The big rig Viscelli, Steve
2016., 20160412, 2016, 2016-04-12
eBook
Long-haul trucks have been described as sweatshops on wheels. The typical long-haul trucker works the equivalent of two full-time jobs, often for little more than minimum wage. But it wasn’t always ...this way. Trucking used to be one of the best working-class jobs in the United States. The Big Rig explains how this massive degradation in the quality of work has occurred, and how companies achieve a compliant and dedicated workforce despite it. Drawing on more than 100 in-depth interviews and years of extensive observation, including six months training and working as a long-haul trucker, Viscelli explains in detail how labor is recruited, trained, and used in the industry. He then shows how inexperienced workers are convinced to lease a truck and to work as independent contractors. He explains how deregulation and collective action by employers transformed trucking’s labor markets--once dominated by the largest and most powerful union in US history--into an important example of the costs of contemporary labor markets for workers and the general public.
•According to police officers, mobile phone use is under-reported in traffic accident records.•Drug and alcohol use may also be under-reported in road accident records.•Some factors contributing to ...accidents (e.g., inattention, dangerous driving) are not included in reporting procedures.•Police officer’s views about accident causation may influence their memory recall.
What are the main contributing factors to road accidents? Factors such as inexperience, lack of skill, and risk-taking behaviors have been associated with the collisions of young drivers. In contrast, visual, cognitive, and mobility impairment have been associated with the collisions of older drivers. We investigated the main causes of road accidents by drawing on multiple sources: expert views of police officers, lay views of the driving public, and official road accident records. In Studies 1 and 2, police officers and the public were asked about the typical causes of road traffic collisions using hypothetical accident scenarios. In Study 3, we investigated whether the views of police officers and the public about accident causation influence their recall accuracy for factors reported to contribute to hypothetical road accidents. The results show that both expert views of police officers and lay views of the driving public closely approximated the typical factors associated with the collisions of young and older drivers, as determined from official accident records. The results also reveal potential underreporting of factors in existing accident records, identifying possible inadequacies in law enforcement practices for investigating driver distraction, drug and alcohol impairment, and uncorrected or defective eyesight. Our investigation also highlights a need for accident report forms to be continuously reviewed and updated to ensure that contributing factor lists reflect the full range of factors that contribute to road accidents. Finally, the views held by police officers and the public on accident causation influenced their memory recall of factors involved in hypothetical scenarios. These findings indicate that delay in completing accident report forms should be minimised, possibly by use of mobile reporting devices at the accident scene.
Advanced technologies are constantly emerging in automobile industry, which not only provides drivers with a comfortable driving experience, but also enhances the safety of passengers. However, there ...are still some security issues need to be solved in automobiles, such as automobile driver fingerprinting. At present, identification technologies, such as fingerprint recognition and iris recognition, cannot monitor the driver's identity in real-time manner. Therefore, it is of great significance to design a real-time automobile driver fingerprinting scheme to ensure the safety of people's properties and even lives. Different from previous work concerning automobile driver fingerprinting, in this article, we conduct a comprehensive study on behavioral characteristics of drivers in two vehicles, namely Luxgen U5 SUV and Buick Regal. We exploit the actual data of the controller area network to construct a driver identity comparison library by extracting and processing the feature data. Then, we construct a combined model based on convolutional neural network and support vector domain description to achieve efficient automobile driver fingerprinting. Extensive experimental results show that the proposed driver fingerprinting scheme can dynamically match the driver's identity in real time without affecting the normal driving.
Impact of driver characteristics (= traits) on driving behavior dimensions.
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•A driving simulator study was carried out to investigate correlations between driver characteristics, ...driver personality and driving behavior.•Several driver characteristics, such as age, gender and sensation seeking, were significantly correlated with driving behavior.•A principal component analysis was run and three dimensions for clustering relevant driving behavior parameters were identified: Speed and Cruise Control, Dynamics and Driver Performance.•A structural path model and a mathematical approach for the integration of driver individuality into a cognitive driver behavior model are presented.
This paper focusses on the role of driver individuality in the field of cognitive driver behavior modeling for the prospective safety impact assessment of advanced driver assistance systems (ADAS) and automated driving functions. Virtual traffic simulation requires valid models for the environment, the vehicle and the driver. Especially modeling human driver behavior is a major challenge, which in recent years has already led to the development of various driver models for the purpose of virtual simulation. Modeling human behavior in traffic with a precise representation of human cognition, capability and individuality, are crucial demands, which require thorough investigation and understanding of the human driver. Current driver behavior models often leave aside the aspect of driver individuality and lack the consideration of differences in driving behavior between different drivers. To take into account all the aspects from complex human cognitive processes to individual differences in action implementation, the Stochastic Cognitive Model (SCM) was developed. The SCM is based on five subcomponents: gaze control, information acquisition, mental model, action manager and situation manager (=decision making process) and action implementation. The aim of the present study is to provide a basis for establishing a solid logic for the integration of driver individuality into the current structure of the SCM by creating a new submodule that takes into account several behavior affecting driver characteristics. This subcomponent controls the stochastic variance in several driver behavior parameters, such as velocity or comfort longitudinal acceleration. In a representative driving simulator study with 43 participants, driver behavior on the highway was investigated and thoroughly analyzed. Information about several relevant driver characteristics and personality traits of the participants was collected and a logical hierarchical model was set up to cluster several dependent and independent variables into four layers: independent manifest driver variables, such as age or gender (Level 1), latent driver personality factors, such as thrill seeking or anxiety (Level 2), driver behavior dimensions, such as dynamics and law conformity (Level 3), and various dependent driver behavior parameters, such as velocity, acceleration or speed limit violation (Level 4). Multiple linear regression analyses were run to find the individual driver characteristics and personality traits, by which most of the stochastic variance in the measured driver behavior parameters can be explained. Subsequently, a principal component analysis (PCA) was run to test, if the previously clustered driver behavior parameters were loading on the presumed behavioral dimensions on the third level of the model to identify significant components of driver behavior, such as dynamics or law conformity. Results of the present study show significant correlations between driver characteristics and driver behavior parameters. According to the results of the PCA, variability in driver behavior can be explained to a great extent by three largely independent components: (1) Speed and cruise control, (2) Dynamics and (3) Driver performance. With the consideration of driver individuality in driver behavior models for the agent-based traffic simulation, validity of the results from prospective safety impact assessment analyses of automated driving functions can be enhanced. Beyond that, the findings of the current study can be used as a solid basis for the development of adaptive functions in the field of vehicle automation, considering the different driving skills and preferences of drivers with different individual profiles.
•Risky driving behaviours are compared against smartphone-use behaviours while driving.•A representative sample of 700 German Young Novice Drivers (YNDs) was used.•An updated German version of the ...Behaviour of Young Novice Drivers Scale (BYNDS) was applied.•A group of high-risk ‘problem’ young drivers was identified.•Policy-relevant suggestions are provided on the issue of smartphone use while driving.
Road traffic collisions are the leading cause of death for those between the ages of 15–29, according to the World Health Organisation. This study investigates one of the primary reasons for the high fatality rate amongst Young Novice Drivers (YNDs) – their use of smartphones while driving. We gathered responses from a representative sample of YNDs on their behaviour while driving using an updated version of the ‘Behaviour of Young Novice Drivers Scale’. Survey responses totalled 700 YNDs situated throughout Germany. From these responses, we examined the prevalence of certain driving behaviours that are described as ‘distracting’ and compared these driving behaviours to the respondents’ use of specific smartphone features. The responses report that music-related activities (e.g. changing music on a smartphone) are most common amongst YNDs. Speaking on the phone is seldom-reported, although more males than females indicated engagement in this behaviour. We further carried out a correlation analysis and correspondence analysis. On that basis we found that those who report speaking on a smartphone are significantly more likely to engage in driving behaviours with potentially fatal consequences, such as speeding and driving while impaired by prohibited substances (drugs, alcohol). We propose that the results could be used by policymakers for public information implications and to tailor financial penalties for those engaging in smartphone behaviours that are linked to harmful driving behaviours. In addition, our findings can also be used in a Usage-based Insurance (UBI) context to financially incentivise safer driving.
•Human errors cause the majority of road crashes, leading to deaths and injuries.•The provision of timely feedback has a positive impact on driving behaviour.•A new methodology based on Long ...Short-Term Memory neural networks is presented.•The method identifies behavioural change using in-vehicle telematics technology.•Feedback may prevent internalisation of new, risky habits and reduce crash risk.
Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result.
Acceptance of new technology and systems by drivers is an important area of concern to governments, automotive manufacturers and equipment suppliers, especially technology that has significant ...potential to enhance safety. To be acceptable, new technology must be useful and satisfying to use. If not, drivers will not want to have it, in which case it will never achieve the intended safety benefit. Even if they have the technology, drivers may not use it if it is deemed unacceptable, or may not use it in the manner intended by the designer. At worst, they may seek to disable it.
•Acceptance for automated technology increases following on-road experience.•Engagement is lower for partially automated driving than manual driving.•The design of automated vehicle interfaces can ...cause driver confusion between modes.•Drivers use strategies to counter hands-on steering wheel sensors.•Complacent behaviours and attitudes threaten the safe use of automated vehicles.
Contextual investigations of automated vehicle technology have so far been rare, however they are crucial to uncover the challenges that exist around its acceptance and safe use. Twenty-one drivers used a partially automated vehicle on a public highway in unaltered traffic conditions, while their behaviour was observed. Subjective measures of technology acceptance were taken before and after the drive, as well as post-drive ratings of engagement, workload, and perceived safety for manual versus automated driving. Post-drive interviews were conducted to better understand participants’ attitudes and their behaviours that were observed. Technology acceptance was higher following the drive as indicated by increased trust and perceived safety. Engagement was lower for automated driving, while no differences were reported for workload or safety between the driving modes. Perceptions of level-2 technology were highly positive, though complacent attitudes and behaviours were apparent. There was dissatisfaction with the hands-on steering wheel sensor, and observed behaviours indicate that it fails to adequately measure driver engagement. Many unintentional automation disengagements occurred due to problems interacting with the physical controls. Concerningly, this sometimes led to situations where the participant incorrectly thought they were still in automated mode, but were in fact responsible for primary driving tasks. Insufficient cues to inform the driver of mode changes and the current automation status contributed to the mode confusion observed. These findings can help inform the design of automated vehicle technology as well as policies and regulations, to ensure the inherent risks in partial driving automation can be managed.
Anthropogenic Drivers of Ecosystem Change Nelson, Gerald C.; Bennett, Elena; Berhe, Asmeret A. ...
Ecology and society,
12/2006, Volume:
11, Issue:
2
Journal Article
Peer reviewed
Open access
This paper provides an overview of what the Millennium Ecosystem Assessment (MA) calls “indirect and direct drivers” of change in ecosystem services at a global level. The MA definition of a driver ...is any natural or human-induced factor that directly or indirectly causes a change in an ecosystem. A direct driver unequivocally influences ecosystem processes. An indirect driver operates more diffusely by altering one or more direct drivers. Global driving forces are categorized as demographic, economic, sociopolitical, cultural and religious, scientific and technological, and physical and biological. Drivers in all categories other than physical and biological are considered indirect. Important direct drivers include changes in climate, plant nutrient use, land conversion, and diseases and invasive species. This paper does not discuss natural drivers such as climate variability, extreme weather events, or volcanic eruptions.
•Body clock, sleep and work factors have an impact on driver’s fatigue.•Drivers working over legal limit is 3 times more likely to sleep less than 6 h/24 h.•If sleep less than 6 h likely to have poor ...quality of sleep is 8 times higher.•Drivers between 45 and 65 years make up significantly more exceeding the driving time.•Time of going to sleep does not affect the quality of sleep.
All around the world numerous studies have been carried out and indicated that 20–50% of commercial vehicle accidents occur because of fatigue. Professional drivers represent an important category of drivers who are present in traffic on a daily basis transporting passengers or goods and their responsibility is at a very high level. These drivers are most exposed to the impact of fatigue. The review of the literature has provided three main factors which can influence the onset of fatigue: sleep factors, work factors, health factors. The main aim of this study was to determine the influence of the three main factors of fatigue between bus and truck drivers in the Republic of Serbia.
The survey has been conducted among bus and truck drivers who are employed in transportation companies across the Republic of Serbia. The research consists of collecting and analyzing bus and truck drivers’ answers according to the above mentioned factors which influence the occurrence of fatigue.
In this study we have found that circadian rhythm, sleep and work factors have an impact on drivers’ fatigue. On the other side, time of going to sleep has no impact on the quality of sleep and on fatigue. The results show that if the drivers work over the legal limit, they are 3 times more likely to sleep less than 6 h in 24 h and if they sleep less than 6 h, it is likely that the poor quality of their sleep will be 8 times higher. The poor quality of sleep reduces driver performance, and therefore increases the risk of accidents.
2 of 3 investigated factors have an impact on the occurrence of fatigue. The third factor, health factor, should be examined in more detail, and other elements should be analysed in order to determine their influence on the fatigue.
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