Predicting unsafe driving risk among commercial truck drivers using machine learning: Lessons learned from the surveillance of 20 million driving miles

Mehdizadeh, Amir; Yazdi, Mohammad Ali Alamdar; Cai, Miao; Hu, Qiong; Vinel, Alexander; Rigdon, Steven E.; Davis, Karen; Megahed, Fadel M.

doi:10.1016/j.aap.2021.106285

Cited by 15 publications

(23 citation statements)

References 36 publications

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“…53 Linked to the aforementioned, another issue that must be (at least briefly) discussed is the negative directionality of the path between long-haul drivers' age and their selfreported work-traffic crash rate, an aspect that has been raised by previous studies dealing with professional drivers, where the correlation between age and crashes remains negative and statistically significant. In this regard, two issues are worth describing; firstly, that professional drivers' age represents an overall good predictor of their safety-related critical events; 54 and secondly, that other studies predicting risky driving behavior have also consistently found a negative association between age/ driving experience (that tend to be collinear factors) and risky driving behaviors reported by professional drivers. 17 Notwithstanding, it must be also noted that, overall, literature endorses the idea that professional drivers' age plays a "relative" protective role on their traffic safety outcomes, as -linked to the typical psychophysiological impairments of aging -its positive effect tends to decrease by the age of 55-60 years.…”

Section: Discussionmentioning

confidence: 99%

“…Notwithstanding, this does not imply that other factors (eg, infrastructural, weather and vehicle-related issues) do not constitute reliable occupational crash predictors; on the contrary, their relevance has been endorsed by company and crash-record-based studies. 26,54 Therefore, it must be highlighted that other factors could act as complementary predictors influencing work-related fatigue and crashes suffered by long-haul drivers, and this must be considered for interpreting the outcomes of this study.…”

mentioning

confidence: 99%

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More Than Just “Stressful”? Testing the Mediating Role of Fatigue on the Relationship Between Job Stress and Occupational Crashes of Long-Haul Truck Drivers

Useche

Alonso

Cendales

et al. 2021

PRBM

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Introduction: Recent evidence consistently highlights the adverse work environment of long-haul professional drivers, whose task structure typically involves the performance of extensive shifts, driving under stressful working conditions. In this regard, job stress and fatigue -that are highly prevalent in this workforce -seem to play a crucial role in explaining this group's negative traffic safety outcomes. The aim of this study was to assess whether work-related fatigue is a mechanism that mediates the relationship between job stress, health indicators and occupational traffic crashes of long-haul truck drivers (LHTD). Methods: The data used in this study were collected from 521 Spanish long-haul truck drivers (97% males) from all 17 regions of Spain, with a mean age of 47 years. Results: Utilizing structural equation models (SEM), it was found that work-traffic crashes of long-haul truck drivers could be explained through work-related fatigue that exerts a full mediation between job stress (job strain), health-related factors and traffic crashes suffered during the previous two years. Discussion: Overall, the findings of this research support that a) stressful working conditions and health issues of drivers have significant effects on traffic crashes, and b) fatigue is a mechanism relating stress-related factors and work-traffic crashes of long-haul drivers. This study highlights the need of stress-and fatigue-management policies and interventions, in order to reduce the crash risk of long-haul truck drivers.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

More Than Just “Stressful”? Testing the Mediating Role of Fatigue on the Relationship Between Job Stress and Occupational Crashes of Long-Haul Truck Drivers

Useche

Alonso

Cendales

et al. 2021

PRBM

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“…The current literature on intelligent commercial driver-assessment employ different computational and artificial intelligence techniques on driver data that represents the manner by which drivers operate vehicle controls, such as, driving incident data (Figueredo et al [2018], Mase et al [2020a], Agrawal et al [2019], Mehdizadeh et al [2021]) and GPS data ], Hébert et al [2021, Satrawala et al [2022]) or/and a narrow subset of contextual factors , Satrawala et al [2022], Feng et al [2017], Öz et al [2010]). This approach of analysing driving performance and risk using solely driver data could potentially lead to incomplete and unfair assessments as real-world commercial driving is mostly affected by drivers' personal traits and external conditions, and the influence of those contextual factors are not represented in driver data.…”

Section: Intelligent Commercial Driver-assessmentmentioning

confidence: 99%

“…The current literature on intelligence-supported road safety assessment of commercial driving are limited to the manner by which drivers operate vehicle controls (Figueredo et al [2015(Figueredo et al [ , 2018, Mase et al [2020a], Agrawal et al [2019], Mehdizadeh et al [2021], , Hébert et al [2021], Satrawala et al [2022]) and do not consider the impact of inevitable contextual factors on driving performance, such as individual drivers' physical and mental states, weather conditions, traffic conditions, road geometry, road types, and work schedules. These factors influence drivers' responses, and therefore need to be considered to better understand the circumstances that led to a driver's performance or/and produce context-specific driving risk assessments.…”

Section: Introductionmentioning

confidence: 99%

“…Bob was driving in favourable conditions e.g., good weather and no time pressure for delivery, while Alice was driving under poor weather conditions with pressure to deliver on time. Current driver-assessment approaches (such as Mase et al [2020a], Agrawal et al [2019], Mehdizadeh et al [2021], , Hébert et al [2021], Satrawala et al [2022]) fail to include the contextual information and provide the same assessment for both drivers. In this paper, we argue that driving performance and risk in both cases is not the same and that context should be taken into account.…”

Section: Introductionmentioning

confidence: 99%

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Contextually Intelligent Decisions: A Co-designed Approach for Commercial Driving Assessment

Mase

Pekaslan

Agrawal

et al. 2022

Preprint

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Commercial driving is a complex multifaceted task influenced by drivers' personal traits and external contextual factors, such as weather, traffic, and road conditions. Current intelligent commercial driving assessment systems only focus on the manner by which drivers operate vehicle controls when analysing driving performance and the impact of driver behaviour on road safety, potentially producing incomplete and unfair assessments. In this paper, we introduce a stakeholder co-designed approach to complement current driver-assessment systems and provide more context-aware, comprehensive and we hope--fairer assessments of commercial driving. The approach engages key stakeholders in the commercial driving sector to capture the impact of personal traits and external contextual factors on driving performance and provides a systematic approach to embed this knowledge into the assessment of commercial drivers. We demonstrate the effectiveness and utility of the approach by illustrating its end-to-end deployment in the context of heavy goods vehicle driving assessment.

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Recognition and interpretation of aggressive driving behavior for heavy‐duty vehicles based on artificial neural network and SHAP

Cheng,

Chen,

et al. 2023

Hum Ftrs & Erg Mfg Svc

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Aggressive driving significantly impacts traffic safety, and heavy‐duty vehicle drivers are more liable for causing serious crashes. This paper analyzes drivers' aggressive driving behavior from the vehicle type perspective and identifies the influencing factors of aggressive driving behavior through artificial neural network (ANN) and Shapley additive explanations (SHAPs). Using Kaggle's open‐source aggressive driving data, we establish an ANN model to identify driving styles, where road conditions, environmental conditions, and vehicle parameters are independent variables and driving style is a dependent variable. The following measurements, including accuracy, recall, precision, and F1 score, are used to evaluate the model's performance, and the neural network got 85.33%, 82.32%, 84.16%, and 0.8308, respectively. To illustrate the influence of independent variables, the SHAP algorithm is used to analyze the model's feature importance. It was found that illumination and weather conditions influenced the model's performance along with the vehicle length. The number of lanes relates to driving style, and there were more aggressive driving behaviors on two‐lane roads than on single‐lane roads. Besides, heavy‐duty vehicle drivers were more likely to drive aggressively in wet road conditions and indulge in aggressive driving behaviors at night. Particularly, drivers of heavy‐duty vehicles were more likely to drive aggressively, provided that the vehicle in front was also a heavy‐duty vehicle. These findings inform heavy‐duty vehicle drivers to reduce aggressive driving behavior. The information is suitable for inclusion in driver education programs, thus improving traffic safety.

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Predicting unsafe driving risk among commercial truck drivers using machine learning: Lessons learned from the surveillance of 20 million driving miles

Cited by 15 publications

References 36 publications

More Than Just “Stressful”? Testing the Mediating Role of Fatigue on the Relationship Between Job Stress and Occupational Crashes of Long-Haul Truck Drivers

More Than Just “Stressful”? Testing the Mediating Role of Fatigue on the Relationship Between Job Stress and Occupational Crashes of Long-Haul Truck Drivers

Contextually Intelligent Decisions: A Co-designed Approach for Commercial Driving Assessment

Recognition and interpretation of aggressive driving behavior for heavy‐duty vehicles based on artificial neural network and SHAP

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