Freight Volume Research Articles

A framework for analyzing service disruptions in last-mile and first-mile reverse logistics

The growth of e-commerce in recent years has altered the way in which retailers and customers interact with each other. The effect on freight distribution has been particularly noticeable: nowadays, customers may have their orders delivered at or close to the point of consumption and, thus, the volume of freight distributed by logistic operators has increased substantially. This has led to a growing number of carrier companies and an increasing demand for couriers, which can sometimes struggle to fulfill their assigned services. In this article, a study of the current state of last-mile and first-mile reverse logistics in Spain is presented based on the analysis of the operation of 20 carriers located in 15 different Spanish provinces. To this effect, a framework for the classification of service disruptions during last-mile delivery and first-mile collection routes is posited. Eight different potential disruption types are proposed, and methods for managers to identify and categorize service incompletions as such are provided. The results of the analysis highlight the importance of service and courier traceability to improve the handling of service disruptions and, overall, show a considerable improvement margin in the operational performance of carriers, for which several managerial implications are provided.

Road Freight Demand Forecasting Using National Accounts’ Data—The Case of Cereals

This paper investigates the potential of utilising historical agricultural production data for enhancing road freight transport forecasting, focusing on cereal production. This study applies a multiple linear regression analysis using national statistical accounts and secondary data. The data were sourced from Finland’s Statistics Agency and the Natural Resources Institute. The analysis identifies an observable correlation between agricultural production and road freight volumes, although this correlation is not statistically significant. The highest adjusted R² observed in the models was 0.62. The analysis reveals that previous years’ production data can help forecast future road freight volumes, with vehicle mileage estimable from recent production and stock levels. Additionally, annual percentage changes in the volume of transported cereals can be partially predicted by the changes in total available cereals and opening stocks from two years prior. This exploratory research highlights the untapped predictive potential of agricultural production variables in forecasting road freight demand, suggesting areas for further forecasting enhancement.

Gansu, Qinghai and Ningxia regions of the new western land and sea corridor Freight network optimization

The new western land and sea corridor makes use of a variety of transport modes to reach major ASEAN countries such as Singapore to the south, connects Southeast Asia and North America to the east, and connects Chongqing, Lanzhou and Xinjiang to the north with the China-European Union (CEU) liner train, which is a composite opening-up corridor in the western region for realising the regional linkage and international co-operation with ASEAN and other countries, and organically connecting with the "One Belt, One Road". In order to explore the regional freight network of Gan-Qing-Ning region of the new western land and sea corridor, we establish the model of node socio-economic attractiveness, topological charisma and comprehensive utility maximization, and analyze the index factors by using entropy weighting, Delphi and comprehensive evaluation methods through the Matlab Genetic Algorithm Toolbox to study the influence of node socio-economic attractiveness, topological charisma, node freight volume, logistics and transportation costs and construction costs on the utility of nodes of the new western land and sea corridor. The influence of the regional node utility of Gansu, Qinghai and Ningxia in the corridor. Analyze the problems and factors affecting the selection of nodes in the existing freight transport corridors in the three provinces. Completing the site selection of Gan-Qing-Ning regional node of the new western land and sea corridor and combining the import and export cargo volume of the three provinces to propose the construction of the South-Middle East three-lane freight corridor, and completing the optimization plan of the freight network. The results show that: scientific optimization and improvement of Gan-Qing-Ning regional freight network can promote the construction of freight network in the northwest region of the new western land and sea corridor, to meet the demand for freight transportation in the corridor, and to promote the development of the corridor industry and economic growth. Thus, it provides reference for the development of Gansu, Qinghai and Ningxia region and freight network optimization.

RELATIONSHIP BETWEEN LOGISTICS SECTOR AND ECONOMIC GROWTH IN ASEAN COUNTRIES

In the modern economics, any manufacturing companies have increasingly relied on professional logistics firms, leading to rapid growth in the logistics sector as a service industry and its significant contribution to economic development. With increasing globalization and competition, countries have focused on enhancing their logistics systems to ensure stable economic growth and strengthen global trade competitiveness. In the ASEAN region, logistics plays a critical role, facilitating cross-border trade and regional connectivity. This study aims to investigate the relationship between the logistics sector, specifically air freight transportation, and economic growth in ASEAN countries, given the sector's central role in supporting supply chain management and trade activities. The primary objective of this study is to determine whether there is a causal relationship between air freight transportation (AFT), a key logistics activity, and gross domestic product (GDP) growth in six ASEAN countries. The study seeks to provide insights into how investment in logistics infrastructure, particularly in air freight, may influence economic growth in the region. The study uses panel data for six ASEAN countries from 1996 to 2021. Data on GDP growth and air freight transportation were sourced from the World Bank’s World Development Indicators database. A series of statistical tests, including the Cross-Sectional Augmented Dickey Fuller (CADF) test, Pesaran’s slope homogeneity test, and the Bootstrap Granger causality test, were performed to examine cross-sectional dependence and the causal relationship between GDP growth and air freight transportation. Descriptive statistics revealed significant disparities in air freight transportation capacity among the countries studied, with Indonesia and Thailand showing substantial air freight volumes compared to the Philippines. However, the results of the panel causality tests, including the Dumitrescu and Hurlin panel causality test, indicate that no causal relationship exists between air freight transportation and GDP growth in the six ASEAN countries.

Study on the environmental benefits of truck parking space for trucks waiting to enter freight terminal - A case study of Gangchen Logistics Park

The gathering effect of trucks at freight terminals and the high emission characteristics of trucks themselves lead to long-term truck queues and serious environmental pollution problems at freight terminals. Repeated stopping and following trucks aggravate the environmental pollution in the terminal. This paper proposes a scheme to plan truck parking spaces during waiting for trucks to enter the freight terminal in order to reduce the repeated stopping of trucks and to quantify the environmental benefits of truck parking spaces in the process. The truck admission process was simulated using the Visual Simulator (VISSIM). The simulation output provided vehicle operating data that was converted into operating mode distribution data using the Python calculation module. Then, the model parameters, including the operating mode distribution, were entered into the Motor Vehicle Emission Simulator (MOVES) to calculate the simulation scenario. Gangchen Logistics Park, for example, statistics of the park's 10 months of freight data, the design of freight volume gradually increased by 11 groups of admission simulation experiments, the simulation learned that the park queuing significant, more than 90% probability of queuing, queuing up to a maximum of 203 vehicles, the average queue of 61 vehicles. Then according to the actual road conditions in the park, add a parking lot in the VISSIM simulation. Signal sensing is realized by calling the COM interface of VISSIM through Python to guide the vehicles to park in order and enter the park. Eleven sets of simulation control experiments after designing and planning parking spaces are designed to calculate the pollutant emissions for each simulation scenario separately. The analysis of the emission measurement results shows that the emissions of CO, HC, NOX, and PM10 can be reduced by 1.90%, 7.90%, 9.42%, and 10.55% at the average level of the park's cargo volume. At the park's maximum cargo volume, it is possible to reduce HC, NOX, and PM10 emissions by about one-third. Truck parking space in the truck waiting to drive into the freight terminal process has obvious environmental benefits, queuing significant freight terminals should be reasonably planned truck parking spaces to reduce the freight terminal exhaust emissions pollution.

Development of the logistics potential of railway transport

An analysis of freight transport statistics over the past 10 years shows sensitivity to political influences from almost all modes of transport to one degree or another, including rail. At the same time, the participation of rail transport in the total volume of freight traffic in recent years can be estimated at 17–18 %. In recent years, there has been a trend towards an increase in the railway transportation sector in the total volume of freight. In order to ensure the technological sovereignty of the Russian Federation, it is necessary to take a number of technical, organizational and managerial decisions to offset the negative consequences. The development of domestic logistics potential involves the development of international transport corridors that will connect the macroeconomic regions of the north, south, east and west. This will certainly have an impact on the global economy as a whole. Therefore, the main task is to develop effective interaction between participants in transport processes both on related modes of transport, as well as the largest representatives of the operator and logistics business, cargo-forming enterprises. In this regard, the current situation has been analyzed using a tool to identify competitive advantages in the domestic market and in conditions of global competition.

Research on interval prediction method of railway freight based on big data and TCN‐BiLSTM‐QR

AbstractWith the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

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

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

Regional Truck Travel Characteristics Analysis and Freight Volume Estimation: Support for the Sustainable Development of Freight

In the field of freight transport, the goal of sustainable development requires us to improve the efficiency of freight transport while reducing its negative impact on the environment, such as reducing carbon emissions and noise pollution. There is no doubt that changes in freight characteristics and volumes are compatible with the objectives of sustainable development. Thus, mining the travel distribution and freight volume of trucks has an important supporting role in the freight transport industry. In terms of truck travel, most of the traditional approaches are based on the subjective definition of parameters from the trajectory data to obtain trips for certain vehicle types. As for freight volume, it is mostly estimated through manual surveys, which are heavy and inaccurate. In this study, a data-driven approach is adopted to obtain trips from the trajectory data of heavy trucks. Combined with the traffic percentage of different vehicle types collected by highway traffic survey stations, the trips of heavy trucks are extended to all trucks. The inter-city and intra-city freight volumes are estimated based on the average truck loads collected at the motorway entrance. The results show a higher proportion of intra-city trips by trucks in port cities and a higher proportion of inter-city trips by trucks in inland cities. Truck loading and unloading times are focused in the early morning or at night, and freight demand in Shandong Province is more concentrated in the south. These results would provide strong support for optimizing freight structures, improving transportation efficiency, and reducing transportation costs.

Analysis on the spatial correlation network and driving factors of carbon emissions in China's logistics industry

Promoting the green development of the logistics industry has become a focal point of attention in China. This study combines an improved gravity model and social network analysis method, focusing on the nineteen provinces of the Yangtze Economic Belt and Yellow River Basin. It constructs a spatial correlation network of carbon emissions in the logistics industry from 2010 to 2021, exploring its formation mechanism and spatial evolution characteristics. The study utilizes a quadratic assignment procedure model to investigate internal driving factors. Building upon this, the study employs an improved STIRPAT model to predict the emission reduction path of the future logistics industry. Results are as follows: (1) Carbon consumption is most pronounced in Shandong, Jiangsu, and Shanghai. The carbon emission shows the characteristics of larger downstream; (2) The bridges of carbon emission-related networks gradually shift from Shandong to Shanxi, Anhui, and Sichuan. Carbon emissions in each sector and spatial spillover effects exhibit dynamic correlations and interactive influences; (3) Energy intensity, freight volume, and the spatial correlation network of the logistics industry are highly correlated; (4) The overall carbon emissions from the logistics industry show a decreasing trend in the future. Anhui and Shaanxi provinces will have high carbon emissions in 2035. The conclusions aim to provide policy suggestions for the region's low-carbon transformation.

Granting analysis of the credit financing of the platform-type highway transportation supply chain.

Against the background of digital development, this study's research object is the platform-based highway transportation supply chain. It also analyzes two modes of supply chain financial credit financing, namely, upstream, and downstream enterprises of the platform, and network freight platform as the main financing body. Notably, the financial provider sets up a transaction credit based on the principle of business truth, and closed-loop transactions, determine the upper limit of the credit line based on the principle of financing self-compensation, build the expected profit maximization model, and establish the optimal credit line. Combined with the Highway Freight Index and Logistics Prosperity Index, the dynamic early warning value is established for the financing mode, where the platform as the main financing body. Through numerical analysis, the credit line and expected profit increase with the transaction credit, expected freight volume, and credit interest rate under the two modes, and the increase deriving from the credit interest rate is more significant. Finally, this paper describes the two-dimensional credit matrix of the financing subject via transaction credit and credit interest rate, which provides an intuitive credit reference for financial institutions to conduct the credit financing of the platform-based highway transportation supply chain.

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.

Zero-Emission Heavy-Duty, Long-Haul Trucking: Obstacles and Opportunities for Logistics in North America

Background: Pressure is growing in North America for heavy-duty, long-haul trucking to reduce greenhouse gas (GHG) emissions, ultimately to zero. With freight volumes rising, improvement depends on zero-emissions technologies, e.g., battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs). However, emissions reductions are constrained by technological and commercial realities. BEVs and FCEVs are expensive. Further, BEVs depend on existing electricity grids and FCEVs rely on steam–methane reforming (SMR) or electrolysis using existing grids to produce hydrogen. Methods: This study assembles publicly available data from reputable sources to estimate breakeven vehicle purchase prices under various conditions to match conventional (diesel) truck prices. It also estimates GHG emissions reductions. Results: BEVs face numerous obstacles, including (1) limited range; (2) heavy batteries and reduced cargo capacity; (3) long recharging time; and (4) uncertain hours-of-service (HOS) implications. On the other hand, FCEVs face two primary obstacles: (1) cost and availability of hydrogen and (2) cost of fuel cells. Conclusions: In estimating emissions reductions and economic feasibility of BEVs and FCEVs versus diesel trucks, the primary contributions of this study involve its consideration of vehicle prices, carbon taxes, and electricity grid capacity constraints and demand fees. As electricity grids reduce their emissions intensity, grid congestion and capacity constraints, opportunities arise for BEVs. On the other hand, rising electricity demand fees benefit FCEVs, with SMR-produced hydrogen a logical starting point. Further, carbon taxation appears to be less important than other factors in the transition to zero-emission trucking.

A MINP model and hybrid heuristic algorithm for railway cold chain service network design problem: a case study of China

Railway Cold Chain Service Network Design (RCC-SND) aims to optimise resource allocation and utilisation to ensure the efficient transportation of perishable foods. In this study, we propose a Mixed Integer Nonlinear Programming (MINP) model to solve the hub selection, service frequency determination, wagon flow organisation and routing problems in RCC-SND. The application of the model is illustrated using the real network comprising 163 cities in China. The PSO-GA algorithm is proven effective in solving the problem. Furthermore, we introduce two future scenarios to assess the carbon reduction potential and economic costs of railway cold chain operations. There are some main findings: as the hub number increases from 30 to 40, the total cost decreases, as the hub continues to rise, this reduction is offset by the increased hub operational costs. Increasing hubs can enhance direct train frequency while causing railway capacity redundancy, this redundancy can be addressed by freight volume increase, as demonstrated in this study, 4.5 times increase in freight volume can boost train utilisation by up to 12.5%. By 2030, railway cold chain carbon emissions are projected to exceed 1.1 million tons, the adoption of hydrogen as alternative energy is an economically viable solution for emission reduction.

Exploring the impact of demand and supply-side interventions on energy decarbonization of freight transportation: a research based on G20 nations

Through quantitative modeling, the study established a dynamic supply and demand system from freight demand, renewable energy production, alternative new energy, renewable energy consumption and carbon dioxide emissions to assess the impact of demand-side and supply-side changes on energy decarbonization. The results indicate that adjusting the freight volumes of railway and aviation, renewable energy electricity supply, and the use of alternative new energy sources have varying degrees of impact on decarbonization in transportation. Through interventions on the demand side of freight volumes, CO2 emissions from transportation decrease to levels below those before the intervention-induced fluctuations, while consumption of renewable energy increases to levels above those before the adjustment.

Driving Economic Growth through Transportation Infrastructure: An In-Depth Spatial Econometric Analysis

This research investigates the crucial role of transportation infrastructure in influencing economic activity, thus employing advanced econometric methods including Moran’s I index, LM, Hausman, and LR tests to ensure analytical accuracy and select the appropriate spatial model. Our findings reveal that freight volumes across road, waterway, and civil aviation significantly enhance economic activity by bolstering domestic trade, industrial production, and supply chains. Conversely, the impact of passenger turnover is comparatively minor, although it still contributes to labor mobility and urban accessibility. This study highlights the need for strategic investment in transportation infrastructure and efficient public transport systems to foster economic growth and sustainable development. We recommend that policymakers focus on optimizing transportation networks and integrating intelligent transport technologies to boost economic competitiveness and societal well-being. This analysis not only sheds light on the direct economic impacts of transportation but also underscores the broader social implications, thus advocating for a holistic approach to transportation planning and policymaking.

ARIMA-based Freight Forecasting and Network Optimization in E-commerce Logistics

Considering the proliferation of e-commerce platforms exemplified by Taobao and JD, the paradigm of online shopping has evolved into an integral facet of contemporary societal existence. To enhance network transport performance, it is essential to predict freight volume, assess existing capacity, and establish new sites to alleviate network pressure. This study focuses on a logistics transportation network, utilizing daily freight turnover data from January 1, 2021, to December 31, 2022. The ARIMA time series method is employed to forecast freight volumes at different sites within the e-commerce logistics network. Taking three logistics site route combinations, namely DC14→DC10, DC20→DC35, and DC25→DC62, as examples, two differencing operations are applied to meet the stationarity requirement of the ARIMA model. Autocorrelation and partial autocorrelation coefficients are used for preliminary model order determination. By minimizing the Akaike Information Criterion (AIC) value, an ARIMA (5) model is established. Single and multiple-step predictions are conducted, resulting in forecast curves for future freight volumes. Subsequently, an evaluation of the transportation network is performed, considering the importance of nodes and routes. The entropy weight method is applied to determine the weights of evaluation indicators. Importance indices for nodes and routes are calculated, leading to a ranking. For the assessment and selection of new site capabilities, Fisher's discriminant function is employed to classify different sites. The valuation level for site selection is determined, providing a scientific basis for the systematic choice of new sites.

Study on the temporal and spatial relationship between public health events and the development of air transport scale: A case of the Southwest China.

The spread of the COVID-19 had profoundly affected the development of the air transportation. In order to determine the changes in air transportation volume associated with the development of the epidemic, this paper takes Southwest China as the study area. Monthly data and methods, such as the coefficient of variation, rank-size analysis and spatial matching index, were applied. The results found that: (1) during 2020-2022, there was a positive relationship between passenger volume and epidemic development, while freight volume increased for most airports in the first quarter of 2020-2022, particularly in the eastern region; (2) From the perspective of changes in air transportation volume under the development of the COVID-19, among various types of airports, the changes in transportation volume of main trunk airports were more significant than those of regional feeder airports in remote areas; (3) however, under the influence of the epidemic, main trunk airports still exhibited stronger attraction in passenger volume. That is to say, the passengers who chose to travel by air still tended to choose the main trunk airports and formed the agglomeration distribution pattern which around high-level airports in the provincial capital. Whereas the freight volume had a tendency of equalization among airports in Southwest China; (4) Over the course of time, the consistency of the spatial distribution of the number of cases and the passenger or freight volume in southwest China gradually increased. Among them, the spatial matching rate of the passenger volume and the number of COVID-19 cases was always higher than that of the cases and freight volume, which might indicate that there was a stronger correlation relationship. Therefore, it is proposed that the construction of multi-center airport system should be strengthened, the resilience of the route network for passenger transportation should be moderately enhanced, and the risk-resistant capacity of mainline airports and airports in tourist cities should be upgraded, so as to provide references for the orderly recovery of civil aviation and regional development.

The impact of the war on the development of freight transportation in Ukraine

The paper analyzes the relevance of the chosen research topic, in particular, the importance of the freight transportation sector for the development of the Ukrainian economy. The significant impact of a full-scale war on the dynamics of freight transportation and structural changes in this market, which began in 2022, is substantiated. The dynamics of prices for A95 gasoline and diesel fuel for 2022-2024, UAH, is analyzed and analyzed. The dynamics of prices for freight transportation for 2022-2023 is analyzed and substantiated, UAH. The structure of the Ukrainian freight transportation market by export and import of goods as of 2022 is analyzed and analyzed. It is determined that the war affected the rise in fuel prices, which led to higher transportation prices and fuel shortages. It is determined that the largest share in 2022 belonged to rail transport, namely: 50% were exports and 41% were imports. It is determined that most of the cargo was transported by road and rail during the study period. The dynamics of the volume of freight traffic in Ukraine by types of transport for 2021-2022, million tons, is analyzed and substantiated. The paper analyzes the introduction of electronic queues and electronic registration "e-queue". The main problems faced by freight transportation are identified. A way to optimize the costs of enterprises under martial law is proposed. The expediency of introducing digital technologies, which should primarily lead to time savings and faster delivery, is substantiated. The use of a logistics chain management system based on the Digital Twin technology is proposed, the use of which will positively affect the organization of the process of cargo transportation by road and minimize unnecessary costs. The process of implementing Digital Twin in the logistics processes of a carrier company is depicted schematically. Conclusions are drawn about the impact of the war on the development of freight transportation in Ukraine.

Exact and heuristic approaches for the Modal Shift Incentive Problem

It is widely acknowledged that freight transportation in general, and road freight transportation in particular, contributes significantly to greenhouse gas and pollutant emissions. In response, many countries have implemented incentive schemes to divert freight traffic from roads towards more environmentally friendly transportation modes (e.g., rail, maritime). However, incentive-based policies to promote modal shift are often sporadic and uncoordinated across non-road modes, thus preventing full leverage of dedicated budgets. In this study, we present a strategic problem encountered by national and supranational entities aiming to reduce emissions from the freight transportation sector, referred to as the Modal Shift Incentive Problem (MSIP). The problem involves designing incentive policies in the form of subsidies aimed at reducing the cost of eco-friendly transportation modes, with the aim of minimizing the volume of freight transported via less environmentally friendly modes. These incentive policies must satisfy various constraints, including a limited budget for incentives, equity requirements, and compliance with regulations pertaining to competitiveness and free-market dynamics. To address the MSIP, we propose an original mathematical formulation on the basis of a deterministic threshold modal choice model used to simulate the shipper behavior in selecting the transportation modes. Then, we develop a two-phase solution method, able to determine good solutions with a small computational burden based on the generation of a subset of promising incentive schemes. Finally, we solve real-world instances generated utilizing data derived from a comprehensive case study in Italy. The results on different instance types, in terms of number of shipments, budget, and costs, show the relationship between these factors, the complexity of the MSIP, and the modal shift.