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Case control study of heavy vehicle drivers' working time and safety
Abstract
A case group of heavy vehicles involved in crashes was compared with a control group of vehicles selected by police going to the scenes of crashes with the objective determining the risk of crash with respect to driving hours and other time intervals related to the working lives of the drivers. This was done by taking into account the driving hours details of the vehicle's driver as well as other vehicle and driver details. Evidence was found of an increased risk of crash in those cases where driving hours since the driver's last compulsory 10 hour off-duty period exceeded about eight. There were no other significant differences between the two groups with respect to other time intervals related to driving habits.
... These facts indicate that large trucks play a major role in fatal crashes that cost billions of dollars of loss every year. A major contributing factor to truck-related crashes is driver fatigue due to truck drivers' heavily irregular working schedules (Arnold et al., 1997;Campbell, 2002;Frith, 1994;Häkkänen & Summala, 2001;Hall & Mukherjee, 2008;Hanowski et al., 2005;Jovanis et al., 1991;Kaneko & Jovanis, 1992;Lin et al., 1993Lin et al., , 1994Mackie & Miller, 1978;Saccomanno et al., 1995). This study focuses specifically on analyzing crashes involving truckload (TL) carriers, since they typically own and operate large commercial trucks. ...
... They concluded that truck crash risks were lowest in the first 4 h, gradually increased after the 4th h, and reached a peak value after 9 h of driving. Another similar conclusion was made by Frith (1994) that crash risks increased beyond 8 h after a driver's 10-h off duty. Saccomanno et al. (1995) analyzed police crash reports and commercial vehicle driver demographics, working hours, and routes. ...
Introduction:
Driving hours and rest breaks are closely related to driver fatigue, which is a major contributor to truck crashes. This study investigates the effects of driving hours and rest breaks on commercial truck driver safety.
Method:
A discrete-time logistic regression model is used to evaluate the crash odds ratios of driving hours and rest breaks. Driving time is divided into 11 one hour intervals. These intervals and rest breaks are modeled as dummy variables. In addition, a Cox proportional hazards regression model with time-dependent covariates is used to assess the transient effects of rest breaks, which consists of a fixed effect and a variable effect.
Results:
Data collected from two national truckload carriers in 2009 and 2010 are used. The discrete-time logistic regression result indicates that only the crash odds ratio of the 11th driving hour is statistically significant. Taking one, two, and three rest breaks can reduce drivers' crash odds by 68%, 83%, and 85%, respectively, compared to drivers who did not take any rest breaks. The Cox regression result shows clear transient effects for rest breaks. It also suggests that drivers may need some time to adjust themselves to normal driving tasks after a rest break. Overall, the third rest break's safety benefit is very limited based on the results of both models.
Practical applications:
The findings of this research can help policy makers better understand the impact of driving time and rest breaks and develop more effective rules to improve commercial truck safety.
... Although heavy vehicle research has been undertaken, previous studies have significant limitations in part, due to inadequate study design [14] small sample sizes [15,16], the reliance on self-reported crashes and the lack of adjustment for confounders in observational studies [17]. Consequently, there is a need to identify the array of factors contributing to this burgeoning incidence of heavy vehicle crashes and to develop strategies to reduce such. ...
... A number of studies have shown that the risk of crashing for heavy vehicle drivers doubles with increased hours of driving [22]. Drivers in a New Zealand study were 2.6 times more likely to crash if they had driven 8 or more hours since their last compulsory 10 hour break, compared with those who had driven less than 8 hours [15]. Similarly, research in the United States reported a 2.2 fold increase in the likelihood of crash for each additional 100 km travelled [23]. ...
Heavy vehicle transportation continues to grow internationally; yet crash rates are high, and the risk of injury and death extends to all road users. The work environment for the heavy vehicle driver poses many challenges; conditions such as scheduling and payment are proposed risk factors for crash, yet the precise measure of these needs quantifying. Other risk factors such as sleep disorders including obstructive sleep apnoea have been shown to increase crash risk in motor vehicle drivers however the risk of heavy vehicle crash from this and related health conditions needs detailed investigation.
The proposed case control study will recruit 1034 long distance heavy vehicle drivers: 517 who have crashed and 517 who have not. All participants will be interviewed at length, regarding their driving and crash history, typical workloads, scheduling and payment, trip history over several days, sleep patterns, health, and substance use. All participants will have administered a nasal flow monitor for the detection of obstructive sleep apnoea.
Significant attention has been paid to the enforcement of legislation aiming to deter problems such as excess loading, speeding and substance use; however, there is inconclusive evidence as to the direction and strength of associations of many other postulated risk factors for heavy vehicle crashes. The influence of factors such as remuneration and scheduling on crash risk is unclear; so too the association between sleep apnoea and the risk of heavy vehicle driver crash. Contributory factors such as sleep quality and quantity, body mass and health status will be investigated. Quantifying the measure of effect of these factors on the heavy vehicle driver will inform policy development that aims toward safer driving practices and reduction in heavy vehicle crash; protecting the lives of many on the road network.
... We consider that the risk of traffic accidents has a correlation with driver fatigue, which could be related to the distance that the driver travels and the volume of loading and unloading while making deliveries (Torregroza-Vargas et al. 2014). Moreover, some other earlier studies found that fatigue due to longer driving distances and duration increases the risk of road accidents (Stevenson et al. 2010;Frith 1994;Cummings et al. 2001). ...
Real-life transport operations are often subject to uncertainties in travel time or customers’ demands. Additionally, these uncertainties greatly impact the economic, environmental, and social costs of vehicle routing plans. Thus, analysing the sustainability costs of transportation activities and reliability in the presence of uncertainties is essential for decision makers. Accordingly, this paper addresses the Sustainable Vehicle Routing Problem with Stochastic Travel times and Demands. This paper proposes a novel weighted stochastic recourse model that models travel time and demand uncertainties. To solve this challenging problem, we propose an extended simheuristic that integrates reliability analysis to evaluate the reliability of the generated solutions in the presence of uncertainties. An extensive set of computational experiments is carried out to illustrate the potential of the proposed approach and analyse the influence of stochastic components on the different sustainability dimensions.
... Also, all transportation process managers should be trained. Many unclear rules due to a poor training of managers make freight transportation planning full of mistakes and forces drivers not to comply with the rules in force (Scheller et al., 2013;Frith, 1994;Blagoev et al., 2018;Muha and Sever, 2009). The correlations analysis has showed certain inaccuracies in the obtained results (Fig. 8, 12, 13). ...
According to the Central Statistical Office in Poland, for the last 10 years (2009-2018), the number of trucks has increased by as much as 25%. More than 6 million trucks drive in the European Union, and Poland, followed by Italy, boasts of the largest fleet (over one million trucks). For some time, freight transportation companies have been signaling the problem of lack of staff. The Polish Road Transport Inspectorate and the National Labor Inspectorate supervise the transportation sector. All issues related to drivers' working time are law-regulated. The main objective of introducing regulations on drivers' working time is to improve road safety and drivers' working conditions. The top-down imposition of break and rest periods prevents drivers' fatigue and serves to regenerate forces. Fatigue reduces psychomotor skills, and the speed of reaction is particularly important in this profession. The practical goal of this article is to show how drivers perceive these problems, this scientific problem but in a different approach was also presented in the works. The analyzed results come from research conducted by the authors of the article. The research was conducted in the form of a multidirectional survey, 100 people (professional trucks drivers) answered each question. Each of them declared that they are a driver and work in Poland. The study was conducted in December 2018.
... Each commercial truck has different capabilities depending on model year and make, and human behavior (risk-taking) components vary within different age groups. Past studies in Australia, New Zealand, and the U.S.A. indicate that drivers under the age of 25 have a relatively higher risk of being involved in fatal crashes compared with other age groups (4)(5)(6)(7)(8)(9). However, the risks for older age groups, especially truck drivers, have not been studied properly, as past studies have not completely analyzed risk profiles as they change with age. ...
Commercial/large-truck fatal crash involvement by drivers of different age groups is a critical issue for the trucking industry. Escalating safety concerns related to these heavy vehicles serving the freight economy in the U.S. have an impact national freight reliability and economic growth. This study identifies major contributing factors leading to large-truck fatal crashes for four age groups of driver: <30, 30–49, 50–65, and 65+. The analysis in this study is based on five years (2012–2016) of Fatality Analysis Reporting System data and provides an overall picture of risk factors in large-truck fatal crashes. In total, 30 variables were found to be significant in the logit models, indicating varying risks associated with large-truck drivers of these four age groups. Model results indicate different risk factors associated with driver characteristics, spatial and temporal characteristics, vehicle and vehicle maneuvering characteristics, and environmental conditions at the time of the crashes. Identifying the risk factors for different age groups of drivers is important so proper countermeasures can be implemented from the perspective of human factors (e.g., safe speed choice, fatigue), roadway engineering (e.g., design of roadside barriers, radius of ramps), enforcement (e.g., presence of law enforcement personnel at critical locations), and emergency medical attention in remote areas. Considering the aging of the truck driver population in the U.S. and around the world, the findings of this study are vital to understand better the importance of safety in relation to large-truck fatal crashes.
... data have found increased crash odds (or relative risk) with hours driving, particularly after about 5 to 6 h; increased crash odds were found in studies by Harris and Mackie (1) and Mackie and Miller (2); by Jovanis and Chang (4) and Chang and Jovanis (5); by Jovanis et al. (6); by Kaneko and Jovanis (7); by Lin et al. (8,9); by Park et al. (10); and by Campbell and Hwang (11). Studies by Frith (12) and Saccomanno et al. (13) also found association between driving hours and increased crash odds. By contrast, the 1996 study (3), using alertness tests and instrumented truck measures rather than crashes, found stronger correlation between fatigue and time of day, and little correlation between fatigue and driving hours. ...
There is a need to explore the relationship, if any, between the probability of a crash and the hours worked by truck drivers. The need arises from the continued adjustment of federal hours of service regulations for truck drivers. This research used data logs from less-than-truckload carrier operations in 2004 to 2005 and in 2010 to estimate the probability of a crash after a certain amount of time spent driving, given no crashes until that time. Driver logs for 7 days before each crash were used and compared with a random sample (two drivers) of drivers who did not crash and were selected from the same company, terminal, and month. This study involved 686 subjects, including 224 crash-involved drivers. Discrete-time survival analysis models indicated a consistent increase in crash odds as driving time increased beyond the fourth hour. Breaks from driving reduced crash odds by as much as 50% compared with situations of drivers with no breaks. Crash odds were lowest when drivers returned to work during the day without an immediately preceding extended recovery period (but with at least minimum required off-duty time). Drivers returning to work immediately after a 34-h recovery period had crash odds 50% to 150% higher than those for drivers without the recovery immediately before a trip. Drivers had the highest crash odds immediately after returning from the extended time off; the effect then diminished with time.
Tight delivery schedules have been blamed for widespread violations of hours-of-service rules by interstate tractor-trailer drivers. The purpose of the present study was to identify determinants of drivers' schedules, particularly delivery requirements of shippers. Long-haul drivers were surveyed at weigh stations in Wyoming and Tennessee and asked who had arranged their current loads. The arranger, usually a motor-carrier dispatcher, then was interviewed by telephone. Interviews were conducted with 270 of 309 identified dispatchers. Revenue was the most frequently cited factor (75%) in decisions to accept or reject loads from shippers. Delivery deadlines (24%) and the hours-of-service status of the nearest driver (9%) were cited much less. Dispatchers reported that shippers ask for sufficient time for drivers to adhere to both speed limits and hours-of-service rules. Trip mileage is reported as the key determinant of trip schedule assignments (58%); however, other factors also are considered, including speed limits (27%) and past experience with particular routes (13%). About two-thirds of the dispatchers reported using rule-of-thumb average travel speeds. Overall, 14% of dispatchers reported that they expected drivers to travel at an average speed in excess of 60 mph. Tight delivery schedules and hours-of-service violations can occur if rule-of-thumb average speeds above 60 mph are the primary basis for assigned trip times. This survey suggests that tight schedules cannot be attributed solely to shipper demands.
Safety, Health and Environment (SHE) Code of Practice (COP) is introduced as one of the road safety intervention programmes as guidance for bus operators to implement safety and health in their working environment. The objective of this study is to investigate the effectiveness of SHE COP implementation of a single bus operator that has adopted one man operation. The data was collected by completing a set of checklist after observing the activities during pre departure, during the trip, and upon arrival. The results show that there are seven widely practiced SHE COP elements. In addition, the results show that 46% of the buses have average speed exceeding the maximum permissible speed on the highways (90km/h), and all the maximum speeds exceed the highway speed limit for buses. The statistical analysis shows that there is no significant association between SHE COP elements and the selected variables such speed and inappropriate driving behavior.
Thesis (M.S.C.E.)--University of Virginia, 2003. Includes bibliographical references (leaves 74-76).
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