Enhance Road Safety Research Articles

Traffic Sign Detection under Foggy Conditions using Machine Learning

Traffic sign detection is crucial for enhancing road safety, especially under adverse weather conditions like fog. This paper explores the challenges of detecting traffic signs in fog and presents an approach that incorporates dehazing techniques to improve detection accuracy. We implement a combination of YOLO (You Only Look Once) and HOG (Histogram of Oriented Gradients) algorithms, evaluating their performance on various datasets. Our findings indicate a significant improvement in detection rates under foggy conditions, suggesting that integrating dehazing techniques can enhance visibility and road safety.

Assessing Satellite-Augmented Connected Vehicle Technology for Security Credentials and Traveler Information Delivery

Vehicle-to-Everything (V2X) technology has the capability to enhance road safety by enabling wireless exchange of telematics and spatiotemporal information between connected vehicles (CVs). Effective V2X communication depends on rapid information sharing between Roadside Units (RSUs), in-vehicle On-Board Units (OBUs), and other connected infrastructure. However, there are increasing concerns with RSUs related to installation needs, reliability, and coverage, especially on rural roadways. This study aims to evaluate the benefits of augmenting CV infrastructure with satellite technology in situations where RSU access or coverage is limited while maintaining V2X security protocols and critical information exchange. The study utilizes data from over 400 personal, fleet, and commercial CVs collected during two real-world pilot deployments in the United States, one in an urban environment in Florida and one in a rural environment in Wyoming. The analysis performed shows that the delivery of critical security credential information and traveler information messages (TIMs) to CVs is dependent on a multitude of environmental and operational reliability factors. Overall, information delivery is faster with dense RSU infrastructure as compared to satellites. However, we show that by augmenting RSU infrastructure with satellite technology, the delivery of information is more robust, improving V2X system reliability, security, and overall road safety.

Development of Conflict-Based Safety Performance Function for Safety Evaluation of Urban Roundabouts in India

This study evaluates the safety performance of urban roundabouts in India using conflict-based Safety Performance Functions (SPFs). High-resolution aerial videos were utilized to extract vehicle trajectories, and the Surrogate Safety Assessment Model (SSAM) was employed for conflict identification. Data were collected from 27 roundabouts across various Indian cities, focusing on non-lane-based heterogeneous traffic conditions. Four SPFs were developed utilizing a Generalized Linear Model with Negative Binomial (NB) distribution and log link function for different proximity levels based on Time-to-Collision (TTC) and Post-Encroachment Time (PET) values. Key factors influencing conflict frequency included conflicting traffic volume, occupancy time, ratio of approaching and conflicting traffic volume, percentage of cars and motorcycles, inscribed circle diameter, roundabout symmetry, and number of legs. The developed conflict frequency prediction models fitted well and demonstrated robust predictive performance. This proactive framework for roundabout safety assessment can be followed to enhance road safety in developing nations.

Analysing crash severity on expressways in India using statistical and ML approaches

The high injury severity of traffic crashes on Indian expressways is a significant concern for road safety experts, although studies dedicated to this critical issue are limited. The present study investigates the factors influencing crash severity using multinomial logit (MNL), decision tree (DT) and random forest (RF) models on a dataset of 2,747 crashes on three selected expressways. The dependent variable, crash severity, had four injury severity categories: fatal, severe, minor, and property damage only (PDO). Various explanatory variables, included traffic and speed characteristics, temporal and geometric characteristics, primary contributing factors, crash type, and the vehicles involved. Synthetic minority oversampling (SMOTE) and randomise class balancing (RCB) techniques were also employed to tackle the class imbalance issue in the dataset. The predictive performance of models was evaluated using classification accuracy and Kappa value. The RF model showed the highest predictive accuracy on the RCB dataset. The key findings highlight the critical need for enforcement of speed limits and entry restrictions, lane discipline, and improvement of design deficiencies such as provisions of truck laybys and bus bays. These measures can help in policy-making and engineering improvements to enhance road safety on expressways in India.

Analyzing the Relationship Between User Feedback and Traffic Accidents Through Crowdsourced Data

Identifying road segments with a high crash incidence is essential for improving road safety. Conventional methods for detecting these segments rely on historical data from various sensors, which may inadequately capture rapidly changing road conditions and emerging hazards. To address these limitations, this study proposes leveraging crowdsourced data alongside historical traffic accident records to identify areas prone to crashes. By integrating real-time public observations and user feedback, the research hypothesizes that traffic accidents are more likely to occur in areas with frequent user-reported feedback. To evaluate this hypothesis, spatial autocorrelation and clustering analyses are conducted on both crowdsourced data and accident records. After defining hotspot areas based on user feedback and fatal accident records, a density analysis is performed on such hotspots. The results indicate that integrating crowdsourced data can complement traditional methods, providing a more dynamic and adaptive framework for identifying and mitigating road-related risks. Furthermore, this study demonstrates that crowdsourced data can serve as a strategic and sustainable resource for enhancing road safety and informing more effective road management practices.

A Review of Current Research and Future Development of Autonomous Driving Technology

Abstract: At present, the automobile industry is undergoing a new round of technological revolution, and autonomous driving technology is also experiencing a period of rapid development, with very active research and technology iteration. Autonomous driving can prevent accidents that occur due to human errors such as inattentiveness and driver fatigue, thereby enhancing road safety. Additionally, autonomous technology helps alleviate traffic congestion and optimize flow by utilizing coordinated driving strategies, such as vehicle-to-vehicle communication (V2V) and vehicle-to-infrastructure communication (V2I), enabling platooning, intelligent rerouting, and other advanced driving techniques. The autonomous driving system can control the vehicle according to the optimal driving strategy, can drive the vehicle in a smoother way, optimize fuel consumption or power use, thereby reducing operating costs and improving travel experience, and has a wide range of application potential and potential social value. This paper summarizes the current research status and development trend of autonomous driving technology, summarizes the current research status of autonomous driving technology from the aspects of environmental perception, machine learning, control execution, etc., while also projecting future directions for the field. Advancements in this technology have the potential to revolutionize transportation, offering significant convenience and transforming daily life. Moreover, it can promote the development of major industries, increase national GDP, affect urban planning and layout, and even improve our living environment and benefit ecological construction.

Enhancing intersection safety: Testing measures to reduce law violations in Loja (Ecuador)

This study evaluates the effectiveness of measures aimed at reducing traffic violations, specifically focusing on wrong-way driving, at intersections in Loja, Ecuador. The high incidence of accidents at these intersections, often resulting from wrong-way driving and non-compliance with traffic regulations, underscores the critical need for effective strategies to enhance road safety. To address this issue, we adopted a multidisciplinary approach to assess the impact of two specific interventions: the implementation of official warnings and the presence of traffic officers at a selected intersection. Data collection involved recording instances of traffic violations, administering road safety surveys, and monitoring the implementation of these interventions. The post-implementation analysis sought to determine the effect of these measures on driver behavior and overall traffic safety. Our findings indicate that while the interventions succeeded in increasing awareness about traffic violations, they did not produce a significant reduction in undesirable driving behaviors. This suggests that, although the presence of warnings and traffic officers is beneficial in raising awareness, these measures alone may not be sufficient to effect substantial behavioral changes. The research provides valuable insights for the development of more comprehensive road safety strategies and emphasizes the need for further studies to explore and address the underlying causes of traffic violations.

Enhancing Road Safety with Smart Infrastructure: Development and Implementation of Advanced Zebra Crossings and Speed Breaking

Abstract: This study addresses a critical issue in road safety by proposing innovative solutions to mitigate accidents caused by speeding and reckless driving, particularly in high-risk areas such as highways, hospitals, and schools. With road traffic accidents accounting for approximately 1.3 million fatalities annually, along with 20 to 50 million injuries or disabilities, the inefficacy of traditional traffic control systems is a growing concern. Factors such as poor visibility, adverse weather, and driver distractions further compromise the effectiveness of conventional signs and signals. This research introduces a "smart zebra crossing" system that integrates ultrasonic sensors and embedded LED lights to provide real-time alerts to both drivers and pedestrians, thereby enhancing visibility and safety, especially under low-light conditions. Additionally, a "smart speed breaking" system is proposed, which dynamically adjusts its height based on vehicle speed, detected through ultrasonic sensors, prompting drivers to decelerate when necessary. Both systems are linked to a central traffic management network, facilitating real-time adjustments in speed limits and providing alerts to connected vehicles. The integration of these technologies represents a significant advancement in road safety, offering an adaptive, intelligent solution for improving traffic management and reducing the risk of accidents, particularly in vulnerable zones.

Accident Prevention System for Cars, Buses, Trucks

Driver drowsiness is a significant factor in road accidents, contributing to a large number of crashes annually. To enhance road safety, the development of a Drivers Drowsiness Detection System is essential. This system builds to detect early signs of lazyness in drivers and provide timely alerts to prevent accidents. The proposed system uses to track key indicators of drowsiness, such as eye closure, head position, yawning, and blink rate. By utilizing a camera-based real-time monitoring system, it continuously analyzes the driver’s facial expressions. The system employs cadcade algorithms to distinguish a featurebased object detection algorithm to detect objects from images between alert and drowsy states based on collected data. When drowsiness is detected, the system triggers alerts like audio warnings, seat vibrations, or even vehicle slowing mechanisms, depending on the integration. This technology has the potential to significantly reduce the number of road accidents related to driver fatigue and could be applied in both personal vehicles and commercial fleets. The implementation of such systems represents a critical step toward improving road safety and decreasing driver-related incidents. Nowadays it is very challenging to stay active all the time due to busy schedules. Falling asleep while driving can lead to serious consequences, accidents, and even death. This situation is much more common and therefore it is very important to solve this problem. So, to solve this problem, we developed a sleep alarm system for drivers. This system alerts the user when he falls asleep at the wheel, thus the main concern is preventing accidents and saving lives. This system is useful for long- distance travelers and late-night drivers.

Metaheuristic optimized complex-valued dilated recurrent neural network for attack detection in internet of vehicular communications

The Internet of Vehicles (IoV) is a specialized iteration of the Internet of Things (IoT) tailored to facilitate communication and connectivity among vehicles and their environment. It harnesses the power of advanced technologies such as cloud computing, wireless communication, and data analytics to seamlessly exchange real-time data among vehicles, road-side infrastructure, traffic management systems, and other entities. The primary objectives of this real-time data exchange include enhancing road safety, reducing traffic congestion, boosting traffic flow efficiency, and enriching the driving experience. Through the IoV, vehicles can share information about traffic conditions, weather forecasts, road hazards, and other relevant data, fostering smarter, safer, and more efficient transportation networks. Developing, implementing and maintaining sophisticated techniques for detecting attacks present significant challenges and costs, which might limit their deployment, especially in smaller settings or those with constrained resources. To overcome these drawbacks, this article outlines developing an innovative attack detection model for the IoV using advanced deep learning techniques. The model aims to enhance security in vehicular networks by efficiently identifying attacks. Initially, data is collected from online databases and subjected to an optimal feature extraction process. During this phase, the Enhanced Exploitation in Hybrid Leader-based Optimization (EEHLO) method is employed to select the optimal features. These features are utilized by a Complex-Valued Dilated Recurrent Neural Network (CV-DRNN) to detect attacks within vehicle networks accurately. The performance of this novel attack detection model is rigorously evaluated and compared with that of traditional models using a variety of metrics.

A lightweight and explainable model for driver abnormal behavior recognition

With the advancement of intelligent transportation systems, accurate identification of driver abnormal behavior is crucial for enhancing road safety. However, the limited computing power of vehicular systems poses a challenge for running efficient and explainable behavior recognition models. This paper proposes a lightweight and explainable driver abnormal behavior recognition model based on an improved You Only Look Once version 8 (YOLOv8). Firstly, a Spatial and Channel Reconstruction Convolution (SCConv) module is introduced to optimize the Convolution to Feature (C2f) structure, enhancing the model's feature extraction capabilities while reducing parameter redundancy. Secondly, a Spatial Pyramid Pooling with Fast Large Separable Kernel Attention (SPPF-LSKA) module is designed to better capture image context and integrate global information. Additionally, a Dynamic upsample (Dysample) module is introduced to improve the model's ability to capture subtle driver movements. Lastly, a Lightweight Shared Group Normalization Convolution Detection Head (LSGCDH) is designed to enhance the model's generalization ability, significantly reducing the model's computational load, parameter count, and size. Experimental results demonstrate that our approach has significant advantages for edge device deployment compared to mainstream algorithms. The visualization results effectively corroborate the role of each improved structure, enhancing the explainability of the abnormal behavior recognition model, which is beneficial for deployment in vehicular systems and contributes to improving road traffic safety.

Investigating the Influence of Time Pressure on Risky Riding Behaviour among P-Hailing Riders in Malaysia: The Mediating Role of Moral Disengagement

This study investigates the impact of time pressure on risky riding behaviour among p-hailing riders in Malaysia, with moral disengagement as a mediating factor. Drawing on Bandura's Moral Disengagement Theory and the Job Demand-Resources (JD-R) Model, the research explores how time pressure influences these behaviours. A survey of 200 respondents, selected through stratified convenience sampling, was conducted across three Northern States in Malaysia, namely Perlis, Kedah, and Penang. Data analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM) reveals that time pressure significantly increases risky riding behaviour, with moral disengagement significantly mediates this relationship. The study finds that younger and less experienced riders are particularly susceptible to time pressure effects. Demographic data indicate that 80% of riders are under 35 years old, with 75% having less than three years of experience. Based on these findings, the research emphasises the need for targeted interventions, including specialised training programmes and improved workload management strategies, to mitigate risks associated with p-hailing. The study contributes to a deeper understanding of the role of a cognitive mechanism factor in influencing risky riding behaviours among p-hailing riders and provides valuable insights for enhancing road safety measures. Future research directions are suggested, including exploration of additional cognitive mechanisms that may impact risky riding behaviours in this context.

A Decentralized Digital Watermarking Framework for Secure and Auditable Video Data in Smart Vehicular Networks

Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a Decentralized Digital Watermarking framework for smart Vehicular networks (D2WaVe). D2WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a Blockchain-based Video frames Authentication scheme. By leveraging an encoder–noise–decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2WaVe scheme for securing and auditing video data exchange in smart vehicular networks.

Multifeatured Electronic Helmet to Enhance Road Safety and Rider’s Comfort

This paper presents a multi-featured electronic helmet designed to tackle critical road safety issues, including accidents, drunk driving, and over-speeding. The design incorporates a global system for mobile communications (GSM) and global positioning system (GPS) modules to accurately capture driver’s current location and send messages to predefined contacts. When encountering an accident, the helmet promptly notifies designated contacts and authorities, ensuring swift assistance. By integrating features for detecting over-speeding, accidents, and drunk driving, the helmet renders real-time alerts to both the driver’s family and traffic police, enhancing accountability and safety measures. Additionally, the helmet includes solar charging functionality for mobile devices receive 100% charged in 3.37 hours, thereby optimizing usability and emergency communication. A rain detection system protects mobile devices, while an internal warming mechanism caters to adverse weather conditions, and proffers enhanced comfort by maintaining internal form temperature between 26-27 ℃ and safety, especially in cold regions or for military personnel.

SafeDrive Pro IoT Based Driver Verification System

SafeDrive Pro is an IoT-based Driver Verification System designed to address the rise in vehicular accidents involving underage and unlicensed drivers. By integrating multi-factor authentication, including age verification, facial recognition, and driver license validation, SafeDrive Pro ensures only authorized individuals can operate vehicles. The system aims to improve vehicle security, reduce unauthorized usage, and enhance road safety. The proposed system leverages IoT technology, machine learning for facial recognition, and RFID for license validation. Initial results indicate that the system can effectively prevent unauthorized access, offering a robust approach to reducing road accidents and promoting vehicle security

Analysis of the environmental factors contributing to road traffic accidents involving school-aged pedestrian children in urban Algerian settings

Despite efforts by the Algerian government and civil society to improve road safety, traffic accidents remain a significant issue, particularly affecting children and youth. Data from the National Delegation for Road Safety (NDSR) reveals that this demographic is especially vulnerable, with 371 child fatalities recorded during the 2020-2021 school year. This study evaluated road safety around twelve primary schools in Oued El Abtal district, Mascara province, and aimed to identify effective interventions through a participatory approach involving 1,074 students. An anonymous questionnaire was used to capture students' perceptions of road risks, and the data were analyzed using IBM SPSS Statistics V27. Chi-square and Fisher’s exact tests were applied to examine associations between gender and variables like travel mode, school accompaniment, student behavior, and crossing locations. The sample was balanced by gender, consisting of 50.9% boys and 49.1% girls. Walking was the predominant travel mode (75.2%; n=808). Key risk factors included inadequate infrastructure (3.2%), insufficient signage (11.7%), sidewalk obstructions (5.1%), dangerous driving behaviors (12.2%, with speeding at 29.5%), heavy traffic (16.2%), and poor urban planning (e.g., lack of speed bumps and low visibility). These findings highlight the urgent need for targeted interventions to improve the safety of school commutes. This research identifies critical risk factors in school travel, providing a foundation for developing strategies aimed at enhancing road safety for children in Algeria.

Traffic Congestion and Safety: Mixed Effects on Total and Fatal Crashes

This paper examines the effects of traffic congestion on total crashes, fatal or serious injury (FSI) crashes, and fatal-only crashes in peak periods using a zone-level safety analysis in Greater Melbourne. Bayesian mixed-effect negative binomial models are employed to investigate the relationship between a congestion index and the frequency of total and FSI crashes. In addition, Bayesian mixed-effect binary logistic models are adopted to explore the association between the congestion index and the likelihood of having fatal crashes in Statistical Area Level 2 (SA2) zones. Modelling results indicate that traffic congestion tends to increase total crashes in both the AM and PM peak periods and FSI crashes in the AM peak period. In contrast, traffic congestion tends to decrease the likelihood of having fatal crashes at both the AM and PM peaks. These findings suggest that many policies to reduce traffic congestion may also enhance road safety by lowering the overall number of crashes. However, it is crucial to incorporate careful speed management within these policies to reduce the risk of fatal crashes effectively.

Distribution and Abundance of Wildlife Roadkills in Jengka, Pahang, Peninsular Malaysia

Malaysia's growing transportation network has increased human impact on wildlife habitats, causing a rise in roadkill cases. This study aims to assess the distribution and abundance of roadkill in Jengka, Pahang. Surveys along selected roads for ten days in March 2022 recorded 72 incidents (0.36 roadkill km-1). The water monitor (Varanus salvator) was the most frequently recorded species with 12 incidents, while the least frequently recorded species, each with 1 incident, included the monocled cobra (Naja kaouthia) and the house rat (Rattus rattus). Species of particular concern based on the International Union for Conservation of Nature (IUCN) Red List Categories included the long-tailed macaque (Macaca fascicularis) (Endangered), clouded monitor lizard (Varanus nebulosus) (Near Threatened), and Malayan flat-shelled turtle (Notochelys platynota) (Vulnerable). Main Federal Roads, particularly Jalan Bukit Tajau - Bandar Pusat Jengka (FT62) and Jalan Utama Jengka Utara/Selatan (FT83), had notable roadkill rates of 0.73 and 0.35 per kilometer, respectively. Jalan FELDA 25 (FT1553), linking Bandar Tun Abdul Razak to Jerantut and Kuala Krau, recorded the highest rate at 0.78 roadkill per kilometer, highlighting the need for attention. Proposed actions include wildlife crossings, wildlife warning reflectors and wildlife warning signs in hotspots, and table drains alongside roads. It is also essential to educate drivers on the importance of reducing speed, especially in wildlife crossing zones, to minimize incidents. These findings stress the importance of proactive measures in reducing roadkill and safeguarding wildlife in Jengka, urging collaborative efforts to enhance road safety for both humans and animals.

Safety Trends in the Automobile Industry in India

The automobile industry in India has experienced significant advancements in recent years, driven by rapid technological innovations and evolving consumer expectations. Safety has emerged as a critical focus area, with stakeholders ranging from government bodies to manufacturers and consumers increasingly emphasizing the need for stringent safety measures. This paper explores the key safety trends shaping the Indian automotive sector, including the adoption of advanced driver-assistance systems (ADAS), the implementation of new crash test standards, and the increased integration of passive and active safety features in vehicles. Additionally, it highlights the regulatory changes brought forth by the Indian government, such as the Bharat New Vehicle Safety Assessment Program (BNVSAP) and mandatory safety norms like airbags and anti-lock braking systems (ABS). The study also examines the role of technological innovations, including connected vehicle technologies and the potential of autonomous driving in enhancing road safety. By analyzing these trends, the paper aims to provide insights into how safety measures in the Indian automobile industry are evolving to meet global standards and reduce the rising number of road accidents in the country.

SMALL OBJECT DETECTION APPROACH BASED ON ENHANCED SINGLE-SHOT DETECTOR FOR DETECTION AND RECOGNITION OF INDONESIAN TRAFFIC SIGNS

The detection and recognition of traffic signs are crucial components of advanced driving assistance systems (ADAS) that enhance road safety. Current traffic sign detection and recognition model technology is proficient in identifying and interpreting traffic signs. However, for accurate detection and recognition, the traffic sign in the image must be of a certain minimum pixel size or distance from the driver's sight line for proper detection. The ADAS system should be capable of detecting and recognizing road traffic signs from a considerable distance as they come into the driver's line of vision. The higher the vehicle speed, the greater the distance required for the sign to be detected and recognized, allowing the driver sufficient time to react according to the sign's meaning. Addressing these challenges, this research proposes an enhanced version of the single shot detector (SSD) algorithm, commonly used in object detection, to improve the algorithm's ability to detect small objects. The proposed method involves adding an auxiliary layer module to the original SSD architecture to increase the feature map resolution and expand the conventional layer's receptive space. With the Enhanced SSD algorithm, the detection capability of the SSD can be significantly enhanced in terms of accuracy. The limitations of this study are related to the influence of occlusion and clutter, which might affect the performance of object detection, especially for small objects, which are more susceptible to being influenced by various factors. The research results demonstrate that Enhanced SSD improves object detection accuracy compared to the original SSD, with a mean average precision (mAP) of 97.87 compared to 95.35 for detecting 21 traffic signs in Indonesia.