Safety Of Transportation Systems Research Articles

Innovative road distress detection (IR-DD): an efficient and scalable deep learning approach

In the rapidly evolving landscape of transportation infrastructure, the quality and condition of road networks play a pivotal role in societal progress and economic growth. In the realm of road distress detection, traditional methods have long grappled with manual intervention and high costs, requiring trained observers for time-consuming and expensive data collection processes. The limitations of these approaches are compounded by challenges in adapting to diverse road surfaces and handling low-resolution data, particularly in early automated distress survey technologies. This article addresses the critical need for efficient road distress detection, a key component of ensuring safe and reliable transportation systems. Effectively addressing these challenges is crucial for enhancing the efficiency, accuracy, and safety of road distress detection systems. Leveraging advancements in object detection, we introduce the Innovative Road Distress Detection (IR-DD), a novel framework that integrates the YOLOv8 algorithm to enhance the accuracy and real-time capabilities of road distress detection, catering to applications such as smart cities and autonomous vehicles. Our approach incorporates bidirectional feature pyramid network (BiFPN) recursive feature fusion and bidirectional connections to optimize the utilization of multi-scale features, addressing challenges related to information loss and gradients encountered in traditional methods. Comprehensive experimental analysis demonstrates the superior performance, efficiency, and robustness of our integrated approach, positioning it as a cost-effective and compelling alternative to conventional road distress detection methods. Our findings demonstrate the superior performance of our approach compared to other state-of-the-art methods across various evaluation metrics, including precision, recall, F1 score, and mean average precision (mAP) at different intersection over union (IoU) thresholds. Specifically, our method achieves notable results with a precision of 0.666, F1 score of 0.630, mAP@0.5 of 0.650, all while operating at a speed of 86 frames per second (FPS). These outcomes underscore the effectiveness of our approach in real-time road distress detection. This article contributes to the ongoing innovation in object detection techniques, emphasizing the practicality and effectiveness of our proposed solution in advancing the field of road distress detection.

Improving Driving Style in Connected Vehicles via Predicting Road Surface, Traffic, and Driving Style

This paper investigates the application of ensemble learning in improving the accuracy and reliability of predictions in connected vehicle systems, focusing on driving style, road surface quality, and traffic conditions. Our study’s central methodology is the voting classifier ensemble method, which integrates predictions from multiple machine learning models to improve overall predictive performance. Specifically, the ensemble method combines insights from random forest, decision tree, and K-nearest neighbors models, leveraging their individual strengths while compensating for their weaknesses. This approach resulted in high accuracy rates of 94.67% for driving style, 99.10% for road surface, and 98.80% for traffic predictions, demonstrating the robustness of the ensemble technique. Additionally, our research emphasizes the importance of model explanation ability, employing the tree interpreter tool to provide detailed insights into how different features influence predictions. This paper proposes a model based on the algorithm GLOSA for sharing data between connected vehicles and the algorithm CTCRA for sending road information to navigation application users. Based on prediction results using ensemble learning and similarity in driving styles, road surface conditions, and traffic conditions, an ensemble learning approach is used. This not only contributes to the predictions’ transparency and trustworthiness but also highlights the practical implications of ensemble learning in improving real-time decision-making and vehicle safety in intelligent transportation systems. The findings underscore the significant potential of advanced ensemble methods for addressing complex challenges in vehicular data analysis.

New Zealand public transport agencies' responses to COVID‐19: Understanding public transport services, infrastructure and communication measures

AbstractCOVID‐19 brought public health challenges to the public transport system throughout the world. As a result, a range of response measures were implemented to ensure the safety of passengers while maintaining a functioning system. This paper explores the responses of public transport agencies in New Zealand's metropolitan cities (Auckland, Christchurch and Wellington) to the COVID‐19 pandemic by examining their physical and communication measures. We analysed public transport agencies' websites to understand the response measures implemented, including the accessibility of COVID‐19‐related information, and the communication of information via websites and social media platforms. The results show that 13 service responses were implemented across the three public transport agencies, with Auckland implementing the most measures. Seven infrastructural responses were implemented, with Christchurch implementing all seven. Wellington was found to be the public transport agency that used social media the most during the pandemic. The website accessibility findings suggest that the more accessible a public transport agency's website was, the less information was present. The research concludes that all three public transport agencies in New Zealand performed reasonably well and were able to maintain a safe and responsive public transport system.

Investigating the Effects of Crack Orientation and Defects on Pipeline Fatigue Life Through Finite Element Analysis

Abstract In response to the steady rise in global demand for energy resources such as gas and oil, there is a pressing need to enhance the efficiency and safety of pipeline transportation systems. These systems, integral for transferring vast amounts of energy, must operate under increasingly higher pressures and larger diameters without compromising reliability. This study focuses on utilizing finite element analysis (FEA) to investigate the influence of crack orientation and the presence of defects on the fatigue life of pipelines. By simulating internal pressure scenarios and examining various defect characteristics with the AFGROW software, this research applies damage tolerance principles to offer insights into the fatigue behavior of pipelines. The findings can be applied to extend the operational life and ensure the integrity of these critical infrastructures, thereby supporting the sustainable and safe transport of energy resources.

Determinants of traffic safety enforcement behaviour among police officers: A narrative review

Traffic safety enforcement (TSE), which includes policing of traffic laws, is a key component of safe transport systems. Effective TSE depends on law enforcement professionals’ (i.e., police officers’) ability and willingness to proactively engage in TSE activities. By conducting a narrative review of past literature, this article documents (i) factors reported to influence law enforcement professionals’ willingness to engage in TSE, and (ii) policy recommendations to improve TSE and thus traffic safety. The extensive literature review presented in this report identified four main types of factors that influence officers’ willingness to engage in TSE: (i) individual-level factors (e.g., officers’ perceptions of TSE), (ii) situational factors (e.g., traffic offenders’ demographic characteristics), (iii) organization-level factors (e.g., traffic citation quotas), and (iv) community-level factors (e.g., political influence). Policy recommendations to improve TSE recognized that a country’s economic condition poses significant barriers to effective TSE. Results of this literature review summarize and provide insights into factors that influence TSE and can be used by road safety professionals, including transport engineers and law enforcement professionals, to improve TSE efforts and outcomes.

MEDAVET: Traffic Vehicle Anomaly Detection Mechanism based on spatial and temporal structures in vehicle traffic

Road traffic anomaly detection is vital for reducing the number of accidents and ensuring a more efficient and safer transportation system. In highways, where traffic volume and speed limits are high, anomaly detection is not only essential but also considerably more challenging, given the multitude of fast-moving vehicles, often observed from extended distances and diverse angles, occluded by other objects, and subjected to variations in illumination and adverse weather conditions. This complexity has meant that human error often limits anomaly detection, making the role of computer vision systems integral to its success. In light of these challenges, this paper introduces MEDAVET - a sophisticated computer vision system engineered with an innovative mechanism that leverages spatial and temporal structures for high-precision traffic anomaly detection on highways. MEDAVET is assessed in its object tracking and anomaly detection efficacy using the UA-DETRAC and Track 4 benchmarks and has its performance compared with that of an array of state-of-the-art systems. The results have shown that, when MEDAVET’s ability to delimit relevant areas of the highway, through a bipartite graph and the Convex Hull algorithm, is paired with its QuadTree-based spatial and temporal approaches for detecting occluded and stationary vehicles, it emerges as superior in precision, compared to its counterparts, and with a competitive computational efficiency.

TRAFFIC PULSE-ROADWAY CONTROL SYSTEM

Rapid transit and transportation networks are essential to any country's economic progress. Long wait times, lost fuel, and financial losses are the outcomes of poor management and traffic congestion. For the sake of national development, a quick, affordable, and effective traffic control system is thus absolutely essential. In many nations, keeping an eye on and managing urban traffic is turning into a significant issue. The Traffic Monitoring Authority needs to come up with innovative ways to solve this issue because there are more and more cars on the road. Using automation and intelligent control techniques is one option to enhance the current transportation system's traffic flow and safety. Efficient and quick congestion relief is the aim. The project includes sound sensors that can recognize emergency cars as they approach in order to improve emergency vehicle detection. By prioritizing and streamlining emergency vehicle traffic at signalized junctions, this cutting-edge feature seeks to enhance overall traffic management and response times. Additionally, the concept has a novel element for pedestrian safety: when a signal changes from red to orange or orange to green, a dynamically triggered block is deployed. By erecting a barrier that guarantees pedestrians are securely clear of traffic before crossing, this creative solution seeks to improve pedestrian safety. Keywords: Traffic jams, traffic surveillance, automation, and clever control techniques, Changes in signals, sound sensors, emergency vehicle detection, quicker reaction times, barriers, and pedestrian safety.

CHANGE IN THE NUMBER OF TRAFFIC ACCIDENTS IN THE LVIV REGION DURING 2019–2023

Current trends in the development and modernisation of transportation systems focus on safety. However, it is worth conducting an in-depth analysis of data related to traffic safety factors to maximise the safety emphasis in future strategies. This approach concentrates the efforts of engineers and scientists primarily on combating the causes of road accidents rather than their consequences, as is often the case today. The object of research in this article is the street and road network of Lviv region, as it is necessary to take into account the regional aspects of traffic and their differences in the country’s regions, given the density of streets and roads, climatic and hydrometeorological conditions, as well as the typology of roads. One of the scientific problems that the study’s results can partially solve is to identify time patterns and dynamics of changes in the number of road accidents in the Lviv region and compare the results with the targets set out in strategic documents in the field of road safety in Ukraine. The article analyses the data on the number of road traffic accidents in the region from 2019 to 2023. At the same time, it carries out the statistical distribution of traffic accidents by the causes of their occurrence. We also account for the time aspect – a breakdown by time of day and days of the week, including the general trend over the past years. We established that the dynamics of the number and severity of road accidents for the period under consideration is negative; however, the patterns of their occurrence persist, which explains the positive statistical result obtained due to quantitative but not qualitative factors affecting traffic safety. The results obtained are of more value from an engineering and scientific point of view, particularly in comparison with traffic accident statistics disseminated on information resources or social media. The analysis conducted in this article allows us to identify problematic areas in the functioning of both the bodies that ensure the safety and organisation of road traffic and transport systems. Keywords: road safety, road traffic accident, accident rate, transport systems.

Development of Fuzzy Logic Controller in Automatic Vehicle Navigation using IoT

The integration of fuzzy logic controllers in automatic vehicle navigation systems represents a significant advancement in intelligent transportation systems, especially when paired with Internet of Things (IoT) functionalities and optimized through genetic algorithms. This innovative fusion harnesses the precision of fuzzy logic, the connectivity of IoT, and the optimization capabilities of genetic algorithms to transform automatic vehicle navigation. Fuzzy logic controllers excel in managing uncertainty and imprecision, providing decision-making capabilities akin to human reasoning. By simultaneously assessing multiple inputs and determining actions based on degrees of truth, fuzzy logic enables safe and efficient navigation in dynamic driving environments with fluctuating variables like obstacle proximity and traffic flow. IoT integration enhances navigation systems by enabling real-time data collection and sharing among vehicles and infrastructure, fostering adaptive route planning and improving the overall navigation experience. Genetic algorithms further optimize system performance by iteratively adjusting fuzzy logic controller parameters, ensuring efficient decision-making tailored to specific performance criteria such as travel time and fuel consumption. This collaborative integration of fuzzy logic controllers, IoT, and genetic algorithms offers a holistic solution to the challenges of automatic vehicle navigation, enhancing safety, efficiency, and adaptability in complex driving scenarios. Beyond enhancing individual vehicle performance, this approach contributes to overall transportation system efficiency and safety by mitigating traffic congestion, reducing emissions, and minimizing accidents. Consequently, these integrated systems address crucial societal challenges and pave the way for widespread adoption of autonomous vehicles in the future.

Exploring the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka

The COVID-19 outbreak created immense disruptions in our daily lives. Travel is one of the major areas severely impacted by the pandemic. Bangladesh experienced two waves of COVID-19 between March 2020 and July 2021. Although several studies focused on significant changes in the travel behavior of urban road users before and during the pandemic, hardly any research examined the differences between the two waves. Hence, it was important to study the differences in travel behavior between the two waves of the COVID-19 pandemic to understand how the effects of travel restrictions, health concerns, and the severity of the pandemic on people's travel decisions change over time. This study intends to investigate the changes in travel behavior between the first and second waves of the COVID-19 pandemic in Dhaka and the factors that influence these changes. Data were collected through online questionnaire surveys using the Google form. Voluntary response and convenience sampling techniques were used to collect responses from 447 people in Dhaka. Descriptive statistics and non-parametric tests were conducted to analyze the data. In addition, two multinomial logistic models were developed to identify the factors behind the changes in travel behavior. This study reveals that work and shopping trips increased, whereas work-from-home and online shopping somewhat decreased during the second pandemic wave compared to the first pandemic wave. Most non-car owners who reduced their use of public transportation during the first pandemic increased their use during the second wave. There was no significant increase in the use of active transport modes for work and shopping trips in Dhaka. The perceived risk of COVID-19 infection was relatively lower during the second wave, influencing the respondents to travel more frequently for work and shopping purposes. Ensuring more adaptive public transportation, flexibility to work from home, and creating supportive infrastructures for active transport modes might help to provide a safe, affordable, and efficient transportation system for all during the future waves of the pandemic and other unprecedented events.

DRCLAS: An efficient certificateless aggregate signature scheme with dynamic revocation in vehicular ad-hoc networks

The emergence of the Vehicular Ad-hoc Networks (VANETs) has greatly enhanced the efficiency and safety of transportation systems. However, due to the dynamicness and openness of vehicular networks, it is vulnerable for VANETs to confront various security threats. The dissemination of disinformation and the occurrence of attacks within these networks can result in severe damage and compromise the overall security of transportation systems. To mitigate such challenges, this paper presents an efficient certificateless aggregate signature scheme with dynamic revocation in VANETs. Taking use of the early-stopping factorial bitwise divisions (EFBD) algorithm, it achieves the invalid signatures tracking, and revokes the malicious vehicles dynamically. In addition, by combining the fingerprint counting bloom filter (FP-CBF), the trust assessment mechanism and the cloud server, it enables the vehicle to remedy its incorrect signatures. At last, through the comprehensive security and performance analysis, it demonstrates that this protocol enjoys improved communication security and computational efficiency.

Safe and Inclusive School Bus Transportation System for Special Needs Children

This white paper does a thorough analysis of school-age special needs children, the current challenges, and surveys a list of all the resources, guidelines, bodies, policies, and best practices that need to be followed in conjunction with each other for this to be successful. It discusses various policies and programs, including those established by the US Department of Health and Welfare (HHS), the U.S. Department of Education, the American Academy of Pediatrics (AAP), and the NHTSA regarding children with special needs, the categorization of disabilities in children, and their various health care requirements. The article then discusses the functions of other bodies, starting with the IEP (Individualized Education Program), which is tailored to each kid and includes details on the services and goals that each student will get in terms of education. Besides these regulations and guidelines, the white paper also discusses experiential-based industry best practices in special education student transportation and emphasizes how the parents, the school bus contractor services with its bus services and equipped fleet, the school bus driver, and the accompanying attendant/monitor with the child, the vehicle - school bus /van, the equipment on the bus, the kind of training that needs to be imparted to all these bodies and also the role of school administrators and staff - all in conjunction with each other is so important and that all these need to come together to give special needs kids the best school bus transportation.

Use of Technology to Promote Road Safety: Public Health Perspective

ABSTRACT Road safety has been acknowledged as a public health concern of global significance owing to its direct association with human lives and their well-being. Road safety is a much broader concept than measures that are aimed to just prevent accidents, but it extends to improving the quality of life of members of the community, safeguarding environmental health, and improving the economy of the nation. With the passage of time, several technologies in road safety have surfaced, and their collective employment can aid in the establishment of a smart, safe, and efficient transportation system in nations. In fact, these technologies will account for a significant reduction in the number of accidents, better management of traffic, and improved road safety. In conclusion, ensuring road safety can save lives, promote responsible behavior, and create secure transportation system. There is an immense need to create awareness, engage the community, and embrace technology to minimize the possibility of road accidents and ensure safe journeys for all.

Safe and Secure Oil and Gas Pipeline Transportation System Based on Industrial Internet of Things

Safe and Secure Oil and Gas Pipeline Transportation System Based on Industrial Internet of Things

Enhancing road safety through advanced predictive analytics in V2X communication networks

"SafeRouteX," which stands for "Enhancing Road Safety through Advanced Predictive Analytics in V2X Communication Networks," is a novel approach to Vehicle-to-Everything (V2X) networks. Despite advancements in vehicle and road safety technologies, traffic accidents continue to be a significant problem. To increase road safety, SafeRouteX uses cutting-edge machine learning to forecast traffic accidents in V2X networks. The first data is processed by a data ingestion layer, and then the temporal patterns and uncertainties in the traffic data are extracted using LSTM-VAE features. The Elastic Net Layer minimizes dimensionality while retaining predictive power after feature selection optimization. A RandomForest classifier, which performs well on complicated, high-dimensional datasets, is the main predictive model. Combining approaches guarantees a solid strategy for the complexity analysis of traffic data. Using 4-fold cross-validation, the accuracy and generalizability of SafeRouteX were thoroughly evaluated across a range of traffic scenarios. This validation approach demonstrated SafeRouteX's ability to anticipate traffic accidents, highlighting its potential to enhance road safety in smart transportation systems.

Vehicle to Vehicle Communication for Crash Avoidance System

The Dramatic increase in the traffic flow raises demand on innovative technologies that can improve safety and efficiency of transportation systems. Road safety can be substantially enhanced by the deployment of wireless communication technologies for vehicular networks, which enable new services such as collision detection traffic management, and further communication facilities between moving vehicles. Aiming at providing reliable wireless communications for vehicular networks the RF communication will serve as an underlying protocol for future inter-vehicular applications worldwide. This paper presents an implementation of a complete vehicle to vehicle communication, designed according to the specification. In addition to this a blind spot detection system for protection against misshapen like vehicle collisions that causes loss of human lives is being implemented. The blind spot detection system will be useful while changing the lane. Ultrasonic sensors, Raspberry pi, RF module and GPS modules are used to implement the complete design The main aim of V2V communication is to prevent accidents by allowing vehicles in transit to send position and speed data to one another. The vehicle’s driver may simply receive a warning should there be a risk of an accident or the vehicle itself may take preemptive actions as braking to slow down.

Evaluating fatigue management regulations for flight crew in Australia using a new Fatigue Regulation Evaluation Framework (FREF)

Flight crew experience fatigue due to sleep loss, circadian desynchrony, early duty start times, long duty periods, high and low workloads, and unscheduled duties. As fatigue is considered a significant safety risk in the air transport industry, the primary approaches to managing fatigue are mandating prescriptive limits for work and rest periods or adopting performance-based fatigue risk management systems (FRMS). However, recent aviation incidents indicate that current regulations may not be adequate to manage the fatigue risk experienced by the flight crew. This study evaluates Australian flight crew fatigue management regulations using a new fatigue regulation evaluation framework (FREF) adapted from Jones et al. (2005). Results show that Australian regulations for flight crew include limits on flight duty period (FDP), off duty period, FDP start time, sleep requirements for flight crew before starting an FDP, high and low workloads, circadian rhythm disruptions, and fatigue awareness. However, there are regulation variations in addressing lengths of sectors and methods to report fatigue, which may need to be reviewed to manage fatigue better. Recommendations are made to consider incorporating these factors into fatigue regulations to ensure a safe air transport system.

ИНСТРУМЕНТЫ И МЕТОДЫ ПОВЫШЕНИЯ БЕЗОПАСНОСТИ ТРАНСПОРТНЫХ СИСТЕМ В ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ

The economic development of the state is connected with the safety of transport systems, transport infrastructure is of exceptional importance in national security, which determined the relevance of the research topic. The theoretical provisions of modern risk management, methods of risk identification, risks of emergency situations at transport enterprises are considered. The risk properties of a transport enterprise are described, a qualitative risk assessment and options for classifying risks of a transport enterprise are presented. Tools for detecting risks of transport systems in emergency situations are presented. The measures of control of identified risks at the enterprises of the transport system are proposed, the effect of their introduction into the practice of the transport enterprise is revealed. A situational approach to forecasting risks at a transport enterprise is recommended. The proposed tools and methods make it possible to improve the safety of transport systems in emergency enterprises. The reliability of the obtained results is determined. Publications on the results of the study of this research topic are presented. Security issues of the transport system in emergency situations have been developed.

Study on ductile fracture behavior of hydrogen-charged API pipeline steel

With the increasing focus on reducing carbon emissions, hydrogen has emerged as a promising alternative energy source. However, the safe transportation of hydrogen poses challenges due to its potential impact on the integrity of pipeline materials. This study aims to investigate the effect of hydrogen charging time on the plastic behavior and ductile fracture characteristics of API 5L X42 pipelines, which are commonly used for transporting hydrocarbons. Tensile tests were conducted on various types of hydrogen-charged specimens at room temperature to assess different fracture modes. Hydrogen was introduced into the specimens using a cathodic electrolytic method, and 24-h charging time durations were considered. Finite element analyses were performed on standard dog-bone and notched tension specimens to evaluate the plastic behavior, employing the Swift hardening law to model the flow stress. Numerical analyses were conducted to determine the loading path leading to fracture initiation. A damage framework based on the Hosford–Coulomb model was utilized to predict ductile fracture under non-proportional loading conditions. The findings of this study provide insights into the fracture behavior of pipelines under hydrogen exposure, aiding in the design and assessment of safe and reliable hydrogen transport systems.

Sensors, standards and analysis techniques for road transport vibration: A systematic review

This comprehensive review paper analyzes 20 published articles focused on the instrumentation and analysis of road transport vibrations. The study encompasses the distribution of articles based on databases, journals, and publication years, revealing trends in research sources. The examination of sensors and standards emphasizes the critical role of vibration sensor selection and the application of standards, with a focus on accelerometer designs and the prevalence of ASTM D4169-16. The paper delves into various analytical methods, highlighting the frequent use of power spectral density (PSD) and the application of these methods in understanding vibration frequencies and their effects on different transportation conditions. The review identifies key areas for future research, emphasizing the need for refined instrumentation methodologies, standardized standards, and exploration of advanced analytical techniques, considering the dynamic nature of real-world vibrations and emerging technologies in the road transport landscape. Additionally, the review suggests future investigations into optimizing packaging designs and developing innovative materials with vibration-damping properties to enhance the safety and efficiency of road transportation systems.