Freight Enterprises Research Articles

A bootstrap dynamic multivariate panel Granger causality analysis to examine the relationship between the COVID-19, Delta and Omicron pandemic era and the maritime shipping freight industry

This work applies a bootstrap multivariate panel Granger causality test to examine the causal relationship between the coronavirus disease 2019, Delta and Omicron pandemic eras and the maritime shipping freight industry across 15 Chinese marine transportation freight stocks through the data from Jannuary 23, 2020 to December 6, 2022. After accounting for both dependency and heterogeneity across these industries, we find support for six of the 15 maritime shipping freight enterprises which display a unidirectional Granger relationship between stock returns and pandemic-related shocks and five of them which demonstrate a bidirectional association. On the other hand, the stock values of three firms in this cohort Granger “lead” the epidemic, suggesting that the adjustment of expectations to external shocks and the efficient market hypothesis could elucidate apparent causal connections. The empirical findings of this work provide important policy implications for the maritime shipping freight industry.

Analysing factors influencing the adoption of blockchain technology in maritime freight enterprises

ABSTRACT Maritime freight companies are beneficial in carrying the majority of commodities globally. The large number of parties involved makes data exchange hard. Blockchain technology’s potential benefits are becoming more apparent to maritime freight firms. This report examines the important factors influencing blockchain technology adoption in the maritime freight sector. To calculate their weights, the study applies the Grey-DEMATEL based Analytic Network Process. According to the analysis, the most important determinants are government policies, firm size, and perceived cost of investment. These insights can assist government agencies, freight companies, and blockchain service providers in planning successful implementations and increasing competitiveness.

Loading and Unloading Points Identification Based on Freight Trajectory Big Data and Clustering Method

Based on the GPS trajectory data of a freight enterprise in Dalian, China, this paper studies the identification of loading and unloading points by a clustering algorithm. Firstly, by analysing the characteristics of freight loading and unloading behaviour, combined with the spatial and temporal distribution characteristics of truck GPS trajectory data, three characteristic variables of the number of trucks passing through a certain place, the average speed of trucks and the average stay time of trucks in the place are extracted. Then, the clustering algorithm and visual analysis are used to obtain the target cluster, and the POI language of the geographic information is obtained according to the points in the target cluster. The meaning information is crawled to accurately identify the result of the freight loading point. Finally, two classical clustering algorithms, K-means and GMM, are evaluated and compared. The results show that the identification method designed in this paper finally identifies 2,320 freight loading and unloading points from 11,406,000 trajectory data, which can realise the accurate extraction of freight loading and unloading points.

Impact of Emerging Transport Technologies on Freight Economic and Environmental Performance: A System Dynamics View.

Road freight transport promotes economic development while impeding the future of green development due to excessive fossil fuel use. Road freight enterprises need to adapt to stricter environmental regulations while maintaining a reasonable level of profit. However, this is not easy in a growing economy such as China's, whose domestic freight demand is increasing rapidly with economic growth. The development of emerging transport technologies (ETTs) creates great potential for reducing the negative environmental impact of road freight transport. This study considers five candidate ETTs: eco-driving, fleet platooning, vehicle utilization, optimized vehicle design, and renewable energy trucks. A system dynamics analytical framework is established to explore the long-term impact of ETTs on road profit and greenhouse gas (GHG) emissions under the uncertainty of macroeconomic development. Road freight enterprises affiliated with the Qingdao port in China are taken as a case study. The economic and environmental impact of their adoption of ETTs is projected from 2020 to 2035. The results show that the economic growth in the port hinterland leads to an increase in road freight volume and profit, but it also yields a greater amount of GHG emissions from road transport. All of the candidate ETTs exhibit a positive effect on reducing GHG emissions from road transport, but they also cause profit losses due to a high application cost, even though they reduce transport operating costs by fuel savings. The results of the Sobol sensitivity analysis show that GHG reductions are sensitive to the adoption of ETTs. Thus, a carbon-based compensation mechanism is introduced. With this mechanism, road freight enterprises should prioritize vehicle utilization, optimized vehicle design, and eco-driving in their adoption of ETTs for more sustainable development. The results provide systems-based insights into ETT deployment decisions for road freight companies.

Modeling the low-carbon behaviors’ development paths of freight enterprises based on a survey in Zhejiang, China

• Extreme value theory was used to model the low-carbon's development path. • Enterprises in low-GDP regions are more likely to adopt new low-carbon behaviors. • Enterprises in high-GDP regions may stop exploring new low-carbon behaviors. Improving freight enterprises’ low-carbon behaviors is a significant measure in reducing carbon emissions and thus promoting cities’ sustainable development. This paper aimed at modeling how various freight enterprises’ low-carbon behaviors vary with their development. A province-wide investigation of 980 freight enterprises was conducted, then the K-means clustering method and Extreme value theory (EVT) mode were adopted to identify the low-carbon development paths of freight enterprises. The results show that: a) freight enterprises with short-distance freight transportation in low-GDP areas have the lowest low-carbon degree (LCD), but they have the highest probability to adopt low-carbon behaviors when LCD>30.37; b) freight enterprises in high-GDP regions have the lowest willingness to adopt low-carbon behaviors when the development degree is higher than 201.5. The findings help to understand the low-carbon development path of freight enterprises at different stages, and it's useful for government to accurately adopt low-carbon development measures in road freight transportation.

Comprehensive Evaluation Model and Empirical Research on Railway Freight Rates in a Competitive Environment

This study aims to establish criteria to evaluate performance in railway freight pricing policy and to strengthen the marketing ability of the railway freight enterprise. The evaluation method used in our study is known as the improved grey relation analysis evaluation. Fifteen factors that influence freight pricing strategy are selected in this paper to establish a freight rate evaluation index system and comprehensive evaluation index values for monthly freight rates are calculated. The improved grey relation analysis not only considers the relation between each index and freight rate but also examines the relationship between each index. We find that the comprehensive evaluation state using the improved grey relation analysis evaluation method can better fit the 2015–2017 changes in monthly railroad freight revenue than the traditional grey relation analysis evaluation. Furthermore, an optimal pricing model for freight shipping by train is built and calculated using the bi-level programming model. Finally, the S Railway Bureau’s actual coal transportation in 2017 is taken as an experiment to support the validity of the improved grey relation analysis method. We find that when the monthly results of improved grey evaluation grades are compared with the grade evaluation of the gap between the historical shipping coal rates and the optimal freight shipping rates, the comparison of results matches well. The research work is helpful to policymakers who need to judge the performance of the market price.

Imbalance and breakout in the post-epidemic era: Research into the spatial patterns of freight demand network in six provinces of central China.

This study aims to explore the freight demand network spatial patterns in six provinces of central China from the perspective of the spread of the epidemic and the freight imbalance and breakout. To achieve this purpose, the big data of “cart search” demand information provided by small and medium freight enterprises on the freight information platform are analyzed. 343,690 pieces of freight demand big data on the freight information platform and Python, ArcGIS, UCINET, and Gephi software are used. The results show that: (1) The choke-point of unbalanced freight demand network is Wuhan, and the secondary choke-points are Hefei and Zhengzhou. (2) In southern China, a chain reaction circle of freight imbalance is formed with Wuhan, Hefei, and Nanchang as the centers. In northern China, a chain reaction circle of freight imbalance is formed with Zhengzhou and Taiyuan as the centers. (3) The freight demand of the six provinces in central China exhibits typical characteristics of long tail distribution with large span and unbalanced distribution. (4) The import and export of freight in different cities vary greatly, and the distribution is unbalanced. This study indicates the imbalance difference, chain reaction, keys and hidden troubles posed by the freight demand network. From the perspectives of freight transfer breakout, freight balance breakout, freight strength breakout, and breakout of freight periphery cities, we propose solutions to breakouts in the freight market in six provinces of central China in the post-epidemic era.

Research on the Carbon Footprint Drawing Method for Highway Freight Enterprises

In order to cope with global climate change and the increasing pressure of energy saving and emissions reduction, this study chooses highway freight transport which is the crucial part of carbon emissions in transportation section as the research object. After analyzing the resent researches and applications of carbon footprint drawing methods in domestic and foreign countries, this study draws the drawing boundary and builds the drawing method of carbon footprint for highway freight enterprises, according to the characteristics of them. Then this study analyses the effects of routing selection as a transportation organization strategy from the view of carbon footprint by using the actual operation data and energy consumption data from a highway freight enterprise.

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.

Research on Fuzzy Demand-Oriented Logistics Network Optimization Method for Railway Freight Enterprises

Research on Fuzzy Demand-Oriented Logistics Network Optimization Method for Railway Freight Enterprises

Application of Data Mining Technique in the Performance Analysis of Shipping and Freight Enterprise and the Construction of Stock Forecast Model

Taiwan’s economic development is closely related to Mainland China. The signing of Economic Cooperation Framework Agreement (ECFA) across the strait has great impact on Taiwan’s industry. This study is going to investigate the impact on the operation of shipping and freight related enterprises after the signing of ECFA, and the results are going to be used as reference by the government departments. First, the financial data of shipping and freight related enterprises before and after the signing of ECFA will be collected from InfoWinner Plus Database. Meanwhile, grey relational analysis and Data Envelope Analysis will be adopted to investigate whether there is significant difference between the business operation performances before and after the signing of ECFA; later on, decision tree analysis is used to investigate the major causes affecting the business operation performance; finally in this study, shipping enterprises with the best performances are selected and stock related information are collected too, then methods such as Particle Swarm Optimization optimized general regression neural network (PSO_GRNN), General Regression Neural Network (GRNN) and multiple regression are adopted respectively to set up stock forecast models to be used as reference by the public investors and the researchers. From the analysis result, it can be seen that after the signing of ECFA, the business operation performance of shipping and freight enterprises is significantly enhanced, and the forecast capability of Particle Swarm Optimization optimized general regression neural network (PSO_GRNN) model is the best.

Whoop-Up Trail: International Highway on the Great Plains

SYMBOL OF THE economic ties uniting the northern Great Plains into a region which defied an international boundary was the Whoop-Up trail.' Despite its rowdy name, this half-forgotten highway once served as an avenue of commerce and culture which opened into a vast interior market stretching northward from the Missouri River to the Bow River valley. From Fort Benton on the Missouri to Fort Macleod on the Oldman, the trail reached into the north, writing its history in furs, freight, and pioneer enterprise. Publicity-conscious highway commissions have dutifully marked other trails possessing less colorful histories and local historians have painstakingly recorded the stories of less important trails in the economic and political development of the North American West. The Whoop-Up trail, however, faded into obscurity as grass healed the deeply rutted wounds on the face of the plains and as time dimmed the memories of those who remembered.