Special Issue "Traffic Safety and Driver Behaviour"

A special issue of Safety (ISSN 2313-576X).

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editor

Prof. Dr. Miguel A. Perez
E-Mail Website
Guest Editor
Director, Center for Data Reduction and Analysis Support, Virginia Tech Transportation Institute, Blacksburg, VA 24061, USA
Interests: naturalistic driving; traffic safety; driver behaviour; big data; human-vehicle interaction; active safety systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The pace of change in personal mobility has greatly accelerated over the last several years, and promises to quicken even more. Automation in our transportation ecosystem is increasing. Technology conglomerates are increasing their footprint in the provision of traffic services and taking leading roles in developing necessary technologies and developing the mobility products of the future.

In the context of all these developments, it is essential to remember that the human driver is still an essential part of our transportation system and will likely remain one for decades to come. While automation promises to facilitate the driving task, perhaps even eliminate it at some point, it will be a long time before these automated systems can expect to exist in isolation of human drivers.

The last two decades have provided us an unparalleled glimpse into drivers. Data acquisition technologies have allowed us to look at driver behaviour at both the macro and micro levels. Our understanding of what, when, and how drivers perform different actions has increased, as we also have come to realize some of the relationships between driver behaviours and traffic safety.

Crashes, however, still occur and represent a sizeable economic and personal cost to those involved and to our society.

This Special Issue will provide a platform for research linking driver behavior and safety, both in the traditional context of manual driving and in the nascent paradigm of automation. Researchers are invited to submit manuscripts regarding any aspect of driver behaviour that influences the safety of the driver and the other actors in the transportation ecosystem. Papers addressing traffic safety in the context of evolving vehicle automation are especially welcome.

Prof. Dr. Miguel A. Perez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Safety is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Traffic safety
  • Driver behaviour
  • Crashes
  • Automation
  • Driver distraction
  • Driver impairment
  • Emerging technologies
  • Driver assistance
  • Driver-vehicle interaction
  • Risk

Published Papers (6 papers)

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Research

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Article
Comparisons of Predictive Power for Traffic Accident Involvement; Celeration Behaviour versus Age, Sex, Ethnic Origin, and Experience
Safety 2018, 4(4), 59; https://0-doi-org.brum.beds.ac.uk/10.3390/safety4040059 - 12 Dec 2018
Cited by 2 | Viewed by 3486
Abstract
Driver celeration behaviour theory (DCBT) assumes that risk for a driver of causing a road crash is linearly related to speed change in any given moment and that the speed change variable (celeration) captures all risk (all vehicle control movements can be measured [...] Read more.
Driver celeration behaviour theory (DCBT) assumes that risk for a driver of causing a road crash is linearly related to speed change in any given moment and that the speed change variable (celeration) captures all risk (all vehicle control movements can be measured as acceleration). When sampling driver behaviour, the celeration variable is calculated as the average of all absolute values of acceleration when the vehicle is moving. DCBT predicts that no other variable can be a stronger predictor of (the same set of) traffic accident involvements than celeration, given equal reliability of the predictors. Also, other predictors, regardless of which ones, should associate with celeration in ways that are similar to how they correlate with accidents. Predictions were tested in a sample of bus drivers, against variables with reliabilities close to 1 (age, sex, experience, ethnic origin), which are not necessarily optimal predictors for testing but were the only predictors available. The results were largely as predicted from theory. The principles for testing the kind of predictions made from celeration theory were discussed, outlining the importance of a larger number of variables, preferably with repeated measurements. Full article
(This article belongs to the Special Issue Traffic Safety and Driver Behaviour)
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Article
Perceived Safety Benefits of Aftermarket Driver Support Systems: Results from a Large Scale European Field Operational Test (FOT)
Safety 2018, 4(4), 54; https://0-doi-org.brum.beds.ac.uk/10.3390/safety4040054 - 20 Nov 2018
Viewed by 3203
Abstract
A field operational test (FOT) is a technique used within traffic safety to evaluate the overall value of in-vehicle information systems (IVISs) under normal operating conditions. In this study, a pan-European FOT was used to evaluate Navigation, Speed Information/Alert, Traffic Information, and Green [...] Read more.
A field operational test (FOT) is a technique used within traffic safety to evaluate the overall value of in-vehicle information systems (IVISs) under normal operating conditions. In this study, a pan-European FOT was used to evaluate Navigation, Speed Information/Alert, Traffic Information, and Green Driving Support functions together with participants’ perceptions of safety’ before, during, and after using the functions. Through utilization and adherence to the FOT methodology, data were collected over a period ranging from 8 to 16 months in five European countries in order to assess the driver pre-conceived ideas and subsequent subjective and objective experiences with the IVIS functions. Several analyses of data were conducted, and this paper describes the results relating to the ‘user-experience’ as evaluated through subjective responses. The study showed that before the FOTs started, overall participants expected a higher safety benefit through using Speed Alert compared to the other functions. This function was also perceived to offer the highest safety benefit after the FOT had been completed. Perceptions of safety were found to be lowest for the green-driving function. The results offer insights into public expectations of IVIS functions and how these change with experience and overall; they suggest that, in some cases, the perception to safety benefits could be somewhat misplaced. Full article
(This article belongs to the Special Issue Traffic Safety and Driver Behaviour)
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Review

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Review
On Driver Behavior Recognition for Increased Safety: A Roadmap
Safety 2020, 6(4), 55; https://0-doi-org.brum.beds.ac.uk/10.3390/safety6040055 - 12 Dec 2020
Cited by 1 | Viewed by 4225
Abstract
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and [...] Read more.
Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced. Full article
(This article belongs to the Special Issue Traffic Safety and Driver Behaviour)
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Review
A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior
Safety 2019, 5(4), 72; https://0-doi-org.brum.beds.ac.uk/10.3390/safety5040072 - 24 Oct 2019
Cited by 7 | Viewed by 4937
Abstract
Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex [...] Read more.
Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human–machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators—such as drivers, train operators, pilots, astronauts—as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs. Full article
(This article belongs to the Special Issue Traffic Safety and Driver Behaviour)
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Review
Driving among Adolescents with Autism Spectrum Disorder and Attention-Deficit Hyperactivity Disorder
Safety 2018, 4(3), 40; https://0-doi-org.brum.beds.ac.uk/10.3390/safety4030040 - 17 Sep 2018
Cited by 10 | Viewed by 3680 | Correction
Abstract
Over the past several decades there has been a surge of research on the contextual, biological, and psychological factors associated with transportation safety in adolescence. However, we know much less about the factors contributing to transportation safety among adolescents who do not follow [...] Read more.
Over the past several decades there has been a surge of research on the contextual, biological, and psychological factors associated with transportation safety in adolescence. However, we know much less about the factors contributing to transportation safety among adolescents who do not follow a typical developmental trajectory. Adolescents with developmental disabilities (DD) such as Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) have a wide range of behavioral and psychological deficits that may make the complex task of driving even more challenging. Because these adolescents often retain characteristic symptoms of their disorder into adulthood, it may impede their ability to achieve important milestones during the developmental transition from adolescent to adult. As the motivating force behind autonomous living and employment, the capacity for independent transportation is paramount to an adolescent’s overall success. This critical review will draw from the current body of literature on adolescent drivers with developmental disabilities to determine (1) areas of impairment; (2) safety risk factors; and (3) effective interventions for improving driving safety in this vulnerable population of adolescent drivers between the ages of 15–22. This review will also identify important unanswered research questions, and summarize the current state of the literature. Full article
(This article belongs to the Special Issue Traffic Safety and Driver Behaviour)

Other

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Correction
Correction: Bishop, H. et al. Driving among Adolescents with Autism Spectrum Disorder and Attention-Deficit Hyperactivity Disorder. Safety 2018, 4, 40
Safety 2018, 4(4), 52; https://0-doi-org.brum.beds.ac.uk/10.3390/safety4040052 - 16 Nov 2018
Viewed by 3031
Abstract
The published version of the paper [...] Full article
(This article belongs to the Special Issue Traffic Safety and Driver Behaviour)
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