Safety effects of work zone advisory systems under the intelligent connected vehicle environment: a microsimulation approach

Mao, Suyi; Xiao, Guiming; Lee, Jae Young; Wang, Zijin; Huang, Helai

doi:10.1108/jicv-07-2020-0006

Cited by 20 publications

(6 citation statements)

References 23 publications

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“…However, seafarers are prohibited from boarding or leaving ships at most ports because of COVID-19 restrictions, which increases seafarers' stress and fatigue. This problem cannot be fully addressed unless autonomous ships are prevalent in the world [28][29][30].…”

Section: Discussionmentioning

confidence: 99%

Ports Opening for Seafarer Change during the COVID-19: Models and Applications

Guo

Yan

et al. 2022

Sustainability

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Countries around the world have tightened their border controls in response to the spread of COVID-19. Consequently, seafarers are prohibited from boarding or leaving ships at most ports, with few exceptions. This situation means that seafarers are highly likely to serve onboard vessels beyond their contracted shifts. Most seafarers are prone to depression because they have to spend long periods at sea away from family and friends, and, thus, banning crew changes will put their mental health further at risk. This will increase the likelihood of maritime accidents, thus jeopardizing global supply chains and ultimately exacerbating current hardships. To tackle this emergency, the International Maritime Organization and the European Commission have called on governments to coordinate efforts to designate ports for crew changes during the pandemic. This study aims to solve the crew change problem by using an integer linear programming model. In the sensitivity analysis section, we find that the number of opening ports for crew changes and the cost of crew changes decrease when the cost of opening ports increases. The results show governments will not invest in opening more ports when the cost of opening ports increases. However, the penalty increases when the number of open ports decreases, and the cost of opening ports increases. As the number of crew changes is decreasing, this leads to the penalty increasing, when the number of open ports decreases.

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Section: Discussionmentioning

confidence: 99%

Ports Opening for Seafarer Change during the COVID-19: Models and Applications

Guo

Yan

et al. 2022

Sustainability

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“…The researchers in [46] assessed the safety impacts of a variety of connected vehicle work zone advisory systems. They performed a comparative study using microsimulation experiments to demonstrate that CV-based warning systems are more effective than dynamic message signs (DMS).…”

Section: Related Workmentioning

confidence: 99%

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

Jawad,

Nitulescu

2024

Applied Sciences

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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.

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“…Bashir and Zlatkovic (2021) generate and share queue information based on the coordinates of vehicles and road infrastructure to avert traffic jams and streamline traffic. Incident/work zone alert Fatalities in work/incident zones account for a sizable proportion of traffic-related deaths. There were an estimated 809 traffic-related fatalities in the USA in 2017, a staggering 38% increase from 2010 (Mao et al , 2021). The Incident/Work Zone alert uses a warning system to inform drivers of ongoing zone operations while also alerting onsite workers about an approaching vehicle, thereby mitigating potential collisions between vehicles and VRUs. Qualcomm, in collaboration with the Virginia Department of Transportation, the Virginia Tech Transportation Institute and American Tower, has deployed intelligent vests based on C-V2X to alert roadside workers about approaching vehicles [9].…”

Section: Vehicle-to-everything Applicationsmentioning

confidence: 99%

“…Fatalities in work/incident zones account for a sizable proportion of traffic-related deaths. There were an estimated 809 traffic-related fatalities in the USA in 2017, a staggering 38% increase from 2010 (Mao et al , 2021). The Incident/Work Zone alert uses a warning system to inform drivers of ongoing zone operations while also alerting onsite workers about an approaching vehicle, thereby mitigating potential collisions between vehicles and VRUs.…”

Section: Vehicle-to-everything Applicationsmentioning

confidence: 99%

A systematic study on the challenges, characteristics and security issues in vehicular networks

Lone

Verma

Sharma

2023

IJPCC

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Purpose The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks. Design/methodology/approach This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication. Findings Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations. Originality/value The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

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Safety effects of work zone advisory systems under the intelligent connected vehicle environment: a microsimulation approach

Cited by 20 publications

References 23 publications

Ports Opening for Seafarer Change during the COVID-19: Models and Applications

Ports Opening for Seafarer Change during the COVID-19: Models and Applications

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

A systematic study on the challenges, characteristics and security issues in vehicular networks

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