Explore the vast range of titles available.

بينت الإحصائيات الرسمية أن الجزائر صارت تسجل أكثر من 4812 حالة وفاة سنويا بسبب ارتفاع نسبة حوادث المرور، وهي تخلف كذلك أكثر من 65263 جريحا يتوفي عدد كبير منهم لاحقا في الشهر الأول بسبب مضاعفات الإصابة وأن هذه الحوادث أول سبب للوفاة بالنسبة للذكور ما بين 15 و 25 سنة، أما خسائرها المادية فتتجاوز 75 مليار دينار جزائري، وأن المتسبب الرئيسي في جل هذه الحوادث هو الإنسان مما يستوجب دق ناقوس الخطر واتخاذ جميع التدابير الردعية للحد من هذه الظاهرة أو تقليصها.

تؤكد الدراسات السابقة على مدي ما تمثله المخاطر المرورية من هم وطني وإقليمي وعالمي ولذلك كان البحث عن دراسة مدي وعي الشباب بالمخاطر المرورية أمراً لابد منه، وكان لابد كذلك من الحديث عن الدور التربوي الذي يجب أن تقوم به المؤسسات التربوية المعدة للنشء في التوجيه المروري المبكر على الصعيدين التوعوي والوقائي وعلى الأخص دور الأسرة والمدرسة، ووضع تصور لدور تلك المؤسسات لتوعية الشباب بالمخاطر المرورية، وعي هذا تحددت أسئلة الدراسة في الكشف عن إجابة السؤال الرئيس الاتي: ما التصور المقترح لدور بعض المؤسسات التربوية بمنطقة القصيم في تنمية وعي الشباب بالمخاطر المرورية ؟ كما تهدف الدراسة الحالية إلى تحديد أهم المخاطر المرورية التي يمارسها أو يتعرض لها طلاب المرحلة الثانوية والكشف عن مدي وعي الطلاب بهذه المخاطر، وكذلك الكشف عن واقع دور الأسرة والمدرسة في توعية للحد من تلك المخاطر مع تقديم تصور مقترح لدور كل منهما. ولتحقيق الهدف من الدراسة تم إعداد مقياس وعي الطلاب بالمخاطر المرورية وكذلك إعداد استبيان لتحديد واقع دور الأسرة في توعية الطلاب للحد من المخاطر المرورية وتطبيقهما على عينة من الطلاب. واستبيان آخر لتحديد واقع دور المدرسة في توعية الطلاب للحد من المخاطر المرورية تم تطبيقه على عينة من المعلمين. وقد أسفرت النتائج عن تدني مستوي وعي الشباب من طلاب المرحلة الثانوية بالمخاطر المرورية التي يمارسونها أو يعرضون لها، وكذلك بينت نتائج الدراسة تدني مستوي اهتمام الأسرة بتوعية وتوجيه أبناءها نحو المخاطر المرورية، كذلك تدني مستوي اهتمام المدرسة بتوعية وتوجيه أبناءها نحو المخاطر، حيت حصلت جميع بنود الاستبيان على مستوي ضعيف، وفي نهاية الدراسة تم عرض تصور مقترح لدور كل من الأسرة والمدرسة في توعية الأبناء من الطلاب بالمخاطر المرورية.

Context: YOLO (You Look Only Once) is an algorithm based on deep neural networks with real-time object detection capabilities. This state-of-the-art technology is widely available, mainly due to its speed and precision. Since its conception, YOLO has been applied to detect and recognize traffic signs, pedestrians, traffic lights, vehicles, and so on. Objective: The goal of this research is to systematically analyze the YOLO object detection algorithm, applied to traffic sign detection and recognition systems, from five relevant aspects of this technology: applications, datasets, metrics, hardware, and challenges. Method: This study performs a systematic literature review (SLR) of studies on traffic sign detection and recognition using YOLO published in the years 2016–2022. Results: The search found 115 primary studies relevant to the goal of this research. After analyzing these investigations, the following relevant results were obtained. The most common applications of YOLO in this field are vehicular security and intelligent and autonomous vehicles. The majority of the sign datasets used to train, test, and validate YOLO-based systems are publicly available, with an emphasis on datasets from Germany and China. It has also been discovered that most works present sophisticated detection, classification, and processing speed metrics for traffic sign detection and recognition systems by using the different versions of YOLO. In addition, the most popular desktop data processing hardwares are Nvidia RTX 2080 and Titan Tesla V100 and, in the case of embedded or mobile GPU platforms, Jetson Xavier NX. Finally, seven relevant challenges that these systems face when operating in real road conditions have been identified. With this in mind, research has been reclassified to address these challenges in each case. Conclusions: This SLR is the most relevant and current work in the field of technology development applied to the detection and recognition of traffic signs using YOLO. In addition, insights are provided about future work that could be conducted to improve the field.

Public transportation plays a crucial role in our lives, and the road network is a vital component in the implementation of smart cities. Recent advancements in AI have enabled the development of advanced monitoring systems capable of detecting anomalies in road surfaces and road signs, which can lead to serious accidents. This paper presents an innovative approach to enhance road safety through the detection and classification of traffic signs and road surface damage using advanced deep learning techniques (CNN), achieving over 90% precision and accuracy in both detection and classification of traffic signs and road surface damage. This integrated approach supports proactive maintenance strategies, improving road safety and resource allocation for the Molise region and the city of Campobasso. The resulting system, developed as part of the CTE Molise research project funded by the Italian Minister of Economic Growth (MIMIT), leverages cutting-edge technologies such as cloud computing and High-Performance Computing with GPU utilization. It serves as a valuable tool for municipalities, for the quick detection of anomalies and the prompt organization of maintenance operations.

The visibility of vertical road signs is a crucial factor for driving safety, especially in low-light conditions. The retroreflectivity of signs is imperative to ensure that drivers are able to perceive the information in a timely manner. However, the effectiveness of signs can be compromised by factors such as material degradation, wear and tear, and dirt on the surface. The objective of this study is to analyze how different surface conditions and different levels of retroreflectivity of vertical signs affect users’ perception and driving behavior in a real controlled environment. A total of twenty-five volunteers undertook the same road test twice. During the initial trial, the subjects encountered signs with a Class II retro-reflective film (EN 12899-1:2007), and during the second trial, they encountered the same signs in the same positions as the first trial but with varied characteristics and additional factors such as dirt, water, and degradation. Through a Mobile Eye Tracker and a Racelogic Video Vbox, it was possible to investigate the alterations in the visual and kinematic behavior of participants across the two tests. The statistical analysis was conducted using the Wilcoxon test, Spearman’s correlation and regression analysis. The analysis revealed that the signal with a dirty surface had the most significant impact on participants’ perception, showing a substantial reduction in the distance of the first fixation (−15%), a decrease in the number of fixations (−37%), and an increase in the time required for it to be perceived (+40%). This study demonstrates that the maintenance of road sign surfaces is a critical factor in their effectiveness and is as influential as the level of retroreflectivity of the material.

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future.

This research enhances and evaluates the performance of a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller that operates a flexible National Electrical Manufacturers Association (NEMA) phasing and timing scheme responding dynamically to fluctuating traffic demands. The DNB controller is enhanced to (1) use traffic density estimates instead of queues to optimize signal timings; (2) to consider the eight-phase two-ring NEMA controller configuration within the game-theoretic approach; and (3) to consider dynamically adaptable control time steps. The enhanced DNB controller is benchmarked against (1) a fixed-time traffic signal control using the state-of-practice Webster’s method and an emerging Laguna-Du-Rakha (LDR) method for computing the optimum cycle length; (2) a state-of-the-practice actuated traffic signal control; and (3) a state-of-the-art reinforcement learning (RL) traffic signal controller presented in the literature. The controller is tested on two isolated signalized intersections, demonstrating enhanced overall intersection performance compared to the baseline pretimed and actuated controllers at various demand levels, and offers better performance than a previously developed RL controller. Specifically, the DNB controller results in a decrease in the average vehicle delay and queue size by up to 54% and 63%, respectively, compared to Webster’s state-of-the-practice pretimed control. Unlike the RL controller, the DNB controller requires no pre-training while adapting to fluctuating traffic conditions, thereby providing a flexible framework for reducing traffic congestion at signalized intersections. As such, this research contributes to the development of smarter and more responsive urban traffic control systems.