impacts on the occupant injury severity in different vehicle-truck crashes. Finally, with the incorporation of the random intercept in the modeling procedure, the presence of intra-crash and intra-vehicle correlations (effects of the common crash- and vehicle-specific unobserved factors) in injury severities were identified among persons within the same crash and same vehicle.

Combined latent class and partial proportional odds model approach to exploring the heterogeneities in truck-involved severities at cross and T-intersections

Although the fatal rate of passenger vehicle-involved crashes has decreased in the United States, the fatal rate of truck-involved crashes has increased. This has, in recent years, become a more severe problem than that caused by passenger vehicle-involved crashes. More studies need to be conducted in order to investigate factors that impact the severity of truck-involved crashes within specific scenarios. This study identifies and evaluates the factors that affect the severity of the truck-involved crashes at cross and T-intersections in North Carolina from 2005 to 2017. A latent class clustering for data segmentation is implemented to mitigate unobserved heterogeneity inherent in the crash data. Four partial proportional odds models, which include fixed and unfixed parameters, are developed considering the heterogeneous and ordinal nature inherent in severities. Estimated parameters and marginal effects are further investigated for better interpreting the impacts. Results show heterogeneous explanatory variables and associated coefficients for different classes and severity levels, which indicate the superiority of this combined approach to obtaining more specific factors and accurate coefficients that are estimated in different scenarios. Many factors are found to contribute to the severities, and crossroad scenarios are found to be more severe than T-intersections. The top five driving behaviors at intersections that contribute to the severity include disregarded signs, improper lane use, followed too closely, ignored signals, and failure to yield. These behaviors arouse a necessity to amend the traffic laws and strengthen drivers’ education while giving further insights to engineering practitioners and researchers.

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.

Injury severity analysis of truck-involved crashes under different weather conditions

This paper investigates truck-involved crashes to determine the statistically significant factors that contribute to injury severity under different weather conditions. The analysis uses crash data from the state of Ohio between 2011 and 2015 available from the Highway Safety Information System. To determine if weather conditions should be considered separately for truck safety analyses, parameter transferability tests are conducted; the results suggest that weather conditions should be modeled separately with a high level of statistical confidence. To this end, three separate mixed logit models are estimated for three different weather conditions: normal, rain and snow. The estimated models identify a variety of statistically significant factors influencing the injury severity. Different weather conditions are found to have different contributing effects on injury severity in truck-involved crashes. Rural, rear-end and sideswipe crash parameters were found to have significantly different levels of impact on injury severity. Based on the findings of this study, several countermeasures are suggested: 1) safety and enforcement programs should focus on female truck drivers, 2) a variable speed limit sign should be used to lower speeds of trucks during rainy condition, and 3) trucks should be restricted or prohibited on non-interstates during rainy and snowy conditions. These countermeasures could reduce the number and severity of truck-involved crashes under different weather conditions.