Transport company safety climate—The impact on truck driver behavior and crash involvement

Sullman, Mark J.M.; Stephens, Amanda; Pajo, Karl

doi:10.1080/15389588.2016.1199865

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…Zwar konnte kein direkter Zusammenhang von Sicherheitsklima mit Ausfallzeiten nachgewiesen werden, jedoch hingen sowohl das wahrgenommene Sicherheitsklima innerhalb von Arbeitsgruppen als auch das wahrgenommene gesamtbetriebliche Sicherheitsklima signifikant positiv mit selbstberichteten sicheren Verhaltensweisen im Straßenverkehr zusammen. Ähnliche Ergebnisse zeigte eine vergleichbare Studie aus Neuseeland (Sullman et al 2017).…”

Section: Organisationale Belastungsfaktorenunclassified

Betriebliche Gesundheitsförderung im Verkehrs- und Transportbereich

Brachwitz¹,

Habenicht²,

Ruttke³

et al. 2020

Springer Reference Pflege – Therapie – Gesundheit

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Section: Organisationale Belastungsfaktorenunclassified

Betriebliche Gesundheitsförderung im Verkehrs- und Transportbereich

Brachwitz¹,

Habenicht²,

Ruttke³

et al. 2020

Springer Reference Pflege – Therapie – Gesundheit

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“…Therein a demographic challenge is increasingly obvious: 26.5% of all truck drivers in Germany are more than 55 years old and are therefore expected to retire in the next decade (Statista 2017). This implies that there is pressure for logistics companies to deal with automatisation and increased incentives for young personnel for example via technology implementation (Shankwitz 2017;Sullman, Stephens, and Pajo 2017). This leads to the question addressed in the following section regarding the motivation circumstances of drivers.…”

Section: Background and Datamentioning

confidence: 99%

Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements

Klumpp

2017

International Journal of Logistics Research and Applications

140

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Increasing application areas and depths of autonomous systems in logistics provide a new level of challenge for the analysis and design of human-machine interaction concepts. Due to scarce high-skilled personnel in several regions and the objectives of efficiency and sustainability improvement, logistics operators have to pursue technological progress like automation with all means. In order to distinguish between more or less performing human-artificial collaboration systems in logistics ex ante for investment decision purposes, a multi-dimensional conceptual framework is developed. A comprehensive case study regarding automated truck driving in logistics is provided in order to test the concept concerning practical implications. Results include the notion of four distinctive and increasing resistance levels before finally an efficient 'trusted' collaboration between human operators and artificial intelligence systems can be achieved. This is important for the design of many automated systems in logistics, among others for driving and piloting professions regarding autonomous driving supervision.

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“…However, human error generally tends to be the leading cause of accidents. According to [29], organisational error and an unsafe environment can also significantly affect accidents. These errors include setting unrealistic schedules that force drivers to drive when tired, leading to fatigue [30][31][32][33], pressure to meet tight delivery deadlines, lack of adequate training for drivers [34], and poor maintenance on truck fleets.…”

Section: Introductionmentioning

confidence: 99%

Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models

Waskito,

Bowo,

Kurnia

et al. 2024

Safety

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Truck accidents are a prevalent global issue resulting in substantial economic losses and human lives. One of the principal contributing factors to these accidents is driver error. While analysing human error, it is important to thoroughly examine the truck’s condition, the drivers, external circumstances, the trucking company, and regulatory factors. Therefore, this study aimed to illustrate the application of HFACS (Human Factor Classification System) to examine the causal factors behind the unsafe behaviors of drivers and the resulting accident consequences. Bayesian Network (BN) analysis was adopted to discern the relationships between failure modes within the HFACS framework. The result showed that driver violations had the most significant influence on fatalities and multiple-vehicle accidents. Furthermore, the backward inference with BN showed that the mechanical system malfunction significantly impacts driver operating error. The result of this analysis is valuable for regulators and trucking companies striving to mitigate the occurrence of truck accidents proactively.

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Transport company safety climate—The impact on truck driver behavior and crash involvement

Abstract: This research shows that safety climate may offer an important starting point for interventions aimed at reducing risky driving behavior and thus fewer vehicle collisions.

Cited by 18 publications

References 24 publications

Betriebliche Gesundheitsförderung im Verkehrs- und Transportbereich

Betriebliche Gesundheitsförderung im Verkehrs- und Transportbereich

Automation and artificial intelligence in business logistics systems: human reactions and collaboration requirements

Analysing the Impact of Human Error on the Severity of Truck Accidents through HFACS and Bayesian Network Models

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