Modeling Metaverse Perceptions for Bolstering Traffic Safety using Novel TrSS-Based OWCM-RAM MCDM Techniques: Purposes and Strategies
Modeling Metaverse Perceptions for Bolstering Traffic Safety using Novel TrSS-Based OWCM-RAM MCDM Techniques: Purposes and Strategies

Author: Mona Mohamed

Publisher: Infinite Study

Published: 2024-01-01

Total Pages: 12

ISBN-13:

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The Metaverse has the potential to revolutionize various aspects of human life, including transportation systems. The integration of the Metaverse into intelligent transportation systems has the potential to significantly improve traffic safety in smart cities. By creating a virtual replica of the physical world, the Metaverse can provide a platform for testing new traffic management systems, road designs, and vehicle technologies in a controlled and safe environment before implementing them in the real world. One way to integrate the Metaverse into intelligent transportation systems (ITS) is by enhancing traffic safety. This can be achieved by developing an evaluation model that considers both safety and traffic efficiency. The proposed evaluation methodology encompasses three phases. Firstly, the obligations/criteria, and subsidiary obligations are modeled into nodes within levels based on Tree Soft Sets (TrSSs). Secondly, the Opinion Weight Criteria Method (OWCM) is utilized for generating the weights for obligations and subsidiary obligations. Finally, the Root Assessment Method (RAM) harnesses the generated weights for assessing and ranking alternative approaches to improving traffic safety in smart cities. The utilized techniques are working under the authority of neutrosophic theory to support these techniques in uncertain and ambiguous circumstances. Subsequently, the proposed methodology is tested in a case study that considers three alternative approaches to improving traffic safety in a smart city. The criteria for evaluation include safety and traffic aspects. The results of the case study indicate that the proposed evaluation model effectively ranks the alternative approaches based on their safety and traffic efficiency. This suggests that the Metaverse can be effectively integrated to enhance traffic safety and improve overall transportation efficiency. Overall, the results of the case study suggest that the proposed evaluation model effectively ranks the alternative approaches based on their safety and traffic efficiency. This indicates that the integration of the Metaverse can indeed enhance traffic safety and improve overall transportation efficiency in smart cities.

Social Internet of Things
Social Internet of Things

Author: Alessandro Soro

Publisher: Springer

Published: 2018-07-20

Total Pages: 221

ISBN-13: 3319946595

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The aim of this book is to stimulate research on the topic of the Social Internet of Things, and explore how Internet of Things architectures, tools, and services can be conceptualized and developed so as to reveal, amplify and inspire the capacities of people, including the socialization or collaborations that happen through or around smart objects and smart environments. From new ways of negotiating privacy, to the consequences of increased automation, the Internet of Things poses new challenges and opens up new questions that often go beyond the technology itself, and rather focus on how the technology will become embedded in our future communities, families, practices, and environment, and how these will change in turn.

Behavior Analysis and Modeling of Traffic Participants
Behavior Analysis and Modeling of Traffic Participants

Author: Xiaolin Song

Publisher: Springer Nature

Published: 2022-06-01

Total Pages: 160

ISBN-13: 3031015096

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A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.