Truck-involved Crashes Research Articles

Injury severity of truck-involved crashes in work zones on rural and urban highways: Accounting for unobserved heterogeneity

Truck-related crashes in the work zones always resulted in severe injury outcomes. This paper analyses the injury severity of truck-involved crashes in the work zones based on a ten-year (2005–2014) data collected in North Carolina. A wide range of variables is considered, including the driver, environmental, work-zone, road, and vehicle characteristics. The mixed logit (MXL) and partial proportional odds (PPO) logit models are employed to investigate the potential unobserved heterogeneity associated with the indicator of urban and rural highways. A comparison of the two models shows that PPO outperforms the MXL model. The estimation results showed that contributing factors resulting in injury-severity levels of work zones crashes involving trucks on rural and urban highways are significantly different. Elasticity effects are provided and used to evaluate the significance of the variables in the rural and urban models. The findings of this study help understand the injury-severity outcomes in truck-involved crashes in the rural and urban work zones.

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.

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.

Endogenous commercial driver’s traffic violations and freight truck-involved crashes on mainlines of expressway

Freight truck-involved crashes result in a high mortality rate and significantly impact logistic costs; therefore, many researchers have analyzed the causes of truck-involved traffic crashes. In the existing literature, it was found that truck-involved crashes are affected by factors such as road geometry, weather, driver and vehicle characteristics, and traffic volume based on a variety of statistical methodologies; however, the endogenous impact resulting from driver traffic violation has not been considered. The goal of the study is to discover the factors influencing freight vehicle crashes and develop more accurate crash probability estimation by explaining the endogenous driver traffic violations. To achieve the purpose of this study, we applied the two-stage residual inclusion (2SRI) approach, a methodology used in the nonlinear regression analysis model for capturing the endogeneity issue. This method improves the accuracy of the model by capturing the unobserved effects of driver traffic violations. From the results, traffic violations were identified to be influenced by the driver’s physical condition, as well as driver and vehicle characteristics. Furthermore, variables of driver traffic violations such as improper passing, speeding, and safe distance violation were found to be endogenous in the probability model of freight truck crashes on expressway mainlines.

Safety effectiveness of truck lane restrictions: a case study on Texas urban corridors

ABSTRACTTruck lane restrictions (TLRs) have become increasingly popular throughout the United States over the last 20 years due to the improvement in traffic operations and safety, pavement life, and other associated factors. This study examines the safety performance of the TLRs that have been implemented in the North Texas region. TLR prohibit semi- trucks (trucks) from using the left-most freeway lane, or inner lane, except in passing or emergency maneuvers. This study uses 16 sites with TLR in the Dallas-Fort Worth area to conduct an observational before-after study. For safety effectiveness analysis, it is always hard to get three years before and three years after data. For immediate safety effectiveness analysis, sophisticated predictive tools could be used in estimating ‘after’ year crashes. This study used the Enhanced Interchange Safety Analysis Tool (ISATe) to supplement the limited after year crash data. This study used Empirical Bayes (EB) method due to its consideration of regression-to-mean bias. The findings show that TLRs perform an overall positive safety effectiveness for large truck-involved fatal and injury crashes for six-lane roadways. This study concludes that implementing TLR as a truck-related safety strategy which are cost-effective to implement, may contribute to improving safety on urban freeways.

Application of multinomial and ordinal logistic regression to model injury severity of truck crashes, using violation and crash data

In 2016 alone, around 4000 people died in crashes involving trucks in the USA, with 21% of these fatalities involving only single-unit trucks. Much research has identified the underlying factors for truck crashes. However, few studies detected the factors unique to single and multiple crashes, and none have examined these underlying factors to severe truck crashes in conjunction with violation data. The current research assessed all of these factors using two approaches to improve truck safety. The first approach used ordinal logistic regression to investigate the contributory factors that increased the odds of severe single-truck and multiple-vehicle crashes, with involvement of at least one truck. The literature has indicated that past violations can be used to predict future violations and crashes. Therefore, the second approach used risky violations, related to truck crashes, to identify the contributory factors to the risky violations and truck crashes. Driver actions of failure to keep proper lane following too close and driving too fast for conditions accounted for about 40% of all the truck crashes. Therefore, the same violations as the aforementioned driver actions were included in the analysis. Based on ordinal logistic regression, the analysis for the first approach indicated that being under non-normal conditions at the time of crash, driving on dry-road condition and having a distraction in the cabin are some of the factors that increase the odds of severe single-truck crashes. On the other hand, speed compliance, alcohol involvement, and posted speed limits are some of the variables that impacted the severity of multiple-vehicle, truck-involved crashes. With the second approach, the violations related to risky driver actions, which were underlying causes of severe truck crashes, were identified and analysis was run to identify the groups at increased risk of truck-involved crashes. The results of violations indicated that being nonresident, driving off-peak hours, and driving on weekends could increase the risk of truck-involved crashes. This paper offers an insight into the capability of using violation data, in addition to crash data, in identification of possible countermeasures to reduce crash frequency.

Truck-involved crashes injury severity analysis for different lighting conditions on rural and urban roadways

This paper investigates factors affecting injury severity of crashes involving trucks for different lighting conditions on rural and urban roadways. It uses 2009–2013 Ohio crash data from the Highway Safety Information System. The explanatory factors include the occupant, vehicle, collision, roadway, temporal and environmental characteristics. Six separate mixed logit models were developed considering three lighting conditions (daylight, dark, and dark-lighted) on two area types (rural and urban). A series of log-likelihood ratio tests were conducted to validate that these six separate models by lighting conditions and area types are warranted. The model results suggest major differences in both the combination and the magnitude of impact of variables included in each model. Some variables were significant only in one lighting condition but not in other conditions. Similarly, some variables were found to be significant in one area type but not in other area type. These differences show that the different lighting conditions and area types do in fact have different contributing effects on injury severity in truck-involved crashes, further highlighting the importance of examining crashes based on lighting conditions on rural and urban roadways. Age and gender of occupant (who is the most severely injured in a crash), truck types, AADT, speed, and weather condition were found to be factors that have significantly different levels of impact on injury severity in truck-involved crashes.

Potential Safety Benefit of the Blind Spot Detection System for Large Trucks on the Vulnerable Road Users in Taiwan

Considering motorcyclists, pedestrians and bicyclists as vulnerable road users (VRUs), more than 75 percent of the victims of fatal crashes involving large trucks in Taiwan are VRUs. Most crashes occurred at or were due to the blind spots of large trucks because of the size and traveling locations of the VRUs. This study applies typology and statistical methods to estimate the potential safety benefit of blind spot detection (BSD) systems for large trucks on VRUs. The pre-crash scenarios associated with the blind spots of large trucks were derived by counting the maneuvers of large trucks and VRUs, prior to crashes, the truck drivers’ improper behaviors (cause of crashes), and the crash types. The number of crashes and fatalities were counted for the pre-crash scenario relevant to the BSD systems. A value of 0.8 of human machine interface factor (HMIF) based on a previous study was applied to estimate the potential safety benefits of the BSD system. The results show that the implementation of BSD systems on all large trucks could help avoid about 24, 10, and 11 percent of large truck-involved crashes with pedestrians, bicycles, and motorcycles, respectively. The BSD systems could also save 5 pedestrians, 3 bicyclists, and 15 motorcyclists per year from crashes involving large trucks.

How big data serves for freight safety management at highway-rail grade crossings? A spatial approach fused with path analysis

Future vehicle warning systems needs a local (instead of global) analysis of real-time information transmitted between vehicles and infrastructures, to provide local warning information matching the instantaneous driving contexts. Spatial modeling techniques extracting the location information into the analysis fulfill the needs of local analysis. For truck-involved collisions at highway-rail crossings, the local warnings seem to matters more, than for traffic crashes at the normal highway segments and regular intersections. Crashes at rail grade crossings can result in severe injuries and fatalities to vehicle occupants, while truck-involved crashes at crossings can further result in serious damage to train, crossing and railway equipment. Truck-involved crashes at grade crossings have received limited attention compared with crashes involved with passenger cars. This study presents a methodology of improving safety of trucks at railroad crossings, by taking advantage of the big data containing location information. Unlike previous studies that constructed a direct relationship between the safety outcomes and associated factors, this study investigates direct relationships together with indirect relationships through the truck driver behaviors before collisions, using path analysis techniques. To sum up, this study applies a spatial approach fused with path analysis to uncover the local relationships between truck driver injury severity and crossing controls across the space. By doing so, the research is able to: i) provide a benchmark of identifying potentially risky vehicles on a real time basis; ii) evaluate current control devices at railroad crossing across the country and pin point the potentially problematic crossing sites. An empirical study was conducted by using a rich crash database from the Federal Railroad Administration (N= 4738 for 2004–2014). The results show that truck-involved crashes occurring at crossings without gate controls are generally associated with higher chance of injury, while the associations vary significantly across the space. In general, crashes in the Midwest and Great Lake regions are associated with an even higher chance of injury at crossings without gates, compared with other regions. More Results and implications are discussed in the paper.

Hierarchical Bayesian random intercept model-based cross-level interaction decomposition for truck driver injury severity investigations

Traffic crashes occurring on rural roadways induce more severe injuries and fatalities than those in urban areas, especially when there are trucks involved. Truck drivers are found to suffer higher potential of crash injuries compared with other occupational labors. Besides, unobserved heterogeneity in crash data analysis is a critical issue that needs to be carefully addressed. In this study, a hierarchical Bayesian random intercept model decomposing cross-level interaction effects as unobserved heterogeneity is developed to examine the posterior probabilities of truck driver injuries in rural truck-involved crashes. The interaction effects contributing to truck driver injury outcomes are investigated based on two-year rural truck-involved crashes in New Mexico from 2010 to 2011. The analysis results indicate that the cross-level interaction effects play an important role in predicting truck driver injury severities, and the proposed model produces comparable performance with the traditional random intercept model and the mixed logit model even after penalization by high model complexity. It is revealed that factors including road grade, number of vehicles involved in a crash, maximum vehicle damage in a crash, vehicle actions, driver age, seatbelt use, and driver under alcohol or drug influence, as well as a portion of their cross-level interaction effects with other variables are significantly associated with truck driver incapacitating injuries and fatalities. These findings are helpful to understand the respective or joint impacts of these attributes on truck driver injury patterns in rural truck-involved crashes.

Modeling Safety Impacts of Off-Hour Delivery Programs in Urban Areas

Truck travel on urban road networks during daytime can be a major contributor to traffic congestion. A possible approach to relieve traffic congestion in urban areas is shifting a portion of trucks from regular daytime hours to nighttime off-hours. Benefits of this off-hour delivery strategy can be noticeable, but safety impacts need to be investigated. Manhattan, the most densely populated borough of New York City, with a large demand for truck deliveries, was the study area. Truck crashes, traffic volumes, and geometric design features of 256 road segments in Manhattan were collected to develop safety evaluation models. For quantification of the safety impacts of off-hour deliveries, an improved modeling approach was proposed; it involved the use of the multivariate Poisson-lognormal model integrated with measurement errors in truck volumes. The proposed model could address the inherent correlation of specific truck crash types and correct the estimation bias for safety effects of daytime and nighttime truck volumes. A Bayesian approach was employed to estimate the parameters of the proposed model. From the Bayesian posterior distributions, daytime and nighttime truck volumes did not have significantly different effects on either minor or serious crashes. In addition, truck crash counts were estimated with the proposed model under scenarios with different proportions of truck traffic shifted to nighttime. Results showed that off-hour delivery programs were not expected to increase the overall risk of truck-involved crashes significantly. Study findings can give transportation planners and policy makers insight on safety implications and decision making on off-hour deliveries.

A time of day analysis of crashes involving large trucks in urban areas

Previous studies have looked at different factors that contribute to large truck-involved crashes, however a detailed analysis considering the specific effects of time of day is lacking. Using the Crash Records Information System (CRIS) database in Texas, large truck-involved crashes occurring on urban freeways between 2006 and 2010 were separated into five time periods (i.e., early morning, morning, mid-day, afternoon and evening). A series of log likelihood ratio tests were conducted to validate that five separate random parameters logit models by time of day were warranted. The outcomes of each time of day model show major differences in both the combination of variables included in each model and the magnitude of impact of those variables. These differences show that the different time periods do in fact have different contributing factors to each injury severity further highlighting the importance of examining crashes based on time of day. Traffic flow, light conditions, surface conditions, time of year and percentage of trucks on the road were found as key differences between the time periods.

Identifying the factors contributing to the severity of truck-involved crashes

To address the dilemma between the need for truck transportation and the costs related to truck-involved crashes, the key is to identify the risk factors that significantly affect truck-involved crashes. The objective of this research is to estimate the effects of the characteristics of traffic, driver, geometry, and environment on severity of truck-involved crashes. Based on four crash severity categories (fatal/incapacitating, non-incapacitating, possible injury, and no injury/property damage only), a multinomial logit model is conducted to identify the risk factors. The investigation of risk ratios indicates that lower traffic volume with higher truck percentage is associated with more serious traffic crash with fatal/incapacitating injury while a non-standard geometric design is the main cause of non-incapacitating crashes. The influences of weather are significant for the possible-injury crashes while driver condition is the principal cause of no-injury/property-damage-only crashes. In addition, the statistical results demonstrate that the influence of the truck percentage is significant. One-unit change in the truck percentage will cause more than one times probability of being in an injury.

기상 및 교통조건이 고속도로 화물차 사고 심각도에 미치는 영향분석

교통안전 측면에서 화물차 관련 교통사고 심각도는 화물차의 차량 중량, 물류수송특성, 후행차량의 시거제약 등과 관련된다. 본 연구는 고속도로 화물차사고의 심각도에 미치는 기상 및 도로 교통요인을 도출함에 그 목적이 있다. 이항 로지스틱 회귀분석을 이용하여 사고심각도의 확률적 증가에 영향을 미치는 요인을 기상별로 비교분석하였다. 분석 결과 속도 단속, 속도 관리전략, 화물차 전용차로 등과 같은 교통류 관리전략이 사고심각도 감소를 위한 대책으로 제안되었다. 본 연구의 결과는 향후 화물차 사고발생 및 사고심각도 감소를 위한 교통 관제 및 운영관련 전략 수립시 유용하게 활용될 수 있을 것으로 판단된다. Understanding the characteristics of truck-involved crashes is of keen interest because such crashes are highly associated with greater potential leading to severer injury. The purpose of this study is to identify factors affecting injury severity of truck-involved crashes on freeways. In addition, a binary logistic regression technique is applied to identify causal factors affecting truck crash severity under normal and adverse weather conditions. Major findings from the analyses are discussed with truck operations strategies including speed enforcement, variable speed limit, and truck lane restriction, from the safety enhancement point of view. The results of this study would be useful for developing traffic control and operations strategies to reduce truck-involved crashes and injury severity in practice.

Large Truck–Involved Crashes: Exploratory Injury Severity Analysis

In recent years, a growing concern related to large truck accidents has increased owing to the level of injury severity that can be sustained and to the related potential economic impact. Current studies related to large truck-involved crashes are scarce and do not address the human factors that can greatly influence accident outcomes. This study presents an analysis of data from the fusion of several national data sets addressing injury severity related to large truck-involved crashes. This is accomplished by considering human, road environment, and vehicular factors in large truck-involved crashes on U.S. interstates. A random-parameter ordered-probit model was estimated to predict the likelihood of five injury severity outcomes—fatality, incapacitating, nonincapacitating, possible injury, and no injury. The modeling approach accounts for possible unobserved effects relating to human, vehicular, and road environment factors not present in the data. Estimation findings indicate that the level of injury severity is highly influenced by a number of complex interactions between factors, and the effects of some factors can vary across observations. DOI: 10.1061/(ASCE)TE.1943-5436.0000539. © 2013 American Society of Civil Engineers. CE Database subject headings: Freight transportation; Trucks; Injuries; Parameters; Traffic accidents. Author keywords: Freight movement; Large trucks; Ordered probit; Random parameter; Safety; Injury severity.

Investigation of Truck-Involved Angle Crashes Based on GES and FARS Crash Databases

Truck-car angle collisions have a higher crash frequency than other truck-involved collision types and tend to increase injury severity. This paper investigates both general and fatal truck-involved angle collisions using two national crash databases (2000-2004), General estimates system (GES) and Fatality Analysis Reporting system (FARS). In this study, two-vehicle angle collisions were classified into three groups based on fault roles of truck or car drivers in the acci- dents, including Truck-Car, Car-Truck, and Car-Car crashes. The occurrence conditions of the three angle crash types were compared to each other to identify the potential risk factors such as driver characteristics, road environments, and highway designs related to the truck-involved crashes. The multinomial logistic regression is used for the statistical analy- sis. Based on the result analysis of this study, it is suggested that truck-involved angle collisions should be considered as an important scenario design for retraining or education programs for the purposes of reducing older drivers' fatality rate; improving either the conspicuity of truck trailers or lighting design of the highway would reduce the frequency and sever- ity of truck-involved angle crashes; to improve incompatibilities between truck, car, and highway design, further studies should conduct in-depth analyses of geometric factors related to driver performances and behaviors in the car-truck con- flicts at intersections.

Safety Impact of Truck Lane Restrictions on Multilane Freeways

Many states have implemented truck lane restrictions in an attempt to improve safety and mobility on freeways. These restrictions typically prohibit trucks from traveling in the median (left-most) lane of multi-lane highways; the restriction potentially increases passing opportunities and reduces negative interactions between slow-moving trucks and faster-moving vehicles. Virginia has restricted trucks from the median lane of some interstates with three or more lanes by direction since 1997, but no systematic evaluation of the safety impact of the restrictions has been conducted. Evaluations of similar restrictions in other states have yielded contradictory findings, but those studies often used a limited data set. An empirical Bayes’ analysis of Virginia's restrictions was performed by using 6 years of crash data from 22 sites with restrictions and 16 similar sites without restrictions. The analysis showed that a breakpoint in safety performance occurred around 10,000 vehicles per day per lane. Facilities where the volume was below this threshold had significantly fewer crashes than anticipated, averaging a 13% reduction in total crashes and a 32% reduction in fatal and injury crashes. Sites where the volume exceeded this threshold had increases in total crashes and fatal or injury crashes of 28% and 23%, respectively. A detailed analysis of crashes on the higher-volume roads was conducted to screen out crashes in which the restrictions likely played no role. That analysis showed that the number of truck-involved crashes was still significantly higher than predicted, indicating that safety on higher-volume roads may be negatively affected by truck lane restrictions.

Costs of large truck-involved crashes in the United States

This study provides the estimates of the costs of highway crashes involving large trucks by type of truck involved. These costs represent the present value of all costs over the victims’ expected life span that result from a crash. They include medically related costs, emergency services costs, property damage costs, lost productivity, and the monetized value of the pain, suffering, and lost quality of life that a family experiences because of death or injury. Based on the latest data available, the estimated cost of police-reported crashes involving trucks with a gross weight rating of more than 10,000 pounds averaged US$ 59,153 (in 2000 dollars). Multiple combination trucks had the highest cost per crash (US$ 88,483). The crash costs per 1000 truck miles however, were US$ 157 for single unit trucks, US$ 131 for single combination trucks, and US$ 63 for multiple combinations.