Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Sustainable and Safe Road User Behaviour
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sustainable and Safe Road User Behaviour

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 20117

Special Issue Editors

Dr. Cornelia Mairean

E-Mail Website
Guest Editor
Faculty of Psychology and Educational Sciences, Alexandru Ioan Cuza University of Iasi, 700554 Iasi, Romania
Interests: transport psychology; trauma, stress and critical life events; resilience, posttraumatic development, quality of life; coping, emotional regulation
Dr. Danijela Baric

E-Mail Website
Guest Editor
Faculty of Transport and Traffic Sciences, University of Zagreb, 10000 Zagreb, Croatia
Interests: railway safety; road safety; railway engineering
Dr. Grigore M. Havarneanu

E-Mail Website
Guest Editor
International Union of Railways (UIC), F-75015 Paris, France
Interests: railway transport; traffic psychology; safety and security

Special Issue Information

Dear Colleagues,

 

Sustainable and safe road user behavior research is characterized by multi-disciplinarity. To understand risky behavior and drive positive behavioral changes we need to explore psychological, social, environmental, and technological factors, as well as the interplay between them. The current special issue aims to offer a clear perspective of sustainable and safe behavior of different road users categories (e.g., drivers, powered two-wheelers, cyclists, pedestrians) as well as of passengers in a wide mobility context. Different sustainable mobility options are explored (e.g., active mobility, micromobility, shared mobility, public transport), as well as people’s preference for specific mobility options mainly in urban settings. We anticipate that this special issue will present a variety of studies with a wide range of methods, both qualitative and quantitative.

 

Dr. Cornelia Mairean
Dr. Danijela Baric
Dr. Grigore M. Havarneanu
Guest Editors

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 submissions that pass pre-check are 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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

  • road safety
  • human factors
  • risky behavior
  • behavioral changes
  • drivers
  • pedestrians
  • cyclists
  • urban mobility
  • autonomous vehicles
  • level crossings

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 6338 KiB  
Article
Improving Autonomous Vehicle Controls and Quality Using Natural Language Processing-Based Input Recognition Model
by Mohd Anjum and Sana Shahab
Sustainability 2023, 15(7), 5749; https://doi.org/10.3390/su15075749 - 25 Mar 2023
Cited by 5 | Viewed by 2301
Abstract
In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform [...] Read more.
In contemporary development, autonomous vehicles (AVs) have emerged as a potential solution for sustainable and smart transportation to fulfill the increasing mobility demands whilst alleviating the negative impacts on society, the economy, and the environment. AVs completely depend on a machine to perform driving tasks. Therefore, their quality and safety are critical concerns for driving users. AVs use advanced driver assistance systems (ADASs) that heavily rely on sensors and camera data. These data are processed to execute vehicle control functions for autonomous driving. Furthermore, AVs have a voice communication system (VCS) to interact with driving users to accomplish different hand-free functions. Some functions such as navigation, climate control, media and entertainment, communication, vehicle settings, vehicle status, and emergency assistance have been successfully incorporated into AVs using VCSs. Several researchers have also implemented vehicle control functions using voice commands through VCSs. If a situation occurs when AV has lost control due to malfunctioning or fault in the installed computer, sensors and other associated modules, driving users can control the AV using voice notes to perform some driving tasks such as changing speeds, lanes, breaking, and directing the car to reach a safe condition. Furthermore, driving users need manual control over AV to perform these tasks in some situations, like lane changing or taking an exit due to divergence. These tasks can also be performed with the help of voice commands using VCSs. Therefore, finding the exact voice note used to instruct different actuators in risk situations is crucial. As a result, VCSs can greatly improve safety in critical situations where manual intervention is necessary. AVs’ functions and quality can be significantly increased by integrating a VCS with an ADAS and developing an interactive ADAS. Now, the driver functions are controlled by voice features. Therefore, natural language processing is utilized to extract the features to determine the user’s requirements. The extracted features control the vehicle functions and support driving activities. The existing techniques consume high computation while predicting the user command and causing a reduction in the AVs’ functions. This research issue is overcome by applying the variation continuous input recognition model. The proposed approach utilizes the linear training process that resolves the listening and time-constrained problems and uncertain response issues. The proposed model categorizes the inputs into non-trainable and trainable data, according to the data readiness and listening span. Then, the non-distinguishable data were validated by dividing it into the linear inputs used to improve the response in the AVs. Thus, effectively utilizing training parameters and the data decomposition process minimizes the uncertainty and increases the response rate. The proposed model has significantly improved the exact prediction of users’ voice notes and computation efficiency. This improvement enhances the VCS quality and reliability used to perform hand-free and vehicle control functions. The reliability of these functions ultimately improves the safety of AVs’ driving users and other road users. Full article
(This article belongs to the Special Issue Sustainable and Safe Road User Behaviour)
►▼ Show Figures

Figure 1

19 pages, 5928 KiB  
Article
Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method
by Mohd Anjum and Sana Shahab
Sustainability 2023, 15(4), 3069; https://doi.org/10.3390/su15043069 - 8 Feb 2023
Cited by 5 | Viewed by 2447
Abstract
Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and [...] Read more.
Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics. Full article
(This article belongs to the Special Issue Sustainable and Safe Road User Behaviour)
►▼ Show Figures

Figure 1

14 pages, 1925 KiB  
Article
The Influences of Different Sensory Modalities and Cognitive Loads on Walking Navigation: A Preliminary Study
by Xiaochen Zhang, Lingling Jin, Jie Zhao, Jiazhen Li, Ding-Bang Luh and Tiansheng Xia
Sustainability 2022, 14(24), 16727; https://doi.org/10.3390/su142416727 - 13 Dec 2022
Cited by 3 | Viewed by 2026
Abstract
External cognitive burden has long been considered an important factor causing pedestrian navigation safety problems, as pedestrians in navigation inevitably acquire external information through their senses. Therefore, the influences of different types of sensory modalities and cognitive loads on walking navigation are worthy [...] Read more.
External cognitive burden has long been considered an important factor causing pedestrian navigation safety problems, as pedestrians in navigation inevitably acquire external information through their senses. Therefore, the influences of different types of sensory modalities and cognitive loads on walking navigation are worthy of in-depth investigation as the foundation for improving pedestrians’ safety in navigation. This study investigated users’ performance in visual, auditory, and tactile navigation under different cognitive loads by experimental simulation. Thirty-six participants were recruited for the experiment. A computer program simulating walking navigation was used, and three different cognitive task groups were set up. Participants’ reaction times and performances were recorded during the experiment, and a post-test questionnaire was administered for evaluation purposes. According to the tests, the following points are summarized. First, visual navigation performed the best in load-free conditions, which was significantly faster than auditory navigation and tactile navigation, but the difference between the latter two was not significant. There was a significant interaction between navigation types and cognitive load types. Specifically, in the condition without load, reaction time in auditory navigation was significantly slower than those in visual navigation and tactile navigation. In the condition with auditory load, reaction time in visual navigation was significantly faster than those in auditory navigation and tactile navigation. In the condition with visual load, there were no significant differences among the three navigations. Full article
(This article belongs to the Special Issue Sustainable and Safe Road User Behaviour)
►▼ Show Figures

Figure 1

13 pages, 1798 KiB  
Article
Implementing the Maximum Likelihood Method for Critical Gap Estimation under Heterogeneous Traffic Conditions
by Arshad Jamal, Muhammad Ijaz, Meshal Almosageah, Hassan M. Al-Ahmadi, Muhammad Zahid, Irfan Ullah and Rabia Emhamed Al Mamlook
Sustainability 2022, 14(23), 15888; https://doi.org/10.3390/su142315888 - 29 Nov 2022
Cited by 2 | Viewed by 2006
Abstract
Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed [...] Read more.
Gap acceptance analysis is crucial for determining capacity and delay at uncontrolled intersections. The probability of a driver accepting an adequate gap changes over time, and in different intersection types and traffic circumstances. The majority of previous studies in this regard have assumed homogeneous traffic conditions, and applying them directly to heterogeneous traffic conditions may produce biased results. Moreover, driver behavior concerning critical gap acceptance or rejection in traffic also varies from one location to another. The current research focused on the estimation of critical gaps considering different vehicle types (cars, and two- and three-wheelers) under heterogenous traffic conditions at uncontrolled crossings in the city of Peshawar, Pakistan. A four-legged uncontrolled intersection in the study area was used to investigate drivers’ gap acceptance behavior. The gaps were investigated for various vehicle types: two-wheelers, three-wheelers, and cars. For data collection, a video recording method was used, and Avidemux video editing software was used for data investigation. The study investigated the applicability of the maximum likelihood (MLM) method to analyzing a vehicle’s critical gap. MLM estimation results indicate that the essential critical gap values for car drivers are in the range from 7.45 to 4.6 s; for two-wheelers, the critical gap was in the range from 6.78 to 4.7 s; and for three-wheelers, the values were in the range from 6.3 to 4.9 s. At an uncontrolled intersection, the proposed method’s results can assist in distinguishing between different road user groups. This study’s findings are intended to be useful to both researchers and practitioners, particularly in developing countries with similar traffic patterns and vehicle adherence patterns at unsignalized intersections. Full article
(This article belongs to the Special Issue Sustainable and Safe Road User Behaviour)
►▼ Show Figures

Figure 1

9 pages, 477 KiB  
Article
Cognitive Biases, Risk Perception, and Risky Driving Behaviour
by Cornelia Măirean, Grigore M. Havârneanu, Danijela Barić and Corneliu Havârneanu
Sustainability 2022, 14(1), 77; https://doi.org/10.3390/su14010077 - 22 Dec 2021
Cited by 8 | Viewed by 6401
Abstract
This study evaluated the relationship between drivers’ cognitive biases (i.e., optimism bias, illusion of control) and risky driving behaviour. It also investigated the mediational role of risk perception in the relationship between cognitive biases and self-reported risky driving. The sample included 366 drivers [...] Read more.
This study evaluated the relationship between drivers’ cognitive biases (i.e., optimism bias, illusion of control) and risky driving behaviour. It also investigated the mediational role of risk perception in the relationship between cognitive biases and self-reported risky driving. The sample included 366 drivers (Mage = 39.13, SD = 13.63 years) who completed scales measuring optimism bias, illusion of control, risk perception, and risky driving behaviour, as well as demographic information. The results showed that risky driving behaviour was negatively predicted by optimism bias and positively predicted by the illusion of control. Further, risk perception negatively correlated with risky behaviour and also mediated the relation between both optimism bias and illusion of control with risky driving. The practical implications of these results for traffic safety and future research are discussed. Full article
(This article belongs to the Special Issue Sustainable and Safe Road User Behaviour)
►▼ Show Figures

Figure 1

18 pages, 1198 KiB  
Article
Traffic Injury Risk Based on Mobility Patterns by Gender, Age, Mode of Transport and Type of Road
by Guadalupe González-Sánchez, María Isabel Olmo-Sánchez, Elvira Maeso-González, Mario Gutiérrez-Bedmar and Antonio García-Rodríguez
Sustainability 2021, 13(18), 10112; https://doi.org/10.3390/su131810112 - 9 Sep 2021
Cited by 13 | Viewed by 2782
Abstract
The role of gender and age in the risk of Road Traffic Injury (RTI) has not been fully explored and there are still significant gaps with regard to how environmental factors, such as road type, affect this relationship, including mobility as a measure [...] Read more.
The role of gender and age in the risk of Road Traffic Injury (RTI) has not been fully explored and there are still significant gaps with regard to how environmental factors, such as road type, affect this relationship, including mobility as a measure of exposure. The aim of this research is to investigate the influence of the environmental factor road type taking into account different mobility patterns. For this purpose, a cross-sectional study was carried out combining two large databases on mobility and traffic accidents in Andalusia (Spain). The risk of RTI and their severity were estimated by gender and age, transport mode and road type, including travel time as a measure of exposure. Significant differences were found according to road type. The analysis of the rate ratio (Ratemen/Ratewomen), regardless of age, shows that men always have a higher risk of serious and fatal injuries in all modes of transport and road types. Analysis of victim rates by gender and age groups allows us to identify the most vulnerable groups. The results highlight the need to include not only gender and age but also road type as a significant environmental factor in RTI risk analysis for the development of effective mobility and road safety strategies. Full article
(This article belongs to the Special Issue Sustainable and Safe Road User Behaviour)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop