Driver Perception Research Articles

Diagnosis of perception of drivers of deforestation using the partial least squares path modeling approach

Deforestation is recking havoc on the world's forests, leading to the widespread depletion of forest biodiversity and ecosystem services, as well as eventual forest cover loss. Ghana is one of the highest deforestation rated countries in the World. Despite efforts of the Forestry Commission and other private organizations, the country's protected forest infrastructure continues to deteriorate. This is because policymakers have not examined the interrelation of the causes of deforestation. The study examines the interrelation among factors affecting deforestation in the Sefwi-Wiawso Forest District (SWFD) of Ghana. Partial least squares (PLS) approach was used to identify the direct and indirect effects of the factors of deforestation and assess’ the relationships among the perceived causes of deforestation. The model showed that, Mining activities, Conflict over ownership rights, Illegal logging and Agricultural activities had positive direct impacts on Deforestation. On the contrary, Population growth, Knowledge on forest resources and Policy and enforcement had negative indirect impacts on Deforestation. Again, Population growth, policy and enforcement, and illegal logging were significant in predicting deforestation. As a result, the study propounds that, to effectively protect our forest and achieve Sustainable Development Goal 15, it is critical to address the direct effects of the variables influencing deforestation.

Motion detection and classification: ultra-fast road user detection

With the emerge of intelligent and connected transportation systems, driver perception and on-board safety systems could be extended with roadside camera units. Computer vision can be utilised to detect road users, conveying their presence to vehicles that cannot perceive them. However, accurate object detection algorithms are typically computationally heavy, depending on delay-prone cloud computation or expensive local hardware. Similar problems are faced in many intelligent transportation applications, in which road users are detected with a roadside camera. We propose utilising Motion Detection and Classification (MoDeCla) for road user detection. The approach is computationally lightweight and capable of running in real-time on an inexpensive single-board computer. To validate the applicability of MoDeCla in intelligent transportation applications, a detection benchmark was carried out on manually labelled data gathered from surveillance cameras overseeing urban areas in Espoo, Finland. Separate datasets were gathered during winter and summer, enabling comparison of the detectors in significantly different weather conditions. Compared to state-of-the-art object detectors, MoDeCla performed detection an order of magnitude faster, yet achieved similar accuracy. The most impactful deficiency of MoDeCla was errors in bounding box placement. Car headlights and long dark shadows were found especially difficult for the motion detection, which caused incorrect bounding boxes. Future improvements are also required for separately detecting overlapping road users.

The perception of public transport drivers (PTDs) on preventing road traffic injury (RTIs) in Vanuatu: a qualitative study

ABSTRACT Purpose Road Traffic Injury (RTI) is major public health concern globally and is excessively affecting vulnerable road users in the pacific Island nations. This study aimed to explore and understand the perception of Public Transport Drivers (PTDs) on risk factors and the existing prevention strategy of RTI in Vanuatu. Methods This study employed qualitative methods that used 31 In-Depth Interviews (IDIs) to gather data from PTDs from 14 October to 30 November 2020. Purposive sampling was used to recruit PTDs from three main municipalities, Luganville, Port Vila and Lenakel. Semi-structured open-ended questionnaire were used to gather data. Interview data was transcribed and manual thematic analysis was performed to analysis the data. Results Data saturation was reach from interviewing 31 PTDs who were all male. Five main themes were generated from the study including the trend of RTI, the determinants of RTI, high-risk road users, traffic law enforcement and public education. The respondents perceived that the best measures for preventing RTI include community education, enforcement of road traffic control laws and addressing specific road infrastructure issues. Conclusions Changing driver behaviours, community education on road safety and enforcement of traffic laws are essential for preventing RTI in Vanuatu.

Trucking in the Era of COVID-19

COVID-19 resulted in health and logistical challenges for many sectors of the American economy, including the trucking industry. This study examined how the pandemic impacted the trucking industry, focused on the pandemic’s impacts on company operations, health, and stress of trucking industry employees. Data were collected from three sources: surveys, focus groups, and social media posts. Individuals at multiple organizational levels of trucking companies (i.e., supervisors, upper-level management, and drivers) completed an online survey and participated in online focus groups. Data from focus groups were coded using a thematic analysis approach. Publicly available social media posts from Twitter were analyzed using a sentiment analysis framework to assess changes in public sentiment about the trucking industry pre- and during-COVID-19. Two themes emerged from the focus groups: (1) trucking company business strategies and adaptations and (2) truck driver experiences and workplace safety. Participants reported supply chain disruptions and new consumer buying trends as having larger industry-wide impacts. Company adaptability emerged due to freight variability, leading organizations to pivot business models and create solutions to reduce operational costs. Companies responded to COVID-19 by accommodating employees’ concerns and implementing safety measures. Truck drivers noted an increase in positive public perception of truck drivers, but job quality factors worsened due to closed amenities and decreased social interaction. Social media sentiment analysis also illustrated an increase in positive public sentiment towards the trucking industry during COVID-19. The pandemic resulted in multi-level economic, health, and social impacts on the trucking industry, which included economic impacts on companies and economic, social and health impacts on employees within the industry levels. Further research can expand on this study to provide an understanding of the long-term impacts of the pandemic on the trucking industry companies within the industry and segments of the trucking industry workforce.

Truckers’ perceptions on truck regulations and policies in a developing country: A case study of Metro Manila, Philippines

Challenges in truck operation within metropolitan areas encourage the government to develop policies and strategies to address issues on truck traffic congestion and accidents. Given the need for making proactive policies, it is indispensable to understand truck drivers’ perceptions as a behavioral intervention measure. This paper examined the perception of truck drivers regarding issues on truck operation and policies, including their preferences on truck lane management and truck ban policies in Metro Manila, Philippines. The study surveyed 390 truckers in the North and South Harbor of Manila in July and August 2018. The survey results showed that most truck drivers have experienced difficulty with the existing truck ban and one truck lane policy in Metro Manila. Most truck drivers preferred two middle lanes for trucks, and truck ban time should be implemented during morning peak hours only. The willingness-to-pay attitude by truck drivers indicated a strong relationship with travel time-savings. Policy intervention should weigh up the perceptions of the stakeholders to provide a holistic approach and satisfactory truck management performance. This study would interest transport planners and policy developers as the findings can be applied to deal with truck operation issues.

Determining driver perceptions about distractions and modeling their effects on driving behavior at different age groups

This study was aimed at determining the driving distractions which are perceived most hazardous and determining the effects of these distractions and age on speed and headway. A questionnaire survey was done to find out the opinion of drivers related to the most hazardous distraction. 639 responses were collected in the survey which were used to determine the top-rated distractions for drivers in Bahrain. Roadside observations were taken to observe the speed, headway, age and type of distraction for the driver. Speed was observed for 48 drivers while headway was observed for 36 drivers along with other parameters. The most hazardous distractions, according to the participants of the questionnaire, are using mobile phones, handling children, and accidents or incidents on the road. Further, the results of the two-way analysis of variance (ANOVA) test and regression analysis demonstrated that using mobile phones and age have a significant effect on both speed and headway. Speed tends to decrease with distraction for all age groups while decreasing the headway for young and middle-aged drivers. The effect of distraction is higher than the effect of age on speed, as well as headway. Texting has the most significant effect among distractions on headway. It is hereby recommended that policymakers should focus on increasing awareness and stringent law enforcement related to handling mobile phones and children, especially for young and middle-aged drivers.

A method to improve the hazard perception of young novice drivers based on Bandura’s observational learning theory: Supplement to expert commentary training

Compared with experienced drivers, young novice drivers are more likely to have traffic accidents. The main reasons are that they lack experience, their hazard perception is weak, and their visual search strategy is underdeveloped. Research shows that hazard perception training can improve the visual search strategy of young novice drivers and enhance their hazard perception ability. We propose that a driver's visual search behaviour, like any other action, can be developed by observing the behaviour of a role model. In an experiment based on a driving simulator, we clustered the visual search paths of 20 experienced drivers, selected the drivers with the best visual search behaviour, and obtained visual search path video footage to produce hazard perception training videos. Using these videos, we applied Bandura’s observation learning theory to train 20 young novice drivers. We call this approach “training based on experienced drivers' performance”. To determine the effects of training based on experienced drivers' performance, 20 young novice drivers were given the usual expert commentary training, and 20 young drivers were given no training. The results showed that training based on experienced drivers' performance and expert commentary training had positive effects on the average speed of young novice drivers through hazard sections. Compared with untrained young novice drivers, trained young novice drivers decreased their speed in response to dangerous road sections. Compared with young novice drivers who received expert commentary training and those who did not receive training, young novice drivers who received training based on experienced drivers' performance showed significant improvements in visual search. These results suggest that training based on experienced drivers' performance can help young novice drivers improve their hazard perception, especially in visual aspects. This training method can complement existing training methods for young novice drivers.

Uber drivers perception about their work and health conditions in the context of covid-19 in Brazil

Uber drivers perception about their work and health conditions in the context of covid-19 in Brazil

Innovative Framework for Distracted-Driving Alert System Based on Deep Learning

Distracted driving is the most common cause of traffic accidents. According to a World Health Organization report, the number of traffic accidents has been increasing in recent years. To address this issue, distracted-driving recognition is an important area of traffic safety research. However, distracted behavior may be a part of a driver’s regular tasks. For example, sometimes a delivery person must use his/her phone while driving. The use of walkie-talkies is required for container-truck drivers because they improve unloading efficiency and reduce the time cargo ships spend in a port, resulting in cost savings. While driving on the highway, it is sometimes necessary to tune the radio to receive an update on road conditions. Furthermore, drinking water is permitted while waiting for a traffic signal for an extended period of time. Therefore, for the distracted-driving alert system, the driving scenario is important. To address this issue, we present a novel framework herein that combines driving perception and driver behavior recognition to provide the driver with appropriate warnings. By combining driver perception and behavior recognition, our proposed framework can reduce false alerts. We also define different time-to-collision standards to achieve humane and effective warnings. We try to study various behaviors and define various time-to-collision standards for making safety-level decisions. For driving perception and driver behavior recognition, a modified convolutional neural network is used, which alerts the driver immediately.

Effect of Lane Narrowing Implemented on an Urban Dual Carriageway on Speed Reduction and Acoustic Environment

The on-going growth in the use of motor vehicles leads to a number of traffic safety problems. Therefore, various traffic calming measures are applied to increase traffic safety in built-up areas. Lane narrowing is one of the techniques used to influence the perception of drivers and make them slow down. With this in mind, the authors conducted traffic volume and speed counts before and after installation of the lane narrowing treatment, covering 100 passing vehicles, at an intersection between an urban dual carriageway and an estate street where over a dozen fatal accidents occurred in the recent years. Traffic noise was also measured, both before and after lane narrowing. The traffic count data were analysed to show speed reduction by ca. 15 km/h, yet in all cases still above 50 km/h, which is the speed limit applicable to built-up areas in Poland. The analysis of the “before” and “after” noise maps showed only minor changes to the distribution of noise. The locations of these changes to the acoustic environment depended on the speed and volume of traffic and, much more importantly, on the topography of the surrounding area, the existing roadside layered landscape and the type of planted vegetation. The detailed analyses of the analysed parameters demonstrated that sustainable design of traffic calming measures on urban dual carriageways must consider not only the desired speed reduction but also the surrounding landscape and the local topography.

Assessment of the effect of passenger car wheel unbalance on driving comfort

This paper presents the results of experimental investigations of the effects of car wheel unbalance on driving safety and comfort. Basic information about types of wheel unbalance, their causes, and effects are included. The test subject was a BMW 3 Series car with rear-wheel drive. A specific unbalance was introduced on the front steered wheels. The vehicle was driven in a straight line on an asphalt road in good condition at speeds between 70 km/h and 140 km/h. During the test runs, acceleration waveforms were recorded from sensors placed on the lower control arm, driver's seat, and steering wheel. The vibration level of the unbalanced wheel increases with the driving speed and with the increase in unbalance. The highest increase in vibration amplitude occurred on the steering wheel at speeds between 100 km/h and 120 km/h. These vibrations have a direct effect on the driver. This is evidenced by negative driver perceptions such as fatigue and driving discomfort. This was also confirmed by the calculated vibration exposure levels. Driving with unbalanced wheels accelerates wear on the tyres, steering, drive, and suspension components of the vehicle.

The Determinants of Consumer Acceptance of Autonomous Vehicles: A Case Study in Riyadh, Saudi Arabia

ABSTRACT The factors affecting the acceptance of Autonomous Vehicles (AVs) in Saudi Arabia were examined by conducting a stated choice survey among 500 participants. Descriptive analysis showed that the participants believed that using AVs will decrease the risk of car crashes and help them safely reach their destination. Parametric analysis and prediction models showed a wide variation in public opinion regarding willingness to use AVs, despite an average high score on this factor. The study found that the trust in AVs was low, and women favored AVs more than men. Prediction models showed that age, trust, and being tech-savvy determine the willingness to use AVs. As younger participants had a high willingness to use AVs, we recommended focusing on changing the perception of older drivers to increase overall AV acceptance by increasing their trust in this new technology and highlighting the features of AVs. From the findings of this study, it is expected that wide-scale adoption of AV depends upon its competitiveness with the traditional and its performance in terms of enhancing road safety.

Modelling self-reported driver perspectives and fatigued driving via deep learning

Driving while fatigued is a considerably understudied risk factor contributing to car crashes every year. The first step in mitigating the respective crash risks is to attempt to infer fatigued driving from other parameters, in order to gauge its extend in road networks. The aim of this study is to investigate the extent to which declared fatigued driving behavior can be predicted based on overall driver opinions and perceptions on that issue. For that purpose, a broad cross-country questionnaire from the ESRA2 survey was used. The questionnaire is related to self-declared beliefs, perception, and attitudes towards a wide range of traffic safety topics. Initially, a binary logistic regression model was trained to provide causal insights on which variables affect the likelihood that a driver engaged in driving while fatigued. Drivers reporting driving under the influence of drugs, fatigue, or alcohol, as well as speeding, safety, and texting while driving or drivers who were more acceptable of fatigued driving were more likely to have recently driven while fatigued. In contrast, acceptability of other hazardous behaviors, namely mobile phone use and drunk driving, was negatively correlated with fatigued driving behavior, as were more responsible driver perspectives overall. To provide a more accurate detection mechanism, which would also incorporate non-linear effects, a Deep Neural Network (DNN) was subsequently trained on the data, slightly outperforming the binary logistic model. From the results of both models, it was concluded that declared fatigued driving behavior can be predicted from questionnaire data, providing new insights to fatigue detection.

Driver perceptions of advanced driver assistance systems: A case study

Background: Automobile manufacturers need to have an insight and understand how consumers, specifically drivers, respond to the advanced driver assistance systems (ADAS) technology in their manufactured vehicles. This study reveals drivers’ perceptions of Malaysia’s advanced driver assistance systems, which is currently lacking in the literature. So far, other studies have focused on countries that are unlike Malaysia’s multi-culture environment. Methods: A survey was designed and distributed using convenience sampling to obtain responses from licensed drivers. Questions included demographic and driving questions, the perceptions of benefits and obstacles relevant to ADAS use, vehicle decision-making, and technology use. Data were collected from 818 respondents who were licensed drivers in Malaysia. Results were then analysed using statistical approaches. Results: The findings indicated that 76.8% of drivers have a positive attitude towards ADAS technology, particularly safety applications when they are available. Regardless of the accuracy of these systems, acceptance of the technology may shift upon viewing or hearing messages of possible problems with ADAS. Conclusions: It can be concluded that the safety advantages of ADAS technology are less valued by drivers who do not have experience of road traffic accidents. Furthermore, acceptance of the technology could be undermined by assuming that the safety applications could be compromised.

Digital Entrepreneurship: Public Perception of Barriers, Drivers, and Future

The widespread access to the Internet has undoubtedly changed the way businesses handle their processes and interact with their customers. With the surge of new devices, business models, technologies, and platforms, alongside social media growth and innovative advertising, it became easier to transition from employment to entrepreneurship. The paper aims to assess the public perception of digital entrepreneurship, with a focus on its barriers, drivers, and expectations for the future. The results show that there is a slight agreement with a digital business being easier to establish compared with a traditional one. The driving forces behind starting a digital business are recognized, and the digital environment is considered essential for business growth in the following years. With some exceptions, there are no significant differences between age groups, genders, relationship statuses, levels of education, and/or occupations when rating the barriers, drivers, and expectations for the future of digital entrepreneurship.

Measuring driver-vehicle cooperation: Development and validation of the Human-Machine-Interaction-Interdependence Questionnaire (HMII)

The development of partially and highly autonomous vehicles that take over parts of the driving task will result in changes in e.g. the responsibilities, interface and system design, task allocation and communication between driver and automated vehicle. To support this change and the increasing space of cooperative possibilities between driver and vehicle, generally accepted design principles and preconditions for successful driver-vehicle cooperation (Directability, Mutual Predictability, Joint Goals and Mutual Task-Dependency) were defined. However, research lacks validated scales measuring the proposed basic principles of driver-vehicle cooperation. Furthermore, a theory is missing that links those basic principles with a theory that enables an understanding of the influence of drivers' perception of the autonomous vehicle and context on driver-vehicle cooperation. Therefore, this work links the basic theoretical principles of driver-vehicle cooperation with the social psychological Theory of Interdependence and their dimensions (Conflict, Power, Mutual Dependence, Information Certainty, Future Interdependence). Filling the gap of missing validated scales for the principles of driver-vehicle cooperation, this study provides the development and validation of the Human-Machine-Interaction-Interdependence (HMII) questionnaire.In two studies, the new HMII questionnaire to measure drivers' perception in driver-vehicle cooperation was developed. In the first study (n = 94), items for the perception of the situation were transferred from the original Theory of Interdependence to the driver-vehicle cooperation. A 7-dimension model was identified via Explorative Factor Analysis. In the second study (n = 314), the model and items were validated via confirmatory factor analysis. A seven-factor model (Power, Conflict, Mutual Dependence Information Certainty: System to Human, Information Certainty (two levels) Future Interdependence (two levels) with 33 items showed a good fit to the data, chi2 = 841, p < .001, adjusted chi2 = 1.77, SRMR = 0.071. In sum, this questionnaire can help designers evaluate the impact of their system designs on driver perceptions of the driver-vehicle cooperation.

A new theory of driver vision pressure energy field and its application in driver behaviour decision‐making model

The interpretation of driver behaviour decisions is an essential part of driver behaviour research. Unlike previous studies that use driver's vision indicators or behaviour indicators as the basis for behaviour decision-making models, this paper proposes a new concept of vision pressure energy field to describe driver's vision perception. Driver behaviours are regarded as the results of the energy fluctuation of the “Potential Energy-Field Energy-Kinetic Energy” cycle. The energy field model and corresponding classification method of driving risk level are presented. The micro-effect and macro-effect of driver behaviour decisions are considered in the decision effect evaluation process. These models are integrated into an RNN (Recurrent Neural Network) framework. After the field test data training, the model results show that the decision-making framework with a hidden layer can successfully describe the car-following and lane-changing behaviours. The phenomenon of continuous behaviour change can be explained by the prediction result of decision effect level. The vision pressure energy field theory integrates the driver behaviour into the physical energy field theory. It presents a new way to interpret driver's vision perception results. The driver behaviour changes can also be successfully predicted through this theory.

Barriers and drivers for the adoption of industrial sustainability measures in European SMEs: Empirical evidence from chemical and metalworking sectors

As industrial sustainability measures and interventions play a central role in enhancing the sustainability performance in industrial firms, it is of great importance to properly understand the factors that might influence the decision-making process leading to their adoption, namely barriers and drivers. However, there is scarce empirical literature discussing barriers and drivers to industrial sustainability as well as the effect of contextual factors or of the firm's approach towards sustainability issues. For this reason, we conducted an exploratory investigation in 26 small and medium enterprises operating in the chemical and metalworking manufacturing sectors across Germany and Italy. Our preliminary findings show that the sampled firms are mainly hindered by economic barriers and fostered by external drivers. The investigation highlighted the influence of the contextual factors sector, country, and size on the perception of barriers and drivers. Moreover, the presence of a dedicated manager for sustainability, the number of certifications held by a firm, and a holistic definition of sustainability, seem to affect the barriers and drivers perceived by the sampled industrial decision-makers. The paper concludes by offering insights to both theoretical and practical discussion over the adoption of industrial sustainability measures, while also providing additional knowledge to practitioners and policy makers on critical areas for the improvement of industrial sustainability.

Обзор принципов пространственного проектирования лесовозных автомобильных дорог

The purpose of the study is to review and analyze the experience of specialists in the field of spatial design of haulage roads for further improving the methodological foundations by providing clarity and visually acceptable degree of spatial curves. Drivers perception of the haulage road as well as overspeeding lead to critical situations or road accidents. A decrease in speed at seemingly abrupt bends on the road affects the efficiency of the log trucking transport. Therefore, the perspective view of the road should clearly orient the driver, that is, be visually clear, clearly changing, ensuring the constancy or modulated reduction of the traffic condition. The need for an optimal spatial solution of the road increases. It is required to determine the conditions under which the visual smoothness and clarity of the most common simple spatial curves when looking from points corresponding to the normal position of the eyes of car drivers is ensured.

The Existence Of Pedicab In Developing Tourism In Malioboro

The objective of this research is to describe “the existence of pedicab in developing tourism in Malioboro” which is viewed from the perception of pedicab drivers toward tourism in Malioboro and the strength and the obstacle of a pedicab. Malioboro is one of the tourist objects in the City of Yogyakarta that currently still to place the public transportation non motorized such as pedicab in developing the tourism in Malioboro. Its existence is also one of the tourism activities in the region that have made as to the beautiful one for tourists. This research is descriptive qualitative. The data is collected through observation, an interview and a literature review. The result of the research shows that the perception of pedicab drivers toward its tourism is good. There is progress in tourism in Malioboro street. Second, it can be viewed from the strengths and the obstacles of pedicabs. The strength shows that pedicab is a means of promotion and advertisement of tourism in Malioboro. The pedicab is an icon of Yogyakarta, especially in Malioboro. The obstacle is the existence of competitors in the field. This condition makes the difficulties for the pedicab as non-motorized transportation. In the economic aspect, this phenomenon will influence the economic life of the pedicab driver.