Freight Volume Prediction Research Articles

Study on Freight Volume Prediction of Routes Based on Random Forest Model

Study on Freight Volume Prediction of Routes Based on Random Forest Model

Multi-Factor Highway Freight Volume Prediction Based on Backpropagation Neural Network

With the development of the times, the traditional single-factor time series prediction cannot meet the needs of actual prediction, and it is necessary to comprehensively consider the influence of various variables on prediction results. Therefore, we use MATLAB R2022a to predict the multi-factor highway freight volume. According to the relevant data of highway freight volume in Chinese history, the BP neural network prediction model of highway freight volume is established, and the model is coded and calculated in the MATLAB software environment. Through repeated training of the data, the predicted value is finally obtained. The results show that the prediction accuracy of the BP neural network model based on multi-factor prediction is very high. Through the example analysis of China’s highway freight volume, the original data are accurately fitted, and the validity of the highway freight volume prediction model based on BP neural network is proved. Through the prediction of freight volume, the investment in infrastructure construction is improved to promote the development of transportation industry and the progress of social economy.

A novel hybrid model for freight volume prediction based on the Baidu search index and emergency

A novel hybrid model for freight volume prediction based on the Baidu search index and emergency

Regional Expressway Freight Volume Prediction Algorithm Based on Meteorological Information

In the post-epidemic era, dynamic monitoring of expressway road freight volume is an important task. To accurately predict the daily freight volume of urban expressway, meteorological and other information are considered. Four commonly used algorithms, a random forest (RF), extreme gradient boosting (XGBoost), long short-term memory (LSTM) and K-nearest neighbour (KNN), are employed to predict freight volume based on expressway toll data sets, and a ridge regression method is used to fuse each algorithm. Nanjing and Suzhou in China are taken as a case study, using the meteorological data and freight volume data of the past week to predict the freight volume of the next day, next two days and three days. The performance of each algorithm is compared in terms of prediction accuracy and training time. The results show that in the forecast of freight volume in Nanjing, the overall prediction accuracies of the RF and XGBoost models are better; in the forecast of freight volume in Suzhou, the LSTM model has higher accuracy. The fusion forecasting method combines the advantages of each forecasting algorithm and presents the best results of forecasting the freight volumes in two cities.

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all < 2.5%), mean absolute error, and root mean square error for all types of traffic at all the analyzed airports. Fuzzy sets can handle uncertainty and imprecision in time series. Therefore, the prediction accuracy of the entire time series model is improved by taking advantage of SVR and fuzzy sets. By using the highly accurate FSVR model to predict the future growth of air freight volume, airport management could analyze their existing facilities and service capacity to identify operational bottlenecks and plan future development. The FSVR model is the most accurate forecasting model for air traffic forecasting.

Application of de-noising automatic coding method in freight volume prediction under intelligent logistics

Application of de-noising automatic coding method in freight volume prediction under intelligent logistics

Application of de-noising automatic coding method in freight volume prediction under intelligent logistics

Application of de-noising automatic coding method in freight volume prediction under intelligent logistics

Transportation route optimization based on heuristic algorithm

With the rapid development of Internet technology, many enterprises are committed to finding the best solution in transportation organization and solving the vehicle distribution routing problem. Firstly, this paper introduces the current situation of transportation organization of Sichuan Yida Feiniu Transportation Company, and analyzes the main problems of the company. Secondly, through the prediction of freight volume, prepare the truck vehicle operation plan and optimize the company’s transportation organization and production plan. Finally, the heuristic algorithm is used to establish a mixed integer programming mathematical model to optimize the pooled vehicle distribution path problem and the vehicle distribution path with time window. In terms of centralized vehicle distribution, combined with the actual situation of Sichuan Yida Feiniu Transportation Company, an example is analyzed, the shortest total path is obtained, and the goal of shortest vehicle travel distance is realized. Through the optimization of the company’s transportation organization, this paper is of great significance to improve the company’s transportation organization to a certain extent.

A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities

An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.

Forecasting the railway freight volume in China based on combined PSO-LSTM model

Considering the shortcomings of the current railway freight volume prediction model, this paper proposes a railway freight volume forecasting model based on PSO-LSTM. LSTM can learn long-span nonlinear time series effectively, and it is highly efficient in training forecasting of railway freight volume data. In order to improve the accuracy of parameter selection, PSO is used to optimize neural network model. The simulation results show that the PSO-LSTM model proposed has lower prediction error and higher prediction accuracy than the traditional LSTM model GA-LSTM model. This indicates that this model is an effective prediction model for railway freight volume.

Research on Railway Freight Volume Prediction Based on Neural Network

Railway freight volume is an important part of the total social freight volume and an important indicator of the national economy. Scientific prediction of railway freight volume can provide decision support for the formulation of China's railway policy and railway investment planning, and is of great significance for adjusting transportation structure and building an efficient transportation network. In order to improve the prediction accuracy, this paper constructs a combined prediction model based on GRA-GABP. The model uses grey correlation analysis to screen out the key influencing factors of railway freight volume, and optimizes the weight and threshold of BP neural network based on genetic algorithm to improve the prediction accuracy. This paper comprehensively considers the influencing factors of macroeconomics, market demand, logistics competition and railway supply. The historical data of railway freight transport from 1978 to 2018 is selected for case analysis. The results show that the prediction accuracy of the GRA-GA-BP based combination prediction model is significantly improved and can be used as an effective tool for railway freight volume forecasting.

Short-Term Shipping Freight Volume Prediction Based on Temporal-Spatial Features

Short-Term Shipping Freight Volume Prediction Based on Temporal-Spatial Features

Application of Fuzzy Linear Regression Model in Predicting the Freight Volume of City

Fuzzy mathematics models are widely used in the fields of computer science and other engineering. Fuzzy linear regression model as a novel regression model has effective results in research of data forecasting. In this paper, in order to analyze the freight volume prediction problem of city under the influence of various factors, triangular fuzzy linear regression model and trapezoidal fuzzy linear regression model are respectively set up to forecast the freight volume. These models combine the advantage of fuzzy mathematics and mathematical statistics methods, reflect the fuzzy relation between independent variables and dependent variables in implementing fuzzy coefficients, realize the dynamic handling freight volume, and facilitate to determine the change scope and trend of freight volume. Based on analysis of the relativity of various impact factors and fitting results of freight volume, the case example verifies that compared with other prediction methods, the average absolute relative error of fuzzy linear regression model is the least, and this model has a better applicability.

Railway Freight Volume Prediction Based on Support Vector Regression (SVR)

This paper mainly introduces the SVR method in the application of the railway freight volume forecasting. The paper analyzed the railway freight volume of eight major social and economic factors. Then, the paper selected learning samples and test method validation samples, through the normalized processing. It is concluded that the SVR to the effectiveness of the railway freight volume forecasting. <br />

A PSO-GRNN Model for Railway Freight Volume Prediction: Empirical Study from China

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.

Prediction Models for Railway Freight Volume Based on Artificial Neural Networks

The current China railway freight transport has always been faced with the situation of limited transport resources. Many relative studies have been done to solve the problem of resource shortage. And railway freight volume prediction is the basis of all these studies. With accurate volume prediction, railway freight transport administrations can precisely allocate the transport resources, such as wagons and locomotives. In order to overcome the limitations of traditional prediction methods, in this study, we design four artificial neural network models for prediction, including BP neural network model, linear neural network model, RBF neural network model and generalized regression neural network model. The results of simulation and comparison show that all these models can reach high prediction accuracy and generalized regression neural network has both higher prediction accuracy and better curve fitting capacity compared with other models.

Railway freight volume prediction based on grey neural network with improved particle swarm optimization

Concerning the shortcomings of the methods which forecast railway freight volume,the paper proposed Grey Neural Network(GNN) based on the Improved Particle Swarm Optimization algorithm(IPSO-GNN).To make up for the shortfall of the conventional GNN and guarantee the prediction accuracy,it optimized the GNN whitening parameters through the IPSO.And it computed the railway freight volume and the correlation degree of influential factors.It built a railway freight volume model based on IPSO-GNN with six relating factors.The simulation results show that the prediction method is effective and feasible.The prediction precision of the given model in the railway freight volume forecast is better than those of the conventional GNN prediction method and other prediction methods.