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Vehicles
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Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning
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Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 15 September 2026 | Viewed by 5264

Special Issue Editors

Dr. Muhammad Hussain

E-Mail Website
Guest Editor
Western Australian Centre for Road Safety Research, The University of Western Australia, Perth, WA 6009, Australia
Interests: road safety; aberrant driving behaviours; driver distraction; crash modelling; traffic simulation; transportation infrastructure; human factors in road safety
Dr. Xiaomeng Li

E-Mail Website
Guest Editor
The Centre for Accident Research and Road Safety, Queensland University of Technology, Brisbane City, QLD 4000, Australia
Interests: road safety; human factors; automated vehicles and human-machine-interaction/interface; traffic and transportation planning and management
Special Issues, Collections and Topics in MDPI journals
Dr. Paul Roberts

E-Mail Website
Guest Editor
1. School of Psychological Science, The University of Western Australia (M304), 35 Stirling Highway, Perth 6009, Australia
2. Western Australian Centre for Road Safety Research, The University of Western Australia, Perth 6009, Australia
Interests: road safety, driver fatigue, distraction and driver error; digital billboard distraction; experimental psychology and implicit memory; fatigue management in transportation and resources sectors; applied research on road user behaviour and safety interventions
Dr. Amjad Pervez

E-Mail Website
Guest Editor
SPIRIT, School of Traffic and Transportation Engineering, Central South University, Changsha, China
Interests: driving behaviour analysis; autonomous vehicles safety and regulations; vulnerable road users safety; travel behavioural change after disasters caused by climate change and pandemics; transportation sustainability; adoption behaviour towards emerging transportation (electric vehicles; electric motorcycles); artificial intelligence and machine learning applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As transportation systems evolve with the advancement of intelligent technologies and increasing emphasis on sustainability, a siloed approach to safety, behaviour, and planning is no longer sufficient. This Special Issue highlights the need for an integrated framework that considers road safety in conjunction with driver behaviour, Intelligent Transport Systems (ITSs), and long-term planning for sustainable mobility. By bringing together interdisciplinary research, this Special Issue aims to showcase innovative strategies and data-driven interventions that collectively contribute to safer, smarter, and more environmentally responsible transport networks.

Our objective is present cutting-edge research at the intersection of driver behaviour, ITS, and sustainable transportation planning, with a particular focus on road safety outcomes. Advancements in data-driven modelling, vehicle automation, connected vehicle systems, and behavioural science offer exciting opportunities to improve traffic efficiency, reduce crashes, and support resilient urban mobility systems.

We invite original research articles and review papers that examine these connections through empirical studies, simulations, policy evaluations, or theoretical frameworks. Contributions from interdisciplinary teams, cross-sectoral collaborations, and real-world applications are especially encouraged.

Research areas may include (but are not limited to) the following:

  • Aberrant driving behaviours and their implications for road safety;
  • Behavioural modelling of drivers in response to smart infrastructure and in-vehicle systems;
  • Safety impacts of Connected and Autonomous Vehicles (CAVs);
  • Role of ITSs in mitigating aberrant driving behaviours and enhancing decision-making;
  • Use of AI and big data analytics in crash prediction, safety audits, and transport planning;
  • Integration of driver-centric insights into sustainable urban mobility plans;
  • Innovative policy and planning tools to promote safe and sustainable transport networks.

Dr. Muhammad Hussain
Dr. Xiaomeng Li
Dr. Paul Roberts
Dr. Amjad Pervez
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 250 words) can be sent to the Editorial Office for assessment.

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. Vehicles is an international peer-reviewed open access monthly 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 1800 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

  • transportation systems
  • road safety
  • driver behaviour
  • Intelligent Transport Systems (ITSs)
  • sustainable mobility
  • data-driven modelling
  • vehicle automation
  • connected vehicle systems

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

Published Papers (5 papers)

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Research

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24 pages, 3119 KB  
Article
PHR-Net: Proposal-Level Historical Retrieval for Non-Stationary Temporal Consistency in Trajectory Prediction
by Bo Zhang and Ming Xu
Vehicles 2026, 8(5), 109; https://doi.org/10.3390/vehicles8050109 - 12 May 2026
Viewed by 209
Abstract
Multi-agent trajectory prediction serves as a critical component in autonomous driving systems, bridging environment perception, behavior understanding, and motion planning. Its outputs not only affect candidate trajectory evaluation and interactive decision-making but also directly influence downstream processes such as risk anticipation, braking and [...] Read more.
Multi-agent trajectory prediction serves as a critical component in autonomous driving systems, bridging environment perception, behavior understanding, and motion planning. Its outputs not only affect candidate trajectory evaluation and interactive decision-making but also directly influence downstream processes such as risk anticipation, braking and yielding, and safety margin allocation. Therefore, obtaining accurate and stable prediction results is of great importance. Although existing methods have achieved remarkable progress in single-timestep prediction accuracy, most of them still adopt an independent decoding paradigm under a sliding-window setting. As a result, during continuous online prediction, these models are prone to frequent mode switching, temporal discontinuities in overlapping segments, and local trajectory jitter, which become particularly pronounced in complex interactive scenarios such as yielding, merging, and unprotected turning. To address these issues, this paper proposes PHR-Net, a two-stage proposal-level historical retrieval framework that introduces cross-timestep historical context to perform consistency-aware refinement of current predictions on top of multimodal coarse proposals. Experiments on the Argoverse 1 benchmark show that PHR-Net achieves competitive performance under both Top-1 and Top-6 settings. PHR-Net obtains a Top-1 minFDE of 1.0834 and MR of 0.1046 and achieves an MR of 0.1027 under the Top-6 setting. In the overlapping-interval consistency evaluation, PHR-Net reduces the summed ADE to 2.08. These results show that proposal-level historical retrieval improves endpoint reliability and cross-timestep temporal consistency. Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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11 pages, 1933 KB  
Article
Study on the Mechanism of Urban Road Car-Following Safety Under Adverse Weather Conditions
by Zhipeng Gu, Xing Wang and Yufei Han
Vehicles 2026, 8(3), 56; https://doi.org/10.3390/vehicles8030056 - 13 Mar 2026
Viewed by 435
Abstract
Car following is a common and important behavior in vehicle traffic flow, and the fluctuation of car-following behavior caused by the change in weather environment has also become one of the main causes of traffic accidents. To solve this problem, a driving scene [...] Read more.
Car following is a common and important behavior in vehicle traffic flow, and the fluctuation of car-following behavior caused by the change in weather environment has also become one of the main causes of traffic accidents. To solve this problem, a driving scene on urban roads was built through the driving simulation platform, and the driving simulator was used to carry out the vehicle-following test. The operating behavior parameters of the test drivers, such as steering wheel angle, headway, throttle opening, standard deviation of vehicle speed, acceleration, collision times, and so on, were collected and studied. The results showed that there were significant differences (p < 0.05) in indicators such as steering wheel angle, headway, acceleration, and standard deviation of speed under adverse weather conditions. The bad weather caused the line of sight to be blocked, which the driver compensated for by strengthening the trimming of the steering wheel angle, leading to the deterioration of the vehicle lateral stability. Moreover, safety studies have shown that the minimum driving interval occurred in foggy weather, while the maximum occurred in snowy weather. In addition, the standard deviation of vehicle speed and acceleration fluctuations have been reduced to ensure driving safety in adverse weather conditions. The driving experience of the drivers has a significant impact on the number of collisions, as novice drivers had a higher probability of collision. Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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18 pages, 3133 KB  
Article
Towards AI-Assisted Motorcycle Safety: Multi-Modal Video Analysis for Hazard Detection and Contextual Risk Assessment
by Fatemeh Ghorbani, Augustin Hym, Mohammed Elhenawy and Andry Rakotonirainy
Vehicles 2026, 8(2), 39; https://doi.org/10.3390/vehicles8020039 - 13 Feb 2026
Cited by 1 | Viewed by 932
Abstract
Motorcyclists face a disproportionately high risk of severe injury or death compared to other road users, highlighting the need for intelligent rider assistance technologies. This paper presents an initial, modular, and interpretable AI pipeline that generates context-aware safety advice from first-person motorcycle videos [...] Read more.
Motorcyclists face a disproportionately high risk of severe injury or death compared to other road users, highlighting the need for intelligent rider assistance technologies. This paper presents an initial, modular, and interpretable AI pipeline that generates context-aware safety advice from first-person motorcycle videos with practical inference latency suitable for on-device deployment, framing large language models as interpretable cognitive support agents for motorcycle safety. The system integrates lightweight perception and reasoning components to emulate the function of an Advanced Rider Assistance System (ARAS). Video frames are processed at 1 FPS using Pixtral, a Mistral-based multimodal large language model (MLLM), to produce descriptive scene captions, while YOLOv8 identifies key objects such as vehicles, pedestrians, and road hazards. A Mistral-small language model then fuses this information to generate concise, imperative safety tips. Preliminary evaluations on publicly available motorcycle POV datasets demonstrate promising performance in terms of contextual accuracy, interpretability, and scalability, suggesting potential for real-world deployment in low-resource or embedded environments. The proposed framework offers interpretable, context-aware safety assistance that is particularly valuable for young and newly licensed riders during the transition from supervised training to independent riding, where real-time hazard interpretation support is most needed. Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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16 pages, 2410 KB  
Article
Revealing the Impact Factors of the Electric Bike Riders’ Violation Riding Behaviors in China: Integrating SEM with RP-Logit Model
by Yazhu Zou, Chunjiao Dong and Jing Shi
Vehicles 2025, 7(4), 122; https://doi.org/10.3390/vehicles7040122 - 26 Oct 2025
Cited by 1 | Viewed by 1768
Abstract
The purpose of this study was to investigate how environmental judgments and psychological factors jointly influence self-reported violation riding behaviors among e-bike riders in China, with attention to sociodemographic heterogeneity. To achieve this, the e-bike violation riding behavior questionnaire was designed. Additionally, a [...] Read more.
The purpose of this study was to investigate how environmental judgments and psychological factors jointly influence self-reported violation riding behaviors among e-bike riders in China, with attention to sociodemographic heterogeneity. To achieve this, the e-bike violation riding behavior questionnaire was designed. Additionally, a hybrid approach integrating the Structural Equation Model (SEM) with the Random Parameters Logit (RP-Logit) model was constructed to reveal the impact factors of e-bike riders’ violation riding behaviors, in which demographic information and latent variables were comprehensively considered. This methodology simultaneously analyzed the complex relationships among latent variables (measured by SEM) and captured the heterogeneous effects of demographic factors on discrete violation tendencies (modeled by RP-Logit). The following two main findings emerged: (1) Experienced riders and those who use e-bikes as operating tools tend to exhibit a higher tendency to engage in violation riding. (2) Perceived Risk has the greatest impact on the performance of high-violation tendencies. Specifically, the probability of choosing high-violation riding behaviors decreases by 0.18 for each unit increase in the rider’s Perceived Risk. (3) Similarly, for each unit increase in riders’ Perceived Law Enforcement, the probability of choosing high-violation riding behaviors decreases by 0.15. The findings suggest that relevant authorities should address e-bike violation behaviors through enhanced safety education and strengthened enforcement measures, particularly targeting high-risk rider groups. Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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Review

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30 pages, 6506 KB  
Review
Driving Simulator-Based Driving Behavioural Research: A Bibliometric and Narrative Review Providing Key Insights for New and Emerging Researchers
by Muhammad Hussain, Muladilijiang Baikejuli, Jing Shi, Amjad Pervez, Matthew A. Albrecht, Etikaf Hussain, Razi Hasan and Teresa Senserrick
Vehicles 2026, 8(2), 32; https://doi.org/10.3390/vehicles8020032 - 6 Feb 2026
Cited by 1 | Viewed by 1141
Abstract
The driving simulator’s ability to provide practical, safe, and controlled environments has made it a widely used tool for evaluating driving behaviours in the realm of road safety. To consolidate the fragmented research in this area, this study is divided into two parts: [...] Read more.
The driving simulator’s ability to provide practical, safe, and controlled environments has made it a widely used tool for evaluating driving behaviours in the realm of road safety. To consolidate the fragmented research in this area, this study is divided into two parts: a bibliometric analysis and a narrative review: (a) the bibliometric analysis identified 4992 studies, expanding from 2000 to June 2025, sourced from four databases—Web of Science, Scopus, TRID, and Google Scholar (supplementary)—and examined trends over the years, the general topics covered, the countries where studies were conducted, and the main research fields associated with driving simulators; and (b) the narrative review further analysed 48 selected studies from eight domains (distraction, fatigue and drowsiness, traffic-calming measures, impairment from psychoactive drugs, road curves, intersections, tunnels, and adverse weather conditions) to provide insights into how driving simulators have contributed to these fields, the methodologies employed by researchers, and the practical applications of the findings. The study aims to provide clear and essential insights for new and emerging researchers, offering an accessible overview of how driving simulators have evolved, why they are important, how they measure different driving metrics, and how they ultimately improve road safety. The findings indicate that driving simulator studies are increasingly prominent in research on driver behaviour (e.g., driving speed, lateral movement, and acceleration/deceleration). Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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