Freight Transportation Network Research Articles

Impact of COVID-19 pandemic on greenhouse gas and criteria air pollutant emissions from the San Pedro Bay Ports and future policy implications

The Ports of Los Angeles and Long Beach, collectively known as the San Pedro Bay Ports, serve as vital gateways for freight movement in the United States. The COVID-19 pandemic and other influencing factors disrupted freight movement and led to unprecedented cargo surge, vessel congestion, and increased air pollution and greenhouse gas emissions from seaport and connected freight system operations beginning in June 2020. In this study, we conducted the first comprehensive monthly assessment of the excess particulate matter, oxides of nitrogen (NOx), and carbon dioxide (CO2) emissions due to the heightened congestion and freight transport activity from ocean-going vessels (OGVs), trucks, locomotives, and cargo handling equipment (CHE) supporting seaport operations. Excess emissions peaked in October 2021 at 23 tons of NOx per day and 2001 tons of CO2 per day. The strategic queuing system implemented in November 2021 significantly reduced the number of anchored and loitering OGVs and their emissions near the ports, even during continued high cargo throughput until Summer 2022. Looking forward, we analyzed projected emissions benefits of adopted California Air Resources Board regulations requiring cleaner and zero-emission trucks, locomotives, and CHE over the next decade. If a repeated port congestion event were to occur in 2035, NOx emissions from land-based freight transport should be lessened by more than 80%. Our study underscores the potential emissions impacts of disruptions to the freight transport network and the critical need to continue reducing its emissions in California and beyond.

A Proposed Simulation Model for Enhancing the efficiency of Freight Transportation Network in Egypt Based on Physical Internet Tools

A Proposed Simulation Model for Enhancing the efficiency of Freight Transportation Network in Egypt Based on Physical Internet Tools

A descriptive and prescriptive analysis of rail service subsidies in the China–Europe freight transportation market

This study delivers a descriptive and prescriptive analysis of rail service subsidies for China Railway Express (CRE) in the China-Europe freight transportation market. The analysis is conducted by advanced mathematical modeling and programming methods. Specifically, we implemented a multicommodity multimodal freight transportation network equilibrium model that can be used for predicting the commodity-specific mode-route cargo flow pattern and hence for assessing the effectiveness and limitations of the current CRE subsidy scheme. To properly quantify the impact of subsidies on individual shippers’ decision making, the model explicitly characterizes individual shippers’ mode-route choice behavior and takes into account shipping cost, transit time, capacity-induced congestion surcharge, and unobserved transportation impedances as shippers’ disutility. The solution of the network equilibrium model resorts to a disaggregate simplicial decomposition algorithm within the well-known Lagrangian relaxation framework. A bi-level network-based subsidy optimization model is constructed, in which the upper level aims at minimizing the sum of revenue loss and congestion charge, and the lower level is the aforementioned freight transportation network equilibrium model. A tabu search procedure is proposed and implemented to derive the solution of the bi-level model. The above models and algorithms are then applied to the China-Europe containerized freight transportation network, which comprises all China-Europe liner shipping lines, all CRE service lines, and the highway networks in China and Europe. The evaluation and optimization results show that the current subsidy scheme creates an imbalanced capacity utilization pattern across CRE service lines while an optimized line-specific subsidy solution can yield noteworthy improvements in the service utilization and economic efficiency of CRE.

Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks

AbstractMultimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi‐modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance for cargo transportation, as it enables SC participants to increase logistics efficiency and quality. It requires adequate input data, which can be generated. In recent times, the machine learning (ML) algorithm has been well‐suited to resolve complex and nonlinear relationships in the collected tracking data. This study designs a deep learning‐powered travel time estimation in multimodal freight transportation networks (DLTTE‐MFTN) technique. The goal of the DLTTE‐MFTN technique is to estimate the travel time using a hyperparameter‐tuned ensemble learning approach. To achieve this, the DLTTE‐MFTN method initially undergoes data pre‐processing to convert the input raw data into a useful format. In addition, the singular value decomposition (SVD) model can be applied for feature dimensionality reduction in multimodal transport data, considerably improving travel time prediction. Besides, the DLTTE‐MFTN method estimates travel time using an ensemble of three DL approaches including one‐dimensional convolutional neural network (1D‐CNN), stacked autoencoder (SAE) attention, and recurrent neural network (RNN). Finally, the hyperparameter tuning of the DL models takes place using the whale optimization algorithm (WOA). The performance analysis of the DLTTE‐MFTN method takes place using the Kaggle dataset. The experimental results stated that the DLTTE‐MFTN technique attains superior performance over other ML and DL models.

Vulnerability Assessment and Reduction for Intermodal Freight Transportation Networks

Intermodal freight transportation networks (IFTNs) are critical for global logistics, facilitating the efficient transportation of cargo within containers by integrating diverse transportation modes. However, such integration exposes IFTNs to a variety of disruptions, may leading to degradation in transportation capabilities. Thus, vulnerability assessment and reduction (VA&R) for IFTNs are of practical significance. Despite its importance, this research problem has received little attention in the literature. To fill the research gap, we propose the VA&R problem for IFTNs, considering uncertain disruptions that impact IFTN nodes and links, along with cascading failures and influence spreading. We further build models incorporating two-stage robust optimization to assess and reduce the impact of uncertain disruptions, and build exact solution methods for VA&R utilizing column-and-constraint generation and speedup strategies to identify non-conservative solutions. The effectiveness and efficiency of our modeling and solution methods are demonstrated by comparing them to benchmark modeling and solution methods, through numerical experiments for a real-world international IFTN. Furthermore, managerial insights and recommendations for IFTN operations are achieved based on comprehensive sensitivity analyses to address uncertain disruptions in IFTNs.

Hierarchical Probabilistic Decision-making for Intelligent Heavy Vehicles in Freight Transportation Networks

Heavy vehicles play an important role in the freight transportation networks, but they are often plagued by Traffic accidents due to their dynamic characteristics. Autonomous driving technology is considered promising to improve vehicle driving safety. This work proposes a hierarchical probabilistic lane-changing decision-making framework integrating decision-making and control modules, addressing the stability concerns of heavy vehicles. An improved risk assessment method is proposed, utilizing risk field intensity indicators that consider the characteristics of heavy vehicles. Experiments show that the proposed method can effectively improve driving safety and stability during lane changing.

A multi-objective optimization approach for disruption management in an intermodal freight transport network

The growing demand for freight transportation is leading to pay more attention to the management of intermodal freight transport network (IFTN). The optimization of IFTN management will have many benefits in terms of sustainability, reducing costs and pollution. Although many studies on the optimization of IFTN operations exist, the management of disruptions deserves a particular attention since disruptions may lead to a significant drop of performance in the entire network. In particular, seaport terminals are prone to disruptions because of their key role in connecting the land side and the sea-side operations. The availability and reliability of the entire network is directly affected by their disruptions. In this paper, a multi-objective linear programming model is developed to minimize congestion in an IFTN, which is originated from the occurrence of a disruption in a seaport terminal. The model aims to minimize cost and emissions, simultaneously. Computational results of a realistic case study, inspired by an Italian IFTN, shows the effectiveness of the developed model to improve the performance of the entire network.

Assignment of Freight Traffic in a Large-scale Intermodal Network under Uncertainty

This paper presents a methodology for freight traffic assignment in a large-scale road-rail intermodal network under uncertainty. Network uncertainties caused by natural disasters have dramatically increased in recent years. Several of these disasters (e.g., Hurricane Sandy, Mississippi River Flooding, and Hurricane Harvey) severely disrupted the U.S. freight transportation network, and consequently, the supply chain. To account for these network uncertainties, a stochastic freight traffic assignment model is formulated. An algorithmic framework, involving the sample average approximation and gradient projection algorithm, is proposed to solve this challenging problem. The developed methodology is tested on the U.S. intermodal network with freight flow data from the Freight Analysis Framework. The experiments consider three types of natural disasters that have different risks and impacts on transportation networks: earthquakes, hurricanes, and floods. It is found that for all disaster scenarios, freight ton-miles are higher compared to the base case without uncertainty. The increase in freight ton-miles is the highest under the flooding scenario; this is because there are more states in the flood-risk areas, and they are scattered throughout the U.S.

Opportunities for Multimodal Transport Development to Promote a Sustainable Environment

The rapidly growing global production and trade increase the demand for transport and logistics, leading to the establishment of increasingly more new companies providing these services. This also results in a much larger number of vehicles, which is not always a positive thing. Excessive vehicle traffic leads to an increase in environmental pollution, noise and traffic accidents, which have a highly negative impact on society and, more specifically, on human health. The negative impact on the environment has been increasing, and therefore representatives of national politics, the transport and logistics sectors should be alarmed and take action to reduce the environmental pollution. The development of the transport system should be carefully planned and geared towards achieving green and sustainable transport. In order to create a sustainable freight transport network, certain alternatives that could help to stop or at least reduce the process of environmental pollution should be pursued. One of such alternatives is the use of multimodal transport, which involves several different modes of transport for transporting one freight. This both distributes and reduces the burden on the currently dominant road transport mode, which contributes significantly to environmental pollution and is not sufficiently sustainable.

Artificial Neural Networks-Based Route Selection Model for Multimodal Freight Transport Network During Global Pandemic

The global pandemic caused major disruptions in all supply chains. Road transport has been particularly affected by the challenges posed by the COVID-19 pandemic. The selection of an efficient and effective route in multimodal freight transport networks is a crucial part of transport planning to combat the challenges and sustain supply chain continuity in the face of the global pandemic. This study introduces a novel optimal route selection model based on integrated fuzzy logic approach and artificial neural networks. The proposed model attempts to identify the optimal route from a range of feasible route options by measuring the performance of each route according to transport variables including, time, cost, and reliability. This model provides a systematic method for route selection, enabling transportation planners to make smart decisions. A case study is conducted to exhibit the proposed model's applicability to real pandemic conditions. According to the findings of the study, the proposed model can accurately and effectively identify the best route and provides transportation planners with a viable option to increase the efficiency of multimodal transport networks. In conclusion, by proposing an innovative and efficient strategy for route selection in complex transport systems, our research significantly advances the field of transportation management.

DIGITAL TWINNING AS A DECISION SUPPORT TOOL FOR RESILIENCE PLANNING OF COASTAL INTERMODAL TRANSPORTATION NETWORKS

Intermodal freight transportation networks (seaport-road-rail) constitute the economic backbone of coastal communities. From the current state of the literature, it is observed that despite their importance, intermodal transportation networks remain unserved with comprehensive tools that support high-level planning for resilience of existing and future infrastructure under hurricane scenarios. To address these gaps, this paper proposes the implementation of a decision support tool based on digital twinning, which is able to predict system performance in terms of resilience, given three main inputs: hazard intensities, budgetary constraints, and the characteristics and topology of the network.

Obtaining the optimal origin-destination multimodal freight transportation network for the City of Bogotá

Bogotá’s logistics performance as the principal hub for shipping cargo to other destinations in Colombia has not been optimal. Excessive truck traffic, low density of railways in service, and no accessible river routes seem to be major determinant factors. This article proposes a balanced linear transshipment (BLT) model to estimate the optimal origin-destination (OD) multimodal freight transportation network for the mentioned city to seven main destinations in the country. This research also provides an appropriate framework for assessing several hypothetical transportation scenarios. Thus, based on national transport statistics, this study evaluates three main criteria: freight transportation cost, number of nodes, and number of routes. The results of analysis suggest that if alternative modes of transportation are implemented in Bogotá, the three mentioned criteria will improve substantially. The analysis is limited by the size of the OD transportation network considered, the assumptions associated with the research methodology, and the scope and diversity of data sources used as reference. To the authors' knowledge, this study is the first of its kind to examine the optimal OD transportation network in Bogotá by using a BLT model.

Hub seaport multimodal freight transport network design: Perspective of regional integration development

To design the multimodal freight transport network of regional hub seaports, and promote the high-quality development of regional economic integration. Combined with the principal component analysis method, the comprehensive evaluation value of the inland cities is determined. Meanwhile, the gravity model is used to obtain the correlation degree between non-core cities and core cities, and the hinterlands of these core cities are divided. The entropy weight TOPSIS model is applied to select the inland hinterland transshipment cities. Taking the minimum total transport costs as the objective function, the regional hub seaport multimodal freight transport network optimization model with space-time constraints is established, and solved by MATLAB software. Taking the freight transport from Chengdu-Chongqing Economic Zone to Yangtze River Delta as an example, a multimodal freight transport network is constructed. It is calculated that Chengdu and Chongqing can be set as freight consolidation centers, and Changsha, Wuhan, Nanchang, Nanjing, Hangzhou, Hefei, Zhengzhou and Xi'an can be set as transshipment nodes. These non-core cities in the Chengdu-Chongqing Economic Zone can be divided into five groups to achieve effective improvement in transport efficiency through group combined transport. This study gives managerial implications for multimodal transport structure optimization and provides reference for the regional integration development between seaports and hinterlands.

Global Shrinkage of Space and the Hub-and-Spoke System in the Global Trade Network

We explore how the hub-and-spoke system in the international freight transportation network contributes to the global shrinkage of space. The friction in long-distance trade routes varies by the location of shippers and nodal characteristics of traversed ports, and is mitigated by the quality of scale economies driven by the hub-and-spoke distribution system along the trajectory of the logistic process. In order to confirm the shrinkage of space brought about by the hub-and-spoke shipping economies through transshipment routes via hub ports, we examine disaggregated cross-Atlantic cargo shipping trajectory data from Europe to the U.S. recorded both on landside and seaside with a discrete choice model. The results present that hub-and-spoke shipping economies arise with the scales of landside and maritime port operations and with shipping line diversity. Generally, they are found to offset distance friction that occurs along landside and maritime shipping voyages, but hub-and-spoke shipping economies arise differently depending on how hub-and-spoke configurations are set. They mainly stem from larger scale of ports’ operation and more diverse shipping lines serving the final port of export. The hub-and-spoke system is confirmed as a main driver of global shrinkage of space in terms of long-distance commercial activities.

Foliated Transport Networks in Intermodal Freight Transport

Foliated transport networks represent a concept that aims to improve the efficiency of the transport system by combining direct connections and hub and spoke design. In addition to combining these designs, a foliated transport network requires a high level of dynamic planning and control, as well as optimisation of goods and resources between the different network layers. It highlights the advantages of individual transport network designs and minimizes their weaknesses, ultimately leading to better performance of the foliated system than the individual options. The purpose of this paper is to describe the basic models of the freight transport network and the benefits of connecting these models. This survey paper aims to provide an overview of previous research in the field of foliated transport networks and to provide future research guidelines in this field that include the application of this model in intermodal transport.

The Integrated Strategic Planning of Multimodal Freight Transport Network Under Infrastructure Budget Limitation

The Integrated Strategic Planning of Multimodal Freight Transport Network Under Infrastructure Budget Limitation

An advanced intermodal service network model for a practical transition to synchromodal transport in the US Freight System: A case study

• Provide managerial insights for synchromodal freight transportation network designs in long haul transportation. • Present routing models based on MIP for a free mode choice network with different intermodal terminal schedules and delays. • Develop a genetic algorithm to solve the model for an advanced intermodal service network design. Free mode choice, termed “synchromodality,” is an extension of intermodal service network design and is still in the early stages of modeling development. European countries have already started moving toward realizing this innovative transportation system. However, advancement in global transport with longer distances is rare and needs more infrastructural preparation and studies to clarify the steps for such a transition. In this paper, an advanced intermodal service network model (AI-SNM) is proposed to support the development of synchromodal transportation systems. This mixed-integer programming (MIP) model finds the optimal path between O/D pairs while considering horizontal integration of variant transport modes in a supply chain network along with resource constraints and time windows. It minimizes the total transportation cost, transshipment cost, and tardiness with a penalty for delays at intermodal terminals and overdue costs at the destination that accounts for the opening and closing times of the terminals. In order to solve the model for large problem instances, an efficient multiobjective genetic algorithm using a novel coding approach is developed. The algorithm is tested on two US-based case studies, showing the capability of the model to provide cost- and time-saving advantages in long-haul freight. The results of this study can be applied to long-distance global transportation with similar geography and scale.

The Integrated Strategic Planning of Multimodal Freight Transport Network Under Infrastructure Budget Limitation

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Accessibility Measures in Modal Choice Models: A Proof of Concept Applied to Freight Transport

Modal choice models applied to interregional or international freight transportation network models are often based on rather coarse origin-destination matrices, containing annual transported tonnages between (sub)regions, for instance. Generally, only basic (sometimes constructed) independent variables (transportation costs or transit times) are used because other variables such as shipment sizes, service frequencies, etc. are not available. Using origin-destination matrices and an assignment model, it is also possible to compute spatial accessibility measures that can further be used as additional explanatory variables. Indeed, several published studies have identified network accessibility as an important element in the mode-choice decision. This paper also shows that the inclusion of an accessibility measure in the utility functions of a logit model substantially improves the performance of a transportation network model, both in the modal choice and the assignment levels of the classical four-step model. Consequently, the assignment of the estimated modal demands results in more accurate estimated traffic on the networks. The model presented in this paper is to be considered as a proof of concept because its workflow should further be streamlined to make it easily useable by modelers.

A Real-Time Decision Support Approach for Managing Disruptions in Line-Haul Freight Transport Networks

Unexpected disruptions in road freight transport caused by poor weather conditions, traffic accidents, etc., are quite frequent and have negative effects on the whole supply chain. Therefore, an intelligent disruption management system is necessary to revise the transport plan directly after disruptions have occurred. The literature presents several approaches for managing disruptions in road freight transport. However, most of them focus on urban freight distributions where disruptions are often handled by vehicle rerouting. The current work, in contrast, addresses disruption management in a line-haul freight transport network that connects urban distribution systems. We present a novel hybrid approach combining a simulation model, optimization algorithms, and a cost-effectiveness analysis. When a disruption occurs, the proposed approach can be used to analyze the impacts of the disruption, identify the affected trips, and revise their plan quickly in real time. Six re-planning strategies are proposed to handle the disruptions and are evaluated in terms of cost, reliability (expressed in time delays), and CO2 emissions. Cost-effectiveness analyses are conducted to rank the obtained solutions and identify the best strategy. Moreover, we suggest a decision support system architecture, based on the proposed approach, to enable disruption management in real-time settings. Real data is used to evaluate the proposed approach in different disruption scenarios. The results provide transport planners with useful insights into possible re-planning strategies and how to identify the best cost-effective strategy to minimize the disruptions’ effects and be more economically sustainable. This work also supports carriers in the transition towards intelligent disruption management.