Purpose: The purpose of this paper is to propose a mathematical model for the prediction of railway freight volume, and therefore provide railway freight resource allocation with an accurate direction. With an accurate railway freight volume prediction, railway freight enterprises can integrate the limited resources and organize transport more reasonably. Design/methodology/approach: In this paper, a PSO-GRNN model is proposed to predict the railway freight volume. In this model, GRNN is applied to carry out the nonlinear regression analysis and output the prediction value, PSO algorithm is applied to optimize the GRNN model by searching the best smoothing parameter. In order to improve the performance of PSO algorithm, time linear decreasing inertia weight algorithm and time varying acceleration coefficient algorithm are applied in the paper. Originality/value: A railway freight volume prediction index system containing seventeen indexes from five aspects is established in this paper. And PSO-GRNN model constructed in this paper are applied to predict the railway freight volume from 2007 to 2011. Finally, an empirical study is given to verify the feasibility and accuracy of the PSO-GRNN model by comparing with RBFNN model and BPNN model. The result shows that PSO-GRNN model has a good performance in reducing the prediction error, and can be applied in actual production easily.

http://www.jiem.org/index.php/jiem/article/download/1007/590

A PSO-GRNN model for railway freight volume prediction: empirical study from China

Chen, J.; Lu, L.; Lu, J., and Luo, Y., 2015. An early-warning system for shipping market crisis using climate index. The shipping market is always in volatility according to complex impact factors. This paper develops a new operational early warning system (EWS) model based on the economy cycle theory for predicting shipping market crises. To achieve this goal this paper analyses and extends the early warning system by constructing the “climate index”. Taking industry capacity and profit level into account, several indicators are selected for the synthesis of climate index. These indicators are divided into three categories of the leading, coincident and lagging with time difference correlation analysis and cluster analysis approach. Advantageously accessible to extensive resources, multiple datasets are available for the selected indicators. In terms of the economic cycle theory, diffusion and composite indices of global shipping prosperity are presented by using the factor analysis, predicts co...

An Early-Warning System for Shipping Market Crisis Using Climate Index

In order to improve the prediction accuracy of train passenger load factor of high-speed railway and meet the demand of different levels of passenger load factor prediction and analysis, the influence factor of the train passenger load factor is analyzed in depth. Taking into account the weather factor, train attribute, and passenger flow time sequence, this paper proposed a forecasting method of train passenger load factor of high-speed railway based on LightGBM algorithm of machine learning. Considering the difference of the influence factor of the passenger load factor of a single train and group trains, a single train passenger load factor prediction model based on the weather factor and passenger flow time sequence and a group of trains’ passenger load factor prediction model based on the weather factor, the train attribute, and passenger flow time sequence factor were constructed, respectively. Taking the train passenger load factor data of high-speed railway in a certain area as an example, the feasibility and effectiveness of the proposed method were verified and compared. It is verified that LightGBM algorithm of machine learning proposed in this paper has higher prediction accuracy than the traditional models, and its scientific and accurate prediction can provide an important reference for the calculation of passenger ticket revenue, operation benefit analysis, etc.

/pdf/prediction-and-analysis-of-train-passenger-load-factor-of-1h874b2h1x.pdf

Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm

In view of the subway passenger flow's random problem, nonlinear problem and so on, in order to predict the subway passenger volume more accurately, this paper designs a kind of parallel variable coefficient weighted combination prediction model based on Radial Basis Function Neural Network and Least Squares Support Vector machines. In this method, firstly, the original data is preprocessed. Then, this paper respectively sets up RBF Neural Network and LSSVM prediction model for training and calculating the weighting coefficient with the results of the training. Finally this paper separately does two kinds of models' prediction, and weights to get results. This article uses 2012 passenger flow data of Beijing DONGZHIMEN Station for experiments, which shows that the result of combination prediction model is more accurate than the result of single prediction model.

Research on subway passenger flow combination prediction model based on RBF neural networks and LSSVM

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system’s robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system’s potential for real-world application.

Endoscope Automation Framework with Hierarchical Control and Interactive Perception for Multi-Tool Tracking in Minimally Invasive Surgery

Aimed at the shortcomings of the prediction methods for the passenger traffic volume of railways, the improved BP neural network is adopted to predict the time serial of the railway passenger traffic volume. The improved BP neural network theory is analyzed.The railway passenger traffic volume from 1980 to 1998 is normalized.The time serial neural network prediction model of railway passenger traffic volume is set up.The network parameters is designed.The network learning and training simulation experiment is carried out.The prediction result of the improved and normal BP neural network is compared and analyzed. The prediction result of the improved BP neural network is proved to be more accurate and precise.

The Application of Improved BP Neural Network in the Prediction of Railway Passenger Volume Time Serial

Visual Servo Control of Endoscope-Holding Robot Based on Multi-Objective Optimization: System Modeling and Instrument Tracking

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The Application of Improved BP Neural Network in the Prediction of Railway Passenger Volume Time Serial

Visual Servo Control of Endoscope-Holding Robot Based on Multi-Objective Optimization: System Modeling and Instrument Tracking