Preference-based multi-attribute decision-making method with spherical-Z fuzzy sets for green product design

Huang, Zhongwei; Zhang, Honghao; Wang, Danqi; Yu, Hao; Wang, Lingyu; Dai, Yu; Peng, Yong

doi:10.1016/j.engappai.2023.106767

Cited by 15 publications

(7 citation statements)

References 80 publications

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“…At signalized intersections, left turns have the highest fuel consumption and CO2 emissions, while right turns and straight-through movements are similar in this regard. However, for NOx emissions, carID47 11 13…”

Section: Comparison Analysis Between Signalized and Unsignalized Inte...mentioning

confidence: 99%

“…In previous research, many scholars have focused on the energy consumption and emissions issues of motor vehicles at intersections. However, for the study of signalized intersection, most studies have been concentrated on topics such as eco-driving strategies at signalized intersections [11][12][13] and the optimization of traffic signal timing [14,15]. For instance, Ren et al [16] developed the CVIS intersection control model based on traffic efficiency and vehicle energy consumption.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

Li,

Gong,

Peng

et al. 2023

Energies

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With the development of the economy, urban road transportation has been continuously improved, and the number of motor vehicles has also increased significantly, leading to serious energy consumption issues. As critical nodes in the urban road transportation network, intersections have become a focal point of research on vehicle energy consumption. To investigate whether traffic signal lights affect fuel consumption and emissions, this study analyzed the operating characteristics, fuel consumption, and emissions of intersections with and without traffic signal lights using real-world vehicle data. The data from the INTERACTION dataset for both signalized intersection VA and unsignalized intersection MA are used in the study, with a time duration of 3200 s. The VT-micro energy consumption and emissions model was applied to calculate and comprehensively analyze the vehicle flow, fuel consumption, and emissions. Additionally, the study compared the fuel consumption and emissions for different driving scenarios, including straight through, left turn, right turn, and U-turn, within a single traffic signal cycle. The results revealed that at signalized intersections, the average fuel consumption per vehicle was 26.54 L/100 km, NOx emissions were 68.76 g/100 km, and CO2 emissions were 61.07 g/100 km. In contrast, at unsignalized intersections, the average fuel consumption per vehicle was 46.88 L/100 km, NOx emissions were 149.26 g/100 km, and CO2 emissions were 107.16 g/100 km. The study indicated that for traffic volumes between 50 and 103 vehicles per 100 s, signalized intersections demonstrated better fuel consumption and emission performance than unsignalized intersections. Signalized intersections could accommodate larger traffic volumes and provide enhanced traffic safety. In conclusion, the findings of this study are important for urban traffic planning and environmental policies. They provide a scientific basis for reducing fuel consumption and emissions and improving road traffic efficiency. Due to the advantages of signalized intersections in terms of energy consumption and emissions, future urban traffic planning should consider more signal light controls to achieve energy savings, emission reduction, and improved traffic operation efficiency.

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Section: Comparison Analysis Between Signalized and Unsignalized Inte...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

Li,

Gong,

Peng

et al. 2023

Energies

Self Cite

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“…Active safety methods are generally analyzed from the perspective of collision avoidance, while passive safety methods are analyzed from the perspective of reducing the injuries caused by collisions, which usually involve vehicle structure, the restraint system, material properties, etc. [ 13 , 14 ]. Li et al [ 15 ] proposed a data-driven design method to optimize the engine hood through a machine learning model in order to achieve the lightweighting of the engine hood and to improve pedestrian safety.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

Shi,

Zhang,

et al. 2024

Biomimetics

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With the development of intelligent vehicle technology, the probability of road traffic accidents occurring has been effectively reduced to a certain extent. However, there is still insufficient research on head injuries in human vehicle collisions, making it impossible to effectively predict pedestrian head injuries in accidents. To study the efficacy of a combined active and passive safety system on pedestrian head protection through the combined effect of the exterior airbag and the braking control systems of an intelligent vehicle, a “vehicle–pedestrian” interaction system is constructed in this study and is verified by real collision cases. On this basis, a combined active and passive system database is developed to analyze the cross-influence of the engine hood airbag and the vehicle braking curve parameters on pedestrian HIC (head injury criterion). Meanwhile, a hierarchy design strategy for a combined active and passive system is proposed, and a rapid prediction of HIC is achieved via the establishment of a fitting equation for each grading. The results show that the exterior airbag can effectively protect the pedestrian’s head, prevent the collision between the pedestrian’s head and the vehicle front structure, and reduce the HIC. The braking parameter H2 is significantly correlated with head injury, and when H2 is less than 1.8, the HIC value is less than 1000 in nearly 90% of cases. The hierarchy design strategy and HIC prediction method of the combined active and passive system proposed in this paper can provide a theoretical basis for rapid selection and parameter design.

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“…It is used in wide range of applications to appropriately select the most suitable alternatives among several available considering various simultaneous conflicting evaluation criteria and it is widely utilized in materials science [ [32] , [33] , [34] , [35] , [36] ]. Despite of the existence of various decision making tools including the hybrid multi-attribute decision-making ones like TOPSIS, Fuzzy-AHP, best worst method (BWM), and the regret theory based on distance from average solution (RT-EDAS) [ [37] , [38] , [39] , [40] ], the AHP was found in most cases better than other decision making tools due to its high reliability, simplicity, and its capability of dealing with both subjective and objective decisions with rigorous consistency index for the judgments.…”

Section: Introductionmentioning

confidence: 99%

Determining the appropriate natural fibers for intelligent green wearable devices made from biomaterials via multi-attribute decision making model

AL-Oqla,

Hayajneh,

El-Shekeil

et al. 2024

Heliyon

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Preference-based multi-attribute decision-making method with spherical-Z fuzzy sets for green product design

Cited by 15 publications

References 80 publications

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

A Hierarchical Prediction Method for Pedestrian Head Injury in Intelligent Vehicle with Combined Active and Passive Safety System

Determining the appropriate natural fibers for intelligent green wearable devices made from biomaterials via multi-attribute decision making model

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