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An Improved Real-Time Detection Transformer Model for the Intelligent Survey of Traffic Safety Facilities
The undertaking of traffic safety facility (TSF) surveys represents a significant labor-intensive endeavor, which is not sustainable in the long term. The subject of traffic safety facility recognition (TSFR) is beset with numerous challenges, including those associated with background misclassification, the diminutive dimensions of the targets, the spatial overlap of detection targets, and the failure to identify specific targets. In this study, transformer-based and YOLO (You Only Look Once) series target detection algorithms were employed to construct TSFR models to ensure both recognition accuracy and efficiency. The TSF image dataset, comprising six categories of TSFs in urban areas of three cities, was utilized for this research. The dimensions and intricacies of the Detection Transformer (DETR) family of models are considerably more substantial than those of the YOLO family. YOLO-World and Real-Time Detection Transformer (RT-DETR) models were optimal and comparable for the TSFR task, with the former exhibiting a higher detection efficiency and the latter a higher detection accuracy. The RT-DETR model exhibited a notable reduction in model complexity by 57% in comparison to the DINO (DETR with improved denoising anchor boxes for end-to-end object detection) model while also demonstrating a slight enhancement in recognition accuracy. The incorporation of the RepGFPN (Reparameterized Generalized Feature Pyramid Network) module has markedly enhanced the multi-target detection accuracy of RT-DETR, with a mean average precision (mAP) of 82.3%. The introduction of RepGFPN significantly enhanced the detection rate of traffic rods, traffic sign boards, and water surround barriers and somewhat ameliorated the problem of duplicate detection.
Journal Article
Being the safe driver ! : behind the wheel! The new road safety novel book !
This eye-opening book emphasizes the importance of road safety and careful driving through a collection of real-life stories from accident victims who share the hard experiences and lessons they learned in order to better educate drivers everywhere.
Book
In the Company of Cars
Road safety research has traditionally involved a focus on individuals in which social norms are considered but rarely discussed in detail. Outlining the existing body of research on young drivers in particular, In the Company of Cars shows the contribution that considering road safety from a social and cultural perspective could make to the reduction of death and injury on the roads. It highlights the involvement of driving cultures, as distinct from car cultures, in the social framing of cars and the ways in which they are utilised.
eBook
Structured Risk Identification for Sustainable Safety in Mixed Autonomous Traffic: A Layered Data-Driven Approach
With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response to high-risk situations in urban traffic environments where autonomous and conventional vehicles coexist. To this end, high-risk factors were identified through a combination of literature meta-analysis, accident history and image analysis, autonomous driving video review, and expert seminars. For analytical structuring, the six-layer scenario framework from the PEGASUS project was redefined. Using the analytic hierarchy process (AHP), 28 high-risk factors were identified. A risk prediction model framework was then developed, incorporating observational indicators derived from expert rankings. These indicators were structured as input variables for both road segments and autonomous vehicles, enabling spatial risk assessment through agent-based strategies. This space–object integration-based prediction model supports the early detection of high-risk situations, the designation of high-enforcement zones, and the development of preventive safety systems, infrastructure improvements, and policy measures. Ultimately, the findings offer a pathway toward achieving sustainable safety in mixed traffic environments during the initial deployment phase of autonomous vehicles.
Journal Article
Acceptability of Children Road Safety Education in Pakistan: A Mixed-Method Approach to Exploring Parents’ and Teachers’ Perspectives
In Pakistan, implementing road safety education (RSE) initiatives is vital in tackling the concerning rates of road accidents. Since parents and teachers are crucial in moulding children’s road safety behaviours, this study investigated the perspectives of parents and teachers regarding the acceptability of RSE programs in Pakistan. Using a mixed-methods approach, the research combines quantitative data from questionnaires (n = 63 teachers, n = 97 parents) with qualitative insights from interviews (five teachers, four parents). The study reveals significant gaps in RSE implementation across educational levels (i.e., primary, secondary, and high school), with not even half of the teachers reporting dedicated RSE programs in their curriculum, majorly in secondary and high schools. Both parents and teachers express dissatisfaction with current RSE effectiveness, highlighting a critical need for improvement. Key barriers to RSE implementation include cultural norms, inadequate infrastructure, and limited teacher training. However, the study also identifies a strong interest from parents and teachers in participating in effective RSE programs. Parents favour a mixed approach to RSE delivery, combining online and physical formats, and prefer short, frequent sessions for their children. The research underscores the need for a multidimensional RSE approach, addressing educational content, societal perceptions, and infrastructure improvements. These findings provide valuable insights for policymakers and educators to enhance RSE and improve children’s road safety knowledge in Pakistan.
Journal Article
Eliminating serious injury and death from road transport : a crisis of complacency
\"The book explodes the myths that currently drive society's view of traffic safety and limit progress in reducing death and serious injury. It presents current scientific knowledge in a non-technical way and draws parallels with other areas of public safety and public health. It uses examples from the media and from public policy debates to paint a clear picture of a flawed public policy approach and offers preventive medicine principles to take the field forward\"-- Provided by publisher.
Book
Driving Safety and Comfort Enhancement in Urban Underground Interchanges via Driving Simulation and Machine Learning
Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by the interaction of diverse road users, which heightens the risk of accidents. To enhance both safety and sustainability, this study integrates advanced driving simulation techniques with machine learning models to improve driving safety and comfort in underground interchanges. By utilizing a driving simulator and 3D modeling, real-world conditions were replicated to design key traffic safety features with an emphasis on sustainability and driver well-being. Critical safety parameters, including speed, acceleration, and pedal use, were analyzed alongside comfort metrics such as lateral acceleration and steering torque. The LightGBM machine learning model was used to classify safety and comfort grades with an accuracy of 97.06%. An important ranking identified entrance signage and deceleration zones as having the greatest impact on safety and comfort, while basic road sections were less influential. These findings underscore the importance of considering visual cues, such as markings and wall color, in creating safer and more comfortable underground road systems. This study’s methodology and results offer valuable insights for urban planners and engineers aiming to design transportation systems that are both safe and aligned with sustainable urban mobility objectives.
Journal Article