An empirical analysis of driving factors of Xi’an hub economic development

In order to promote Xi’an’s economic development as an export-oriented hub, 7 indicators, including GDP, foreign trade volume, investment volume, passenger transport volume, freight volume, post and telecommunications business income, and the number of industrial enterprises above designated size, are selected to analyze the development of the hub economy through multiple linear regression analysis method. The results show that foreign trade, passenger traffic and post and telecommunications business revenues are significant to the economic development of Xi’an hub. However, the freight volume, investment and the number of enterprises above designated size have not passed the inspection. According to self-organization theory, the countermeasures for Xi’an to develop the hub economy are puts forward.

Improving the Resilience of Port–Hinterland Container Logistics Transportation Systems: A Bi-Level Programming Approach

Port–hinterland container logistics transportation systems (PHCLTSs) are significant to economic and social development. However, various kinds of unconventional emergency events (UEEs), such as natural or human-caused disasters, threaten PHCLTSs. This study aims to measure and improve the resilience of PHCLTSs. Bi-level programming models with two different lower level models are established to help PHCLTSs recover their capacity efficiently in the face of UEEs. In the upper level model, the government makes immediate recovery decisions about a damaged PHCLTS with the goal of improving the resilience of the PHCLTS. In the lower level models, truck carriers make decisions about transportation routes and freight volume in the recovered PHCLTS. They cooperate fully to pursue the maximization of total profit and are coordinated by a central authority, or they make their own decisions to pursue maximization of their own profit noncooperatively. An algorithm combining particle swarm optimization (PSO) and traditional optimization algorithms is proposed to solve the bi-level programming models. The numerical experimental results show the validity of the proposed models.

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.

Is the Spatial-Temporal Dependence Model Reliable for the Short-Term Freight Volume Forecast of Inland Ports? A Case Study of the Yangtze River, China

The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR-GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.

Research on Freight Development of Guangdong Province Based on Grey Theory Model

Logistics and economic development complement each other. The comprehensive competitiveness of Guangdong provincial economy ranks first in China. Under the influence of COVID-19, the freight development of Guangdong Province has been affected, but there is still lack of quantitative research. It is significant to explore the trend of economic development through the freight development of Guangdong Province. Based on the grey theory model, this paper uses six freight indexes to research freight development of Guangdong Province. Under the assumption that COVID-19 did not happen, we predicted the development value of freight index of Guangdong Province from January to December in 2020 and studied the influence based on the comparison between the predicted value and actual value. The empirical study shows three impact characteristics: stage characteristics, structural characteristics, and entity transmission characteristics. COVID-19 has a negative impact on the development of total freight volume, highway freight volume, waterway freight volume, and air freight volume in Guangdong Province. The influence values were −23.001%, −29.344%, −11.296%, and −3.838%. But, the freight volumes of railway and pipeline were positively affected by 14.343% and 13.057%, respectively, due to their continuity and substitution to other transportation modes. To further explore the abate measures of COVID-19 impact on Guangdong’s freight development, the grey correlation model is introduced to study the correlation factors of freight development of Guangdong Province. Through the research of the related factors, this paper puts forward some measures to promote the freight development of Guangdong Province in the postepidemic era.

Analysis of the Relationship Between Railway and Highway Transportation and China's Economic Development

China has a vast land area and frequent interconnections between various regions. China's transportation industry is faced with tremendous pressure. This article combines China’s railway and highway transportation conditions to predict China’s economic development, uses stepwise regression to screen explanatory variables, and finally determines railway passenger turnover, road freight volume and passenger car ownership as the explanatory variables, and GDP as the dependent variable, and also analyzes China’s economic development by establishing a multiple regression model.

Analysis of Factors Affecting Freight Demand Based on Input-Output Model

Freight demand is a highly variable process over economic and industrial structure, and accurate freight demand forecasting is the basis of transportation planning. In order to clarify the influencing factors of freight volume so as to analyze and predict the change trend of freight volume accurately, this paper analyzes the impact of changes in economic, industrial structure, and complete consumption coefficients on freight demand, through constructing an input-output model for transportation value analysis and forecasting freight volume by fitting data of transportation value and freight traffic. Studies have shown that the growth in economic aggregate is the main reason for the increase in the value of transportation, and the change in the complete consumption coefficient is the main reason for the increase in freight traffic.

ATTRACTION AREA DIVISION AND FREIGHT FLOW ORGANIZATION OPTIMIZATION OF INLAND RAILWAY CONTAINER TERMINAL

The attraction area division is the foundation of distribution and organization of freight flow among railway stations. The development of railway container terminal, large railway freight distribution center, is closely related to logistics planning and economy development of local city. In this study, we divide freight flow attraction area of inland railway container terminal by using gravity model, break-point model and weighted-Voronoi-diagram with SPSS and ArcGIS. And then under the target of minimal cost and time window limitations, we develop 0–1 integer programming model for freight flow organization optimization between inland terminal and its attraction area. Finally, this paper takes railway container terminal in Harbin as an example to test model feasibility under different speeds from different transportation modes. The results show that it is necessary to divide attraction area when choosing reasonable transportation mode from feeder nodes to railway container terminal. The improvement of feeder transportation speed is an effective method to improve freight volume, increase railway revenue and realize sustainable development of China Railway (CR) Express.

Forecast of Freight Volume in Xi’an Based on Gray GM (1, 1) Model and Markov Forecasting Model

Due to the continuous improvement of productivity, the transportation demand of freight volume is also increasing. It is difficult to organize freight transportation efficiently when the freight volume is quite large. Therefore, predicting the total amount of goods transported is essential in order to ensure efficient and orderly transportation. Aiming at optimizing the forecast of freight volume, this paper predicts the freight volume in Xi’an based on the Gray GM (1, 1) model and Markov forecasting model. Firstly, the Gray GM (1, 1) model is established based on related freight volume data of Xi’an from 2000 to 2008. Then, the corresponding time sequence and expression of restore value of Xi’an freight volume can be attained by determining parameters, so as to obtain the gray forecast values of Xi’an’s freight volume from 2009 to 2013. In combination with the Markov chain process, the random sequence state is divided into three categories. By determining the state transition probability matrix, the probability value of the sequence in each state and the predicted median value corresponding to each state can be obtained. Finally, the revised predicted values of the freight volume based on the Gray–Markov forecasting model in Xi’an from 2009 to 2013 are calculated. It is proved in theory and practice that the Gray–Markov forecasting model has high accuracy and can provide relevant policy bases for the traffic management department of Xi’an.