Construction of prediction model of neural network railway bulk cargo floating price based on random forest regression algorithm

Guo, Jingwei; Wang, Jian; Li, Qinglin; Guo, Baohua

doi:10.1007/s00521-018-3903-5

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…ey used a complex network to evaluate the freight capacity of China's road and railway nodes and explored the differences in the transportation of bulk cargoes by road and railway [11]. Combine the bulk cargo price prediction model with neural network algorithms to improve railway freight volume prediction speed and accuracy [12]. ey also combined the TOPSIS method to analyze the efficiency of bulk cargo transport in the Brazilian railway system, proposed some methods for optimizing the rail transport of bulk cargoes, and improved rail freight efficiency [13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (10) is a transport route capacity constraint of freight enterprise n. Constraint (11) aims to determine the carbon emissions of freight enterprises. Constraint (12) ensures that the volume of freight sent by all shippers is the same as the consignee receives. Constraints ( 13)-( 15) indicate the capacity constraints for transport between points.…”

Section: Model Buildingmentioning

confidence: 99%

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Research on Green Transport Mode of Chinese Bulk Cargo Based on Fourth-Party Logistics

Wang

et al. 2021

Journal of Advanced Transportation

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Due to the problems such as the excessive proportion of road transport and extreme carbon emission situation of China’s transport structure adjustment, this paper combines the fourth-party logistics with the bulk cargo green transport. It is advancing the adjustment of China’s bulk cargo transport structure using fourth-party logistics. This paper improves the particle swarm optimization algorithm to compare the integrated cost and carbon emissions of different bulk fourth-party transport networks to verify the benefits of the fourth-party logistics on bulk cargo transport networks’ cost reduction and emission reduction. The results show that using the fourth-party logistics model to promote the transfer of cargoes from road to rail can reduce the integrated cost of the transport network, reduce carbon emissions, and achieve green transport.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Model Buildingmentioning

confidence: 99%

Research on Green Transport Mode of Chinese Bulk Cargo Based on Fourth-Party Logistics

Wang

et al. 2021

Journal of Advanced Transportation

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“…Freight has to be contracted, just like commodities. e only difference is that most commodities are real products, while freight is a service instead of a physical product [39]. So when freight is "bought," the service of products being transported is contracted.…”

Section: Model Descriptionmentioning

confidence: 99%

Stackelberg Game Model of Railway Freight Pricing Based on Option Theory

Guo

Xie

2020

Discrete Dynamics in Nature and Society

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In recent years, although rail transport has contributed significantly to the productivity of the Chinese economy, it has also been faced with the fierce competition and challenge from other modes of transportation, and therefore, freight-pricing issue has received more attention by researchers. In this paper, the rail freight option (RFO) based on option theory is proposed to study the optimal pricing decision of the railway transportation enterprise and contract customers’ optimal purchase decisions. To obtain an effective RFO contract, the railway freight contract transaction process is first analyzed. Then, the theoretical framework for the RFO contract trading is put forward in the railway freight market. Next, a two-stage Stackelberg game theoretic approach is presented based on the principle of utility maximization to achieve the optimal decision of RFO contract. Subsequently, the reverse reasoning method in dynamic programming is used to solve the optimal combination decision of the contract customer. Finally, the optimal pricing decision of RFO is discussed using Kuhn–Tucker conditions and Lagrangian function. The result shows that the railway transportation enterprise should pay more attention to the option strike price w1 in terms of maximizing system utility and achieving Pareto optimal.

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“…Based on BP neural network theory, Cui and Jing [20] uses engineering geological database as the research and development platform. Wang et al [21] combine the cargo floating price prediction model with the neural network algorithm (hereinafter referred to as NNA) to establish a prediction model. Zhang and Wang [22] propose a new efficiency prediction model which for the first time combines information granulation (IG) and support vector machine (SVM) with DEA model, to evaluate the future efficiency of decision making unit.…”

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confidence: 99%

Special issue on machine learning-based applications and techniques in cyber intelligence

Mei

Sugumaran

2019

Neural Comput & Applic

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The development of machine learning needs to rely on cyber intelligence technology, because it uses many theories and methods of cyber intelligence. Furthermore, it has achieved certain results. The value of machine learning as an infrastructure of cyber intelligence is about to usher in a long-term development opportunity. On the other hand, solving the problem of scalability and growth of cyber intelligence cannot be done without machine learning technology. The development of cyber intelligence also needs to rely on the environment and technology support of machine learning. It is necessary for cyber intelligence, just like human beings, to have a lot of knowledge and rich experience; behind these knowledge and experience is the need for a large number of data support. The development of cyber intelligence and machine learning will push each other to form an effective mutual promotion effect. Therefore, the development of cyber intelligence relies on machine learning [1-3], at the meanwhile; machine learning also opens a new chapter for cyber intelligence. Humans will develop intelligent technology under a new perspective of mechanism, thinking and environment of machine learning [4][5][6]. This special issue focuses on the theory, methods and creative solutions of cyber intelligence.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues.

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Construction of prediction model of neural network railway bulk cargo floating price based on random forest regression algorithm

Cited by 11 publications

References 19 publications

Research on Green Transport Mode of Chinese Bulk Cargo Based on Fourth-Party Logistics

Research on Green Transport Mode of Chinese Bulk Cargo Based on Fourth-Party Logistics

Stackelberg Game Model of Railway Freight Pricing Based on Option Theory

Special issue on machine learning-based applications and techniques in cyber intelligence

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