Optimizing signal timing of the arterial‐branch intersection: A fuzzy control and nonlinear programming approach

Lu, Zhaoyou; Ding, Naikan; Lu, Linsheng; Tian, Zhengkun

doi:10.1002/asjc.2734

Cited by 7 publications

(4 citation statements)

References 32 publications

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“…The major causes of socio-economic losses and human casualties include traffic accidents [1,2], forest fires [3,4], and natural and geological disasters [5,6]. Among them, the ecological damage caused by forest fires is huge, irreversible and long-lasting compared to the economic damage caused to human society, even endangering the fragile global ecosystem.…”

Section: Introductionmentioning

confidence: 99%

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Wang

Peng

2022

Forests

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In recent years, frequent forest fires have plagued countries all over the world, causing serious economic damage and human casualties. Faster and more accurate detection of forest fires and timely interventions have become a research priority. With the advancement in deep learning, fully convolutional network architectures have achieved excellent results in the field of image segmentation. More researchers adopt these models to segment flames for fire monitoring, but most of the works are aimed at fires in buildings and industrial scenarios. However, there are few studies on the application of various fully convolutional models to forest fire scenarios, and comparative experiments are inadequate. In view of the above problems, on the basis of constructing the dataset with remote-sensing images of forest fires captured by unmanned aerial vehicles (UAVs) and the targeted optimization of the data enhancement process, four classical semantic segmentation models and two backbone networks are selected for modeling and testing analysis. By comparing inference results and the evaluation indicators of models such as mPA and mIoU, we can find out the models that are more suitable for forest fire segmentation scenarios. The results show that the U-Net model with Resnet50 as a backbone network has the highest segmentation accuracy of forest fires with the best comprehensive performance, and is more suitable for scenarios with high-accuracy requirements; the DeepLabV3+ model with Resnet50 is slightly less accurate than U-Net, but it can still ensure a satisfying segmentation performance with a faster running speed, which is suitable for scenarios with high real-time requirements. In contrast, FCN and PSPNet have poorer segmentation performance and, hence, are not suitable for forest fire detection scenarios.

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Section: Introductionmentioning

confidence: 99%

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Wang

Peng

2022

Forests

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“…Second, the speed of the vehicle passing through the intersection is changeable. Generally, the vehicle speed is higher when the vehicle is far from the intersection and lower when the vehicle is close to the intersection (22). Therefore, it is necessary to ensure that the illuminance of street lights does not change significantly when the vehicle speed changes, to avoid potential safety hazards caused by the driver adjusting to the changes in illuminance.…”

Section: Safety Design Principles Of Lighting Scheme At Intersectionsmentioning

confidence: 99%

Dynamic Illumination Method for Rural Highway Intersections with Traffic Flow Changes

Li,

Wang,

Bie

2023

Transportation Research Record: Journal of the Transportation R

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The conflicts between vehicles and vulnerable road users are frequent at rural highway intersections. Improving lighting conditions at intersections can significantly reduce the likelihood of nighttime accidents. This study aims to develop a dynamic illumination method for rural highway intersections with traffic flow changes. First, the safety design principles of a lighting scheme were formulated for minimum illuminance, vehicle speed, and traffic risk at intersections. In addition, a traffic risk quantification model for intersections was developed with illuminance and traffic flow parameters as inputs, incorporating gap acceptance theory and the braking safety distance model. Second, combining safety design principles and a traffic risk quantification model, a calculation method for optimal illuminance at intersections was designed with minimum illuminance as the optimization objective, and considering the risk at intersections not exceeding the safety threshold as the constraint. Finally, a case study was conducted in a simulated environment. Evaluation results demonstrate that limiting the maximum nighttime vehicle speed at intersections to 50 km/h can maximize the energy-saving rate of the lighting system while ensuring traffic safety and transport efficiency. The advantage of the method is that it can calculate the safe lighting scheme for highway intersections without any historic accident data and save lighting energy consumption. It increases the feasibility of promoting lighting facilities at rural highway intersections.

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“…Platoon control, a key technology in CAVs, aims to enable multiple vehicles to form a closely knit convoy and maintain short distances while driving on highways, and improve road utilization and fuel efficiency, leading to economic and environmental advantages [7]. Platoon control involves coordinating and controlling the speed, distance, and behavior of vehicles in the convoy, allowing them to drive in a highly synchronized manner [8]. The technologies involved in platoon control include vehicle-tovehicle communication (V2V) [9], vehicle-to-infrastructure communication (V2I) [10], path planning [11], formation maintenance and adjustment [12], etc.…”

Section: Introductionmentioning

confidence: 99%

The Sustainability and Energy Efficiency of Connected and Automated Vehicles from the Perspective of Public Acceptance towards Platoon Control

Li,

et al. 2024

Sustainability

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This study examines the public acceptance of platoon control for connected and automated vehicles (CAVs) and analyzes it from a sustainability perspective. A questionnaire survey was conducted targeting a diverse social group to collect data on public attitudes, acceptability, and factors related to the environment, social responsibility, personal economy, and behavior. Factor analysis was then performed to reduce these data into three factors: “attitudes and acceptance”, “environment and social responsibility”, and “personal economy and behavior”. Further, multiple regression analysis was employed to investigate the relationship between these three factors and the willingness to accept platooning control, as well as the willingness to actively adopt it. The findings indicate that the ”attitude and acceptance” factor significantly influences the public’s acceptance of platoon control. Moreover, the ”environment and social responsibility” and ”personal economy and behavior” factors also have a certain influence on decision-making. This study not only contributes to understanding public attitudes towards CAVs’ platoon control but also explores how to promote the development of sustainable intelligent transportation systems. By gaining a better understanding of public attitudes, policymakers and relevant stakeholders can take measures to enhance the acceptance of CAVs, thereby driving the sustainable development of transportation systems.

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Optimizing signal timing of the arterial‐branch intersection: A fuzzy control and nonlinear programming approach

Cited by 7 publications

References 32 publications

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Comparative Research on Forest Fire Image Segmentation Algorithms Based on Fully Convolutional Neural Networks

Dynamic Illumination Method for Rural Highway Intersections with Traffic Flow Changes

The Sustainability and Energy Efficiency of Connected and Automated Vehicles from the Perspective of Public Acceptance towards Platoon Control

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