Transportation, Warehousing, and Utilities

Participating core and specialty programs: Center for Maritime Safety and Health Studies, Center for Motor Vehicle Safety, Center for Occupational Robotics Research, Emergency Preparedness and Response, Exposure Assessment,Occupational Health Equity, Prevention through Design, Safe●Skilled●Ready Workforce, and Surveillance.

Employers, insurers (including workers’ compensation), standard setting bodies, other government agencies, manufacturers, professional associations, and labor organizations use NIOSH information to reduce transportation incidents and related injuries among transportation, warehousing, and utilities workers.

NOTE: Goals in bold in the table below are priorities for extramural research.

  Health Outcome Research Focus Worker Population Research Type
A Fatal and non-fatal injuries Role of work organization (e.g., fatigue, sleep, stress, hours of service, commuting, non-standard work arrangements, distraction) Truck drivers, bus and transit (e.g., taxi) drivers, maritime workers, couriers and messengers, utilities workers, aviation workers Basic/etiologic
B Fatal and non-fatal injuries Develop evidence-based interventions (e.g., fleet management, administrative controls) Truck drivers, bus and transit (e.g., taxi) drivers, aviation workers, maritime workers InterventionTranslation
C Fatal and non-fatal injuries Vehicle design and technology (e.g., highly automated vehicles, connected vehicles, advanced driver assistance systems) Truck drivers, bus and transit (e.g., taxi) drivers Basic/etiologic

Intervention

Activity Goal 6.14.1 (Basic/Etiologic Research): Conduct basic/etiologic research to better understand the relationships between work organization factors and vehicle design and technology and transportation incidents and related injuries involving transportation workers.

Activity Goal 6.14.2 (Intervention Research): Conduct studies to develop and assess the effectiveness of interventions to prevent transportation incidents and related injuries involving transportation workers.

Activity Goal 6.14.3 (Translation Research): Conduct translation research to understand barriers and aids to implementing effective interventions to prevent fatal and non-fatal injuries due to transportation incidents and related injuries involving transportation workers.

Burden

In 2015, 29% (615) of all transportation incidents occurred in the transportation, warehousing and utility (TWU) sector, which was the highest percentage among any industry sector. Transportation related incidents represent a high proportion of fatalities in all TWU industry sub-sectors, with the greatest burden in the truck transportation sub-sector at 83% (454 of 546) [BLS 2017a]. The rate of non-fatal days away from work transportation incidents in Transportation and Warehousing was 26.5 per 10,000 workers, 5.5 times the rate for all private industry sectors [BLS 2017b].

The organizational structure of work in the TWU sector (e.g., long hours of work, irregular work schedules, non-standard work arrangements, time pressures, long periods away from home, commuting distances, and pay-by-the-mile compensation) can increase work stress and exacerbate risk factors associated with work-related transportation incidents (e.g., fatigue and distraction) [Härmä et al. 2008; NIOSH 2013]. Fatigue is associated with vehicle crashes and disturbances to cognition [Åkerstedt 2000; Marcus and Rosekind 2016; FMCSA 2007]. Highly-automated vehicles hold great promise for reducing these transportation incidents, but fully-automated vehicles will not become commonplace for 20 to 30 years [IIHS 2016], and the next decade is likely to involve a mix of vehicles with varying levels of automation and advanced driver assistance systems. There are questions about the safety of drivers of motor vehicles and navigators of planes and ships in this rapidly-changing transportation environment.

Need

For TWU workers, research is needed to better characterize individual-level crash risk factors and adverse incident factors such as fatigued and distracted driving or navigating. Organizational-level factors such as fleet management, journey management, shift work, training, safety climate, job demands and design, employment arrangements, pay structures, and safety management systems should be considered. Research is needed to characterize the effects of off-the-job factors such as sleep hygiene and health status on work-related driving and navigational safety, and to understand the interrelationship between off-the-job driving (e.g., “mega commutes”) and on-the-job factors (e.g., company driving and crew transportation policies) and motor vehicle safety. For automated vehicles, research is needed to assess the effectiveness of currently-available advanced driver assistance systems in vehicles used by TWU workers and to determine the safety consequences of operating TWU vehicles in an increasingly automated road environment. Evaluations of interventions, including vehicle design, technology, company practices, and laws are needed to focus implementation efforts on those which are most effective.

Manufacturers, employers, standard setting bodies, other government agencies, professional associations, and labor organizations use NIOSH information to reduce machine-related injuries among transportation, warehousing, and utilities workers.

NOTE: Goals in bold in the table below are priorities for extramural research.

  Health Outcome Research Focus Worker Population Research Type
A Fatal and non-fatal injuries Machine-related injuries (e.g., caught-in, struck-by) Aviation, warehousing, and maritime workers Intervention
B Fatal and non-fatal injuries Machine-related injuries (e.g., caught-between, struck-by) Aviation workers Translation
C Fatal and non-fatal injuries Use of collaborative and mobile robotics Warehousing, utilities, maritime, and transit (e.g., taxi drivers) workers Surveillance research

Basic/etiologic

D Fatal and non-fatal injuries Use of stationary robots Warehousing, utilities, and maritime workers Surveillance researchTranslation

Activity Goal 6.15.1 (Basic/Etiologic Research): Conduct basic/etiologic research to better understand relationship between collaborative and mobile robots and fatal and non-fatal injuries among transportation, warehousing, and utilities workers.

Activity Goal 6.15.2 (Intervention Research): Conduct studies to develop and assess the effectiveness of interventions for machine-related injuries among transportation and warehousing workers.

Activity Goal 6.15.3 (Translation Research): Conduct translation research to understand barriers and aids to implementing effective interventions to prevent machine related injuries among aviation workers and injuries related to traditional robots among transportation, warehousing, and utilities workers.

Activity Goal 6.15.4 (Surveillance Research): Conduct surveillance research to develop new tools and methods for collecting data on injuries related to robots in the transportation, warehousing, and utilities sector.

Burden

Interactions between workers and machines in transportation, warehousing and utilities (TWU) have been beneficial to the employer and worker by reducing work load and increasing production capabilities. But with these efficiencies have come worker injuries and fatalities. In 2015 in the TWU sector there were 654 vehicle and machine-related fatalities [BLS 2017c]. Heavy and tractor-trailer truck drivers and material moving workers are the groups with leading counts of vehicle and machine-related fatalities [BLS 2017d].

The International Federation of Robotics reports sharp increases in sales, and is projecting that a new type of robot, collaborative robots that work alongside and in conjunction with human workers, will have a market breakthrough in the next several years [IFR 2016]. As robotics and automation integrate into the TWU sector, workers are being tasked with working with these complex systems, such as, ship control systems, automated forklifts and picking machines, and use of drones in warehousing and utilities [Volpe 2012; Banker 2016; Schneider & Deml 2017]. Introduction of these highly automated systems has the potential to improve safety in many areas, but there are increased risks. These systems are highly complicated and more emphasis needs to be placed on operator training and maintenance [Moniz and Krings 2016]. Changes in the roles and responsibilities of the operator introduce increased risk of operator errors especially in the context of unforeseen or atypical events. The current faster pace of technology introduction increases the potential for unforeseen hazards being introduced in the workplace.

Need

Current injury statistics illustrate the need for continued research on the human/machine interface for machines used in today’s workplace, and this research will need to be expanded to address future machines and vehicles. Researchers can provide tools to mitigate these hazards, and reduce injuries and fatalities, through hazard identification strategies and hazard mitigation methods, human factors analysis, educational programs on human factors engineering elements for system design for engineers, and integration of human factors engineering principles in technical engineering and design standards [Leva et. al. 2016; Murashov et. al. 2016]. Because robotics and automation are relatively new to the TWU sector, current surveillance systems do not provide readily available data or a real mechanism to tease out the injury and fatality events associated with robotics and automation. Research is needed to identify data for emerging machines including robots and automation, develop and evaluate surveillance tools, and identify emerging safety problems and risk factors. Research is also needed to develop and improve safety engineering features for new types of human-machine interfaces including robots and automated machines. Research should aim to identify safe and effective human-machine interface designs, develop and improve training for human workers, and evaluate and improve standards and policy.

Åkerstedt T [2000]. Consensus statement: Fatigue and accidents in transport operations. J Sleep Res 9(4):395.

Banker S [2016]. Robots in the warehouse: It’s not just Amazon. Forbes: Logistics & Transportation, January 11, https://www.forbes.com/sites/stevebanker/2016/01/11/robots-in-the-warehouse-its-not-just-amazon/#1c675ce740b8External

BLS [2017a]. Workplace injuries, Occupational Injuries and Illnesses and Fatal Injuries Profiles. Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics, https://www.bls.gov/data/External

BLS [2017b]. Table R8. Incidence rates for non-fatal occupational injuries and illnesses involving days away from work per 10,000 full-time workers by industry and selected events or exposures leading to injury or illness, private industry, 2015. Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics, https://stats.bls.gov/iif/oshwc/osh/case/ostb4760.pdfCdc-pdfExternal

BLS [2017c]. Table A-1. Fatal occupational injuries by industry and event or exposure, all United States, 2015, https://www.bls.gov/iif/oshcfoi1.htmExternal.

BLS [2017d]. Table A-5. Fatal occupational injuries by occupation and event or exposure, all United States, 2015, https://www.bls.gov/iif/oshcfoi1.htmExternal.

FMCSA (Federal Motor Carrier Safety Administration) [2007]. The Large truck crash causation study – analysis brief. Publication No. FMCSA-RRA-07-017. Washington, DC: US, Department of Transportation, Federal Motor Carrier Safety Administration https://www.fmcsa.dot.gov/safety/research-and-analysis/large-truck-crash-causation-study-analysis-briefExternal

Härmä M, Partinen M, Repo R, Sorsa M, Siivonen P [2008]. Effects of 6/6 and 4/8 watch systems on sleepiness among bridge officers. Chronobiol Int 25(2-3):413-423. doi: 10.1080/07420520802106769.

IFR (International Federation of Robotics) [2016]. Executive summary world robotics 2016 industrial robots. Frankfurt, Germany: International Federation of Robotics, https://ifr.org/img/uploads/Executive_Summary_WR_Industrial_Robots_20161.pdfCdc-pdfExternal

IIHS (Insurance Institute for Highway Safety) [2016]. Robot cars won’t retire crash-test dummies anytime soon. Status Report 51(8), November 10. http://www.iihs.org/iihs/sr/statusreport/article/51/8/1External

Leva MC, Naghdali F, Alunn C [2016]. Human factors engineering in system design: a roadmap for improvement. The Fourth International Conference on Through-life Engineering Services, Cranfield, UK, November 3-4, http://www.through-life-engineering-services.org/index.php/tesconf/past/tesconf-2015External

Marcus JH, Rosekind MR [2016]. Fatigue in transportation: NTSB investigations and safety recommendations. Injury Prev. doi: 10.1136/injuryprev-2015-041791. Advance online publication.

Moniz AB, Krings BJ. [2016]. Robots working with humans or humans working with robots? searching for social dimensions in new human-robot interaction in industry. Societies 6(23):1-21.

Murashov V, Hearl F, Howard J [2016]. Working safely with robot workers: recommendations for the new workplace. J Occ Env Hyg 13(3):D61-D71.

NIOSH [2013]. National Health Interview Survey: Occupational Health Supplement- Transportation, Warehousing, and Utilities. Cincinnati, OH: US, Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, https://www.cdc.gov/niosh/topics/nhis/transind.html

Schneider M, Deml B. [2017]. Analysis of a multimodal human-robot-interface in terms of mental workload. In: Schlick C. et al. (eds) Advances in ergonomic design of systems, products and processes. Berlin: Springer.

Volpe National Transportation Systems Center [2012]. Automation and the human: intended and unintended consequences. Transportation challenges and opportunities: a colloquia series. Cambridge, MA: U.S. Department of Transportation, VOPLE National Transportation Systems Center.

Page last reviewed: April 24, 2018