:: Industrial Engineering & Management Systems ::
Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.17 No.2 pp.302-310
DOI : https://doi.org/10.7232/iems.2018.17.2.302

Human Factors and Severity of Injury of Delivery Truck Crashes Registered for Work-Related Injuries in South Korea

Dong Seok Shin, Myoung Hwan Park, Byung Yong Jeong*
Department of Industrial and Management Engineering, Hansung University, Seoul, Republic of Korea
Corresponding Author, E-mail: byjeong@hansung.ac.kr
July 13, 2017 January 4, 2018 February 18, 2018

ABSTRACT


This study aims to investigate the human factors and severity of injury of injured drivers in delivery truck crashes. This study compared the distribution of injured truck drivers with the severity of their injuries by cause of traffic crashes. 370 truck drivers registered for work-related injuries were analyzed by driver-related factors, driving condition-related factors, and direct cause of the crash. Human errors accounted for the most significant share of causes for truck crashes while traffic violations and drowsy driving were the primary causes of high severity crashes. Crashes caused by truck failures and drowsiness have a relatively high occurrence rate and high-severity injury rate on the expressway or in trucks over 5 tons. Crashes caused by drowsy driving on a straight road, traffic violation at an intersection, and truck failure on curved roads or hills have a high occurrence rate and high-severity injury rate. Also, rearend crashes and crashes with road barriers have a high occurrence rate. The research findings will serve as a practical guideline for establishing preventive measures for traffic crashes.



초록


    1. INTRODUCTION

    Delivery truck drivers pick up, transport, and drop off packages or merchandise from a distribution center to businesses and households. They often have to maintain their focus and drive until late hours of the night, which make them vulnerable to fatigue. Irregular dietary habits and long hours of fixed posture while driving are some additional hardships they experience. Driving in congested traffic or adhering to strict delivery timelines can also be stressful (BLS, 2017). Furthermore, large truck drivers can be away from home for days or weeks at a time, because they deliver goods over intercity routes. Thus, a truck driver is recognized as an occupation with the highest rates of injuries and illnesses (Smith and Williams, 2014; McCartt et al., 2000). Also, truck delivery crashes can be large in scale, incurring enormous monetary costs or fatal injuries (Chen et al., 2014; Smith, 2015).

    It has been reported that human factors are attributable to approximately 80% of traffic crashes. In an analysis of tank truck crashes, human-related factors caused 73.8% of the crashes (Shen et al., 2014), and Starnes (2006) reported that driver-related factors account for 81% of the crashes. There is also a report that crashes involving heavy vehicles have a more significant share of human errors such as traffic violation, error, and lapse in judgement than those involving light vehicles (Bener et al., 2008). Therefore, an analysis of the cause of traffic crashes focusing on human factors can contribute significantly to developing crash prevention measures.

    Human factors related to traffic crashes can be categorized into three groups: driver’s properties such as age and gender (Donmez and Liu, 2015; Duke et al., 2010); organizational factors such as work pressure and payment structure (Thompson et al., 2015; Zhu and Srinivasan, 2011); and causes that are directly related to the crash such as drowsy driving, inattention, and violation (McCartt et al., 2000; Pylkkönen et al., 2015; Vivoli et al., 2006). Reported human risk factors that can directly contribute to truck crashes include inattentive or drowsy driving, imprudent speed, close following, improper overtaking, violation of traffic signals, and using wrong lanes (Mesken et al., 2002; Sagberg et al., 2015). Studies that compare drivers’ general properties such as gender, age, hours of sleep, driving experience, and alcohol intake can also be considered research on human factors (Duke et al., 2010). Analyses of human factors of crashes mainly use Reason et al. (1990)’s theory. Reason et al. (1990)’s theory analyzes causes of crash by categorizing them into mistake, slip, lapse, and violation (Bener et al., 2008; Blockey and Hartley, 1995; Classen et al., 2010; Lucidi et al., 2010).

    The objective of this study is to describe the human factors and severity of injury of injured drivers in delivery truck crashes. This study compares the distribution of injured truck drivers with the severity of their injuries by the cause of traffic crashes focusing on human factors. Also, this study is to provide the truck delivery industry with the information needed to assist in establishing priorities and strategies intended to reduce occupational injuries or fatalities.

    2. METHODS

    2.1. Data Collection and Terminology

    In the Republic of Korea, employers are required to have industrial accident compensation insurance under the Industrial Safety and Health Act. For compensation purposes, companies are also obliged to report injury crashes to the relevant enforcing authority (Jeong, 1998, 2015; Jeong and Shin, 2016). Workers’ compensation records for workrelated traffic crashes are based on police reports and drivers’ interview. Subjects of this study include employees that have sustained work-related injuries and have taken 4 or more days off following the accident, while crashes caused by driving under the influence of alcohol or drugs were excluded from this study because they were not be accepted as occupational injuries. This study only examined the drivers, not others who were onboard the truck at the time of the accident, which means that the number of crashes is identical to the number of injured drivers. In this study, 370 male injured drivers were classified as injured persons of delivery truck driving crashes from 2008 to 2012.

    2.2. Analysis of Crash Causes and Research Variables

    This study identified direct causes of truck crashes based on accident overview in worker’s compensation record. In this study, we want to focus on the first leading cause which contributed most to the crash based on an overview of the accident. Therefore, in the case of a crash due to multiple causes, the cause of accident was stated based on the first leading cause. Table 1 shows the classification of the cause of crashes used in this study, divided into either driver-related human factors or truck failure. Driver-related human factors adopts the classification of human error used in Reason et al. (1990)’s study and included drowsiness, violation and human error (mistake, slip and lapse). Truck failure comprises failed brake and the flat tire. In this study, the violations refer to the intentional traffic violations of drivers, not including human error, drowsy driving, and truck failures.

    This study further investigated characteristics related to the driver, driving condition, and the crash itself. Driver- related factors include age, duration of employment, and employer’s size while driving condition-related factors include time and weather of the day, truck’s weight size, and road type. Crash factors include location, crash type, injury severity, sick leaves and cause of crash.

    This study used a chi-square test to analyze whether the distribution of injured drivers varied by cause of crash and other crash-related variables. Also, one-way ANOVA analysis was carried out to verify whether the average number of sick leaves of injured drivers varied by cause of accident and other accident-related variables. Statistical tests were run on SPSS, a statistics package, with a significance level of 0.05.

    3. RESULTS

    3.1. Characteristics of Injured Drivers and Sick Leaves

    Table 2 shows the distribution of injured drivers according to the cause of crash and injury severity. In this study, injuries are classified as high severity injuries and nonfatal injuries. A high severity injured means a person who has died or has suffered a loss of work days of more than 1,000 days, and nonfatal injuries mean injuries who takes less than 1,000 days of work lost. In Table 2, of the total injured drivers, 88.9% were nonfatal injuries, and 11.1% were high severity injuries.

    Regarding the cause of the crash, human errors, such as ‘misjudge the gap or road surfaces’ and ‘fail to notice,’ were the primary reasons accounting for 70.3% of all crashes, followed by drowsy driving that represented 10.5%, traffic violation, 10.0%, and truck failure, 9.3%. 90.7% of all crashes were due to human factors while 9.3% were attributable to truck-related causes.

    The primary cause of crashes that led to high severity injuries is human errors accounting for 68.3% of all high severity injuries, followed by drowsy driving (14.6%), traffic violations (9.8%), and truck failures (7.3%). On the other hand, crashes caused by drowsy driving resulted in the highest high-severity injury rate (15.4%), followed by slips and laps (13.7%), violations (10.8%), mistakes (9.6%) and truck failures (8.8%).

    One-way ANOVA was used to test whether the mean of sick leaves varied depending on the cause of crashes. Figure 1 presents the mean of sick leaves for nonfatal injured drivers by cause of crash. Crashes caused by traffic violations have the highest mean of sick leaves at 204.8 days. However, there was no difference in the overall mean of sick leaves at a significance level of 0.05 (F = 1.995, p = 0.095).

    Overall, it was found that human errors (mistake, slips, and lapses) caused the most significant share of crashes, and crashes caused by drowsy driving have the highest severity injury rate while crashes caused by traf-fic violations have the highest mean of sick leaves of nonfatal injured drivers.

    3.2. Characteristics of Driver-Related Factors

    3.2.1. Distributions of Injured Persons By Age

    Table 3 shows the distribution of injured drivers by age and cause of crashes. In Table 3, the number of crashes caused by drivers under 40 years old accounted for the largest portion (45.1%), followed by those in their forties (31.6%) and those in their 50s or older (23.2%). The rate of high severity injuries by age shows that drivers under 40 years of old represented 10.2%, those in their 40s, 13.7%, and those who are 50 years or older, 9.3%. The distribution of high severity and nonfa-tal injuries by age was not significantly different (χ2 = 1.213, p = 0.545).

    There was no significant difference in the age dis-tribution of injured drivers by cause of crash (χ2 = 12.967, p = 0.113). Meanwhile, for injured drivers under the age of 40, the high-severity injury rate was the highest in truck failure (20.0%), while violation (26.7%) in their 40s and drowsiness (25.0%) in 50 years or older.

    3.2.2. Distributions of Injured Persons by Work Experience

    Table 4 shows the distribution of injured drivers by work experience and cause of crashes. Regarding work experience of injured drivers, novice drivers with less than one year on the job accounted for 64.1% of the injured. Meanwhile, the rate of high severity injuries by work experience shows that injured drivers with less than one year of experience represented 10.5%, those with 2 to 5 years of experience, 14.6%, and injured drivers with more than five years of experience, 5.4%. There is no significant difference in the work experience of injured persons across differing levels of severity of injuries (χ2 = 2.437, p = 0.290).Figure 1

    There exists a meaningful difference in the work experience distribution of injured drivers between dif-ferent causes of crashes (χ2 = 12.984, p = 0.043). For drivers with less than one year of experience, the prima-ry cause of crashes is human error accounting for 69.2% of all crashes, followed by violation (11.8%), truck failures (9.7%), and drowsiness (9.3%). In drivers with 2 to 5 years of experience, the primary cause is human error accounting for 67.7%, followed by drowsi-ness (17.7%), violation (7.3%), and truck failures (7.3%). For drivers with more than five years of experience, the primary cause is human error accounting for 83.8%, followed by truck failures (10.8%), and violation (5.4%).

    For injured drivers with less than one year of expe-rience, the high-severity injury rate was the highest in drowsiness (13.6%), while violation (28.6%), truck fail-ures (28.6%) and drowsiness (17.6%) had a higher high-severity injury rate for drivers with 2 to 5 years of expe-rience. In drivers with more than five years of experi-ence, high severity injuries occurred only in human error, and the rate was low

    3.3. Characteristics of Driving Conditions Factors

    3.3.1. Distributions of Injured Persons by Truck Size

    Table 5 shows the distribution of injured drivers by truck size and cause of crash. Overall, 37.3% of the injured drivers were driving trucks weighing less than 5 tons, and 62.7% were driving trucks over 5 tons. Also, the high-severity injury rate (12.9%) in trucks over 5 tons was higher than the high-severity injury rate (8.0%) in trucks less than 5 tons. The distribution of high severi-ty injuries and nonfatal injury by truck type showed no difference (χ2 = 2.161, p = 0.142).

    There was a difference in the distribution of injured drivers by truck size and the cause of crash (χ2 = 23.437, p < 0.001). The cause of crash in trucks less than 5 tons were human errors (81.9%) and violations (10.9%), while crashes caused by human error (63.4%), truck failure (13.8%) and drowsiness driving (13.4%) were the most frequent in trucks over 5 tons.

    For injured drivers driving over 5 tons, the high-severity injury rate was higher in violations (18.2%) and drowsiness (16.1%), while drowsiness (12.5%) had a higher high-severity injury rate for injured drivers driving trucks less than 5 tons.

    3.3.2. Distributions of Injured Persons by Road Type

    Table 6 shows the distribution of injured drivers by road type and cause of crash. Road type is either an expressway which is a car-only road without traffic lights or a local road with intersections and traffic lights for pedestrians. Overall, 33.5% of crashes occurred on highways, and 66.5% of crashes took place on local roads. The rate of high-severity injuries was 15.3% on the express-way and 8.9% on the local road. The distribution of high severity and nonfatal injuries was different by road type (χ2 = 3.405, p = 0.065).

    The Chi-square test showed a different distribution of injured persons by cause of crash and road type as well (χ2 = 25.247, p < 0.001). According to Table 6, 75.8% of crashes on expressways were caused by hu-man errors, 16.1% by drowsiness, and 8.1% by truck failure. On the other hand, 67.5% of crashes on local roads were the result of human error, followed by viola-tion accounting for 15.0% surpassing truck failure, 9.8% and drowsy driving, 7.7%.

    The high-severity injury rates on expressway were high in crashes caused by drowsiness (20.0%) and hu-man errors (14.9%). On the other hand, high-severity injury rates on local roads were high in crashes caused by violations (10.8%) and drowsiness (10.5%).

    3.3.3. Distributions of Injured Persons by Weather and Time of the Accident

    Table 7 shows the cause distribution of injured drivers by weather and time of the accident. Time of the accident is either daytime or nighttime, with daytime defined as hours between 5:00 and 20:00. Overall, 57.0% of all truck crashes occurred on clear (sunny or cloudy) days (5:00-20:00), 19.5% on clear nights, 17.0% on rainy (rainy, snowy or foggy) days, and 6.5% on rainy nights. The distribution of high severity and nonfatal injuries was different by weather and time of the accident (χ2 = 7.754, p = 0.051). Nonfatal injuries occurred most frequently during clear daytimes (56.8%) and clear nighttimes (18.5%), but high severity injuries occurred mainly during clear daytimes (58.5%) and clear nighttimes (31.7%). The high-severity injury rate was the highest at 18.1% during clear nighttimes.

    Table 7 illustrates that most crashes on rainy days or nights were related to human error. On clear days, 62.6% of crashes were caused by human error, 14.7% by truck failure, 13.3% by the violation, and 9.5% by drowsiness. While on clear nights, 59.7% of crashes were caused by human error, 26.4% by drowsiness, and 11.5% by the violation.

    Table 7 also showed that high-severity injury rate was higher for traffic violations (25.0%) and human errors (20.9%) on clear nights, and drowsiness (20%) on clear days.

    3.4. Characteristics of Crash-Related Factors

    3.4.1. Distributions of Injured Persons by Location of Crash

    Table 8 provides the distribution of injured drivers by cause of crash and location of the road where the accident occurred. Overall, 62.7% of all crashes occurred on straight roads, 21.1% at intersections, and 16.2% on curves or hills. The rate of high-severity injuries was the highest at 15.0% on curves or hills. The cause distribution of high severity injuries and nonfatal injuries by location of the crash showed no difference (χ 2 = 1.117, p = 0.572).

    A Chi-square test confirmed a different distribution in injured drivers by cause of crash and location of the crash (χ2 = 64.782, p < 0.001). On straight roads, the primary cause of the crash was human error (71.6%), followed by drowsiness (15.5%) and truck failures (6.9%). At intersections, the primary cause was human error (60.3%), followed by violations (29.5%) and truck failures (7.7%). On curves or hills, most crashes were caused by human error (78.3%) or truck failure (20.0%).

    On straight roads, high-severity rate was high in crashes caused by drowsiness (16.7%), while violation (13.0%) has high-severity injuries at intersections. On the other hand, high-severity injury rates on curves or hills were high in crashes caused by truck failure (16.7%) and human error (14.9%).

    3.4.2 Distributions of Injured Persons by Type of Crash

    Table 9 presents the distribution of injured drivers by cause and type of crash. Overall, rear-end crashes represent 37.6% of crashes while crashes with road barriers such as median barrier or guard rail account for 31.4% and roll over or fall crashes, 10.5%. High-severity injury rates were high in crashes with road barriers (12.9%). The distribution of high severity injuries and nonfatal injury by type of crash showed no difference (χ2 = 0.603, p = 0.963).

    A Chi-square test showed a different distribution in injured drivers by cause and type of crash (χ 2= 153.667, p < 0.001). Head-on crashes were mostly the result of human error (61.5%) and violation (30.8%). Regarding rear-end crashes, 80.6% were due to human error, 14.4% due to drowsy driving. As for sideswipe crashes, 56.8% were caused by the violation, and 37.8% by human error. Meanwhile, crashes with road barriers were attributable to human error in 68.1% of the cases, truck failure in 18.1% of the cases, and drowsiness in 12.9% of the cases. As for rollover or fall crashes, 79.5% were caused by human error, and 10.3% by truck failure.

    Human error (11.6%~12.5%) had a high rate of high severity in head-on and rear crashes, while violations (14.3%) had a high rate in sideswipe crashes. On the other hand, the high-severity injury rates for rollover or crashes with road barriers were greater in crashes caused by drowsiness.

    4. DISCUSSION AND CONCLUSION

    The delivery truck driver has high severity injury crashes compared to other occupations (Smith and Williams, 2014; McCartt et al., 2000). This study examined the general properties of truck drivers, in various delivery truck crashes and the distribution of injured drivers among various accidents.

    Results showed that human errors, such as ‘misjudge the gap or road conditions’ and ‘fail to notice,’ were the primary causes accounting for 70.3% of all crashes. Crashes caused by drowsy driving have the highest severity injury rate.

    Regarding injured drivers’ work experiences, drivers with less than five years of work experience accounted for 90.0% of all injured in crashes. Also, they had a relatively high rate of severe injuries for crashes due to drowsiness or violation. Also, novice drivers with under one year of work experience were involved in 64.1% of the total crashes. The study of Chen et al. (2015) points out the need of improved training for entry-level drivers, as 38% of long-haul truck drivers did not receive proper training when they started their work for the first time.

    Concerning driving conditions on the expressway or in trucks over 5 tons, crashes caused by human errors, truck failures and drowsiness have a relatively high occurrence rate and high-severity injury rate. Meanwhile, high severity or nonfatal injuries occurred most frequently during clear daytimes (57.0%), and clear nighttimes (19.5%), and high-severity injury rate was the highest at 18.1% on clear nights. Crashes caused by human errors and drowsy driving on a straight road, traffic violation at an intersection, and truck’s failure on curved road or hill have a high occurrence rate and high-severity injury rate. Meanwhile, rear-end crashes (caused by human errors and drowsy driving) and crashes with road barriers (caused by human errors, truck failure, and drowsiness) have a high occurrence rate. Results show that cause of crash varies by road type, truck size, weather, time of day, the location of crash and type of crash, indicating the need for tailored training on prevention measures for truck drivers. For instance, on straight roads, measures to prevent drowsy driving will be more effective, while at intersections, measures to avoid traffic violations are required. As for curved roads or hills, drivers can be protected by obeying speed limits and performing regular maintenance checkups on trucks.

    Results of the study indicate that the accident caused by drowsy driving is higher at nighttime than daytime. It can be assumed that drivers who operate under accumulated fatigue are more prone to drowsiness at nighttime. In particular, crashes caused by truck failure mostly occur in the daytime because speeding with heavy loads can lead to a tire or brake failure. Therefore, overloading and speeding must be managed appropriately, and maintenance and repair of trucks are necessary to prevent truck crashes that cause high severity injuries.

    However, there exists limitations to the use of these results: (1) The crashes reported in this study were from the work-related injury compensation records, (2) These crashes resulted in more than 4 days absence from work, and (3) There exists differences in the number of crashes between workers’ compensation records and the whole crash population.

    In spite of these limitations, this study provides an overview of the properties of human factors on delivery truck crashes. Results of this study highlight the importance of reducing drivers’ fatigue, keeping reasonable driving hours and developing driving habits to prevent crashes by truck drivers. The results will contribute towards improving measures to prevent traffic crashes including a system to support truck drivers.

    ACKNOWLEDGMENTS

    This research was financially supported by Hansung University.

    Figure

    IEMS-17-302_F1.gif

    Mean of sick leaves of nonfatal injuries by cause of collisions.

    Table

    Classification of crash causation and associated factors

    Distribution of injured drivers and sick leaves by injury severity and cause of collisions

    Distribution of injured drivers by age and cause of collisions (persons)

    Distribution of injured drivers by work experience and cause of collisions

    Distribution of injured drivers by truck size and cause of collisions

    Distribution of injured drivers by road type and cause of collisions

    Distribution of injured drivers by weather and time of the accident

    Distribution of injured persons by location and collision cause

    Distribution of injured persons by collision type and collision cause

    REFERENCES

    1. A. Bener , M.G. Al Maadid , T. A-zkan , D.A. Al-Bast , K.N. Diyab , T. Lajunen (2008) The impact of four-wheel drive on risky driver behaviours and road traffic accidents., Transp. Res., Part F Traffic Psychol. Behav., Vol.11 (5) ; pp.324-333
    2. P.N. Blockey , L.R. Hartley (1995) Aberrant driving behaviour: Errors and violations., Ergonomics, Vol.38 (9) ; pp.1759-1771
    3. BLS, U. S. (2015) Occupational outlook handbook, 2014- 15 Edition, Heavy and tractor-trailer truck drivers, Available from:, http://www.bls.gov/ooh/transportation-and-material-moving/heavy-and-tractor-trailertruckdrivers.htm
    4. G.X. Chen , H.E. Amandus , N. Wu (2014) Occupational fatalities among driver/sales workers and truck drivers in the United States 2003-2008., Am. J. Ind. Med., Vol.57 (7) ; pp.800-809
    5. S. Classen , O. Shechtman (2010) Traffic violations versus driving errors of older adults: Informing clinical practice., Am. J. Occup. Ther., Vol.64 (2) ; pp.233-241
    6. B. Donmez , Z. Liu (2015) Associations of distraction involvement and age with driver injury severities., J. Safety Res., Vol.52 ; pp.23-28
    7. J. Duke , M. Guest , M. Boggess (2010) Age-related safety in professional heavy vehicle drivers: A literature review., Accid. Anal. Prev., Vol.42 (2) ; pp.364-371
    8. B.Y. Jeong (1998) Occupational deaths and injuries in the construction industry., Appl. Ergon., Vol.29 (5) ; pp.355-360
    9. B.Y. Jeong (2015) Cooking processes and occupational accidents in commercial restaurant kitchens., Saf. Sci., Vol.80 ; pp.87-93
    10. B.Y. Jeong , D.S. Shin (2016) Characteristics of occupational accidents in Korean, Chinese, Japanese and Western cuisine restaurants., Hum. Factors Ergon. Manuf. Serv. Ind., Vol.26 (3) ; pp.316-322
    11. F. Lucidi , A.M. Giannini , R. Sgalla , L. Mallia , A. Devoto , S. Reichmann (2010) Young novice driversubtypes: Relationship to driving violations, errors and lapses., Accid. Anal. Prev., Vol.42 (6) ; pp.1689-1696
    12. A.T. McCartt , J.W. Rohrbaugh , M.C. Hammer , S.Z. Fuller (2000) Factors associated with falling asleep at the wheel among long-distance truck drivers., Accid. Anal. Prev., Vol.32 (4) ; pp.493-504
    13. J. Mesken , T. Lajunen , H. Summala (2002) Interpersonal violations, speeding violations and their relation to accident involvement in Finland., Ergonomics, Vol.45 (7) ; pp.469-483
    14. M. Pylkkönen , M. Sihvola , H.K. Hyvärinen , S. Puttonen , C. Hublin , M. Sallinen (2015) Sleepiness, sleep, and use of sleepiness countermeasures in shiftworking long-haul truck drivers., Accid. Anal. Prev., Vol.80 ; pp.201-210
    15. J.T. Reason , A.S.R. Manstead , S. Stradling , J. Baxter , K. Campbell (1990) Errors and violations on the roads: A real distinction?, Ergonomics, Vol.33 (10-11) ; pp.1315-1332
    16. F.S. Sagberg , G.F.B. Piccinini , J. Engström (2015) A review of research on driving styles and road safety., Hum. Factors, Vol.57 (7) ; pp.1248-1275
    17. X. Shen , Y. Yan , X. Li , C. Xie , L. Wang (2014) Analysis on tank truck accidents involved in road hazardous materials transportation in China., Traffic Inj. Prev., Vol.15 (7) ; pp.762-768
    18. C.K. Smith , J. Williams (2014) Work related injuries in Washington States trucking industry, by industry sector and occupation., Accid. Anal. Prev., Vol.65 ; pp.63-71
    19. S.M. Smith (2015) Workplace hazards of truck drivers, Monthly Labor Review, Available from:, http://www.bls.gov/opub/mlr/2015/article/workplace-hazards-oftruck-drivers-1.htm
    20. M. Starnes (2006) Large-truck crash causation study: An initial overview, NHTSA Technical Report (DOT HS 810 646), Available from:, http://www-nrd.nhtsa.dot.gov/Pubs/810646.pdf
    21. J. Thompson , S. Newnam , M. Stevenson (2015) A model for exploring the relationship between payment structures, fatigue, crash risk, and regulatory response in a heavy-vehicle transport system., Transp. Res. Part A Policy Pract., Vol.82 ; pp.204-215
    22. R. Vivoli , M. Bergomi , S. Rovesti , P. Bussetti , G.M. Guaitoli (2006) Biological and behavioral factors affecting driving safety., J. Prev. Med. Hyg., Vol.47 (2) ; pp.69-73
    23. X. Zhu , S. Srinivasan (2011) A comprehensive analysis of factors influencing the injury severity of large-truck crashes., Accid. Anal. Prev., Vol.43 (1) ; pp.49-57
    오늘하루 팝업창 안보기 닫기