A Data-Driven-Based Wavelet Support Vector Approach for Passenger Flow Forecasting of the Metropolitan Hub

Tang, Ming; Li, Zhiwu; Tian, Guangdong

doi:10.1109/access.2019.2890819

Cited by 26 publications

(14 citation statements)

References 36 publications

(24 reference statements)

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“…However, economic dependencies, structural dependencies, and stochastic dependencies among system components are often constraint and have joint effects. It will be interesting and necessary for us to study the joint effects of two or more different types of dependencies on the maintenance strategy structure in the future [30][31][32].…”

Section: Discussionmentioning

confidence: 99%

Economic Maintenance Planning of Complex Systems Based on Discrete Artificial Bee Colony Algorithm

et al. 2020

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In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dualobjective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

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Section: Discussionmentioning

confidence: 99%

Economic Maintenance Planning of Complex Systems Based on Discrete Artificial Bee Colony Algorithm

et al. 2020

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“…For instance, policies on green standard and licensing systems will significantly decrease the emissions of greenhouse gas [74]. Business firms can reduce carbon emissions by using green materials and green energy, and relieve traffic congestion by constructing logistics facilities and applying information technology to schedule the distribution plans and routing the green vehicles [75,76].…”

Section: B Managerial Insightsmentioning

confidence: 99%

Green Logistics Development Decision-Making: Factor Identification and Hierarchical Framework Construction

Zhang

Sun

et al. 2020

IEEE Access

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The influential factors of green logistics development have been an important research topic for academics. However, little attention has been devoted to studying the issue of the prioritization and the hierarchical structure of these factors. The purpose of this study is to identify the key factors influencing the development of green logistics, and to explore the prioritization and the hierarchical structure of the influential factors. An indicator system for influential factors of green logistics development is first developed based on the theory of triple bottom line, including sixteen indicators or factors. The comprehensive influences and the prioritization of these sixteen factors are then analyzed using the DEMATEL method and the multi-level hierarchical structure of these factors is constructed by applying the ISM method. The results indicate that government policies, public supports, laws and regulations, green education, green technology innovations, consumer green demands, quantity and quality of green logistics talents are more influential than the other factors. Social factors including laws and regulations and government policies, public supports and green education are the underlying factors affecting the development of green logistics. Economic factors are at the middle levels and environmental factors are at the surface-level of the hierarchical structure. Recommendations are finally given to governmental agencies and business firms. These findings will be beneficial to promoting the sustainable development of green logistics.

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“…The experimental operation environment is based on Matlab R2014A. The relevant parameters of the model are obtained through cross validation [26,27,33,35,36], the final selection is 600  p and 3  m . Three algorithms, namely RF-AID algorithm [16] (Algorithm 1), WRF-AID algorithm (Algorithm 2), and the FA-SVM AID algorithm [17] (Algorithm 3), are compared with the algorithm proposed in this paper (Algorithm 4).…”

Section: Detection Effect Analysis Of Aid Algorithm Based On Wrfmentioning

confidence: 99%

Traffic Incident Detection Method Based on Factor Analysis and Weighted Random Forest

Jiang

Deng

2020

IEEE Access

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Timely and accurate detection of traffic incidents can effectively reduce personal casualties and property losses, and improve the ability of macro-control and scientific decisionmaking of traffic. The unbalance of traffic incident data has a great influence on the detection effect. Therefore, a traffic incident detection method based on factor analysis and weighted random forest (FA-WRF) is designed. Through the analysis of the change rule of traffic flow parameters to build the initial incident variable. The factor analysis (FA) method is used to reduce the dimension of the initial incident variables. Using Bootstrap improved algorithm to predetermine the data extraction standard of the training set. The MCC coefficient value is calculated for the classification effect of the decision tree after training, and is assigned to each tree as a weight value, so as to ensure that the trees with better classification ability have more voting power in the voting process, thus improve the overall classification performance of the random forest (RF) algorithm for unbalanced data. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the classification rate and the area under the curve of the receiver operating characteristic (AUC). Based on the location detector data from expressway, the incident data in which accounts for 6.5%, showing a typical unbalance. The experimental results indicate that the model based on FA-WRF has the better classification effect. Meanwhile it is competitive in processing unbalanced data classification compared with Support Vector Machine.

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A Data-Driven-Based Wavelet Support Vector Approach for Passenger Flow Forecasting of the Metropolitan Hub

Cited by 26 publications

References 36 publications

Economic Maintenance Planning of Complex Systems Based on Discrete Artificial Bee Colony Algorithm

Economic Maintenance Planning of Complex Systems Based on Discrete Artificial Bee Colony Algorithm

Green Logistics Development Decision-Making: Factor Identification and Hierarchical Framework Construction

Traffic Incident Detection Method Based on Factor Analysis and Weighted Random Forest

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