Transportation Network Design Research Articles

F-Deepwalk: A Community Detection Model for Transport Networks.

The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in which the sampling is modified with the generalized travel cost and improved logit model. Urban spatial community detection is realized with the K-means algorithm, building the F-Deepwalk model. Using the basic road network as an example, the experimental results show that the Deepwalk model, which considers the generalized travel cost of residents, has a higher profile coefficient, and the performance of the model improves with the reduction of random walk length. At the same time, taking the Shijiazhuang urban rail transit network as an example, the accuracy of the model is further verified.

Modulation of electron transfer behavior on Fe2P-Co2P/NPC oxygen electrocatalyst by lattice cation substitution engineering and charge transport network design for rechargeable Zn-air batteries

Crystal structure modulation engineering on the lattice scale and charge transport network design on the micro/nano scale work together to endow oxygen electrocatalysts with high reactivity to fully release the capacity of Zn-air batteries (ZABs) as the next-generation green energy storage devices. Metal phosphide-based oxygen reduction reaction (ORR)/oxygen evolution reaction (OER) electrocatalysts with tunable electronic structures, variable valence states, and moderate overpotentials are recognized as the most promising and ideal candidates for air cathodes. The cation substitution strategy overcomes the drawback that single metal phosphides with fewer active sites and high adsorption barriers are not sufficient to achieve satisfactory electrocatalytic performance. In this paper, the Fe2P-Co2P/NPC oxygen electrocatalyst employing lattice cation site substitution engineering and three-dimensional (3D) conductive network encapsulation strategies are constructed to successfully ensure fast electron transfer behavior and safe charge/discharge energy storage. Noteworthy, the presence of Schottky barriers at the interface of the two materials hinders the electron reflux, which promotes a unidirectional flow of electrons, and increases the electron condensation in the OER and ORR processes. Relying on these features, the Fe2P-Co2P/NPC-assembled liquid ZABs achieved power densities, specific capacities, and energy densities of 175.18 mW cm-2, 805.75 mAh gZn-1, and 958.84 Wh kg-1, respectively, and importantly good rate performances and stability were exhibited even under high-current-density operation. The corresponding flexible solid-state ZABs also possesses superior electrochemical activity and mechanical flexibility. This work paves the way for the construction of bifunctional oxygen electrocatalysts with high-speed electron transfer channels, thus advancing the development of wearable electronic devices.

Approximate dynamic programming for liner shipping network design

AbstractThe global containerised trade heavily relies on liner shipping services, facilitating the worldwide movement of large cargo volumes along fixed routes and schedules. The profitability of shipping companies hinges on how efficiently they design their shipping network; a complex optimization problem known as the liner shipping network design problem (LSNDP). In recent years, approximate dynamic programming (ADP), also known as reinforcement learning, has emerged as a promising approach for large-scale optimisation. This paper introduces a novel Markov decision process for the LSNDP and investigates the potential of ADP. We show that ADP methods based on value iteration produce optimal solutions to small instances, but their scalability is hindered by high memory demands. An ADP method based on a deep neural network requires less memory and successfully obtains feasible solutions. The quality of solutions, however, declines for larger instances, possibly due to the discrete nature of high-dimensional state and action spaces.

Germany's Way from Russian Natural Gas to Green Hydrogen: Network Data, Procedures, and Topologies for Feasible Transition Paths to a Green Hydrogen Transport Infrastructure in Germany, GreenHyDE [gri:n'haɪ̯də] –

Hydrogen—and thus hydrogen transport—will play an important role in the German and European transition to climate neutrality. A German hydrogen transport network is currently being planned, but its appropriate structure and size highly depend on the spatial distribution of hydrogen demand and supply and their development over time. Moreover, the security of supply with both, hydrogen and natural gas, needs to be guaranteed during the transition period, as repurposing of the existing natural gas transmission system is a highly advantageous option for the building of hydrogen networks. Therefore, the integrated analysis of natural gas and hydrogen networks is a necessary and urgent, though complex, field of research. For this purpose, the article presents the database for the existing German natural gas transmission system (which in parts will be repurposed for hydrogen transport) and procedures for the development and evaluation of network topologies for both gases. The results for the German case show feasible development steps for the design of green hydrogen transport networks and the transition from natural gas in different scenarios. Open access to data and tools for technoeconomic assessment is provided.

Optimizing sustainable multimodal distribution networks in the context of carbon pricing, with a case study in the Thai sugar industry

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs.Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports.

Eco-friendly lane reservation-based autonomous truck transportation network design

As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis.

ПРОЄКТУВАННЯ БАГАТОПРОДУКТОВИХ ТРАНСПОРТНИХ МЕРЕЖ З ДИСКРЕТНИМИ ПОТОКАМИ МАГІСТРАЛЬНИХ ДАННИХ

The article considers the importance of transport and communication as key sectors for the development of the economy of Ukraine, as well as the need to create applied mathematical software for the optimization of transport processes and the development of information and analytical systems. Special attention is paid to the development and study of multi-product communication networks with discrete flows of small consignments, which have a significant impact on market transformations in Ukraine, especially in the field of transportation of small consignments. The study focuses on modern and future communication networks, which, as a rule, have a multi-level structure determined by the administrative division of the territory, management hierarchy and technologies for the processing and distribution of goods and information. The authors of the article emphasize the need to design multi-level communication networks, which would take into account the complexity of the system, as well as the impossibility of complete centralization of information processing and management in one link. The article also focuses on the analysis of existing research and publications in this field, paying attention to the contribution of foreign and domestic scientists to the development of models and methods of network resource management. Most research focuses on deterministic models and models based on the mathematical foundations of mass service theories and random Markov processes. The authors indicate the inadequacy of the existing mathematical tools for modeling and analyzing the functioning of multi-product networks with discrete flows, which requires the development of new mathematical models and methods.The purpose of the research is the development and analysis of effective approaches to the design of multi-tracks pipeline transport networks with a special focus on discrete trunk data flows. The authors note that this includes the development of new approaches and methods for effective management and optimization of resource allocation, as well as modeling the specifics of discrete flows in the context of trunk transport systems. An important part of the research is the creation of a hierarchical system structure for automated resource management and flow distribution in large-scale networks. The authors emphasize the need to develop a methodology that integrates already existing achievements in this field and allows to effectively solve practical problems related to long-term development, ongoing planning and operational management in large-scale networks. Such a methodology should include models of different levels of aggregation and be suitable for all levels of the network’s hierarchical structure. The study also includes consideration of mathematical models and algorithms for modeling and analyzing the functioning of multi-product networks with discrete flows.

Liner shipping network design problem in modern transportation system of Russia

The liner shipping network design problem is considered in the paper. This problem is particularly relevant in the current global political and economic conditions, where finding optimal solutions for business becomes crucial for survival. State-of-the-art formulations and methods for solving the problem in domestic and international scientific papers are discussed. Four types of liner services such as simple service, “butterfly”, “pendulum” and complex service are described. A simple service that can be reduced to the traveling salesman problem is primarily focused on. Our own formulation of the problem is proposed and an example of liner shipping network description, based on the proposed formulation, is provided. In the example, presented in the paper, a container line, based on a single domestic seaport (Vladivostok) and four seaports in China, is demonstrated. It should be noticed, that the problem includes several optimization sub-problems (such as multi-commodity flow problem), solving which is important for finding best solutions for the designed shipping line, but is outside the bounds of this research. The liner services as a sequence of vessel calls to ports are described; and some results of the foregoing generation process are discussed. The optimal solution is selected from the set of generated solutions. It represents a particular case that simultaneously has the lowest costs for implementing the container line and the highest number of serviced container flows. It is noted that this solution is optimal only within the generated solution space and may not be optimal for the considered example of the problem.

Design of mixed fixed-flexible bus public transport networks by tracking the paths of on-demand vehicles

On-demand ridepooling (ODRP) vehicles follow routes that are fully flexible. However, when the system does not provide door-to-door service and users can be asked to walk, their paths tend to concentrate, particularly along main streets that connect highly demanded areas of the city. These frequently travelled segments are hence useful to multiple passengers, which can be used as an indicator that it would be efficient to allocate a fixed public transport line there.In this paper, we formalise this idea and propose a novel method to design a public transport network, where bus fixed lines are combined with ODRP. Given a network and a transport demand, we first simulate how to serve it using only ODRP with walking. For this, we employ a state-of-the-art assignment algorithm, and take as output the resulting users’ paths. These paths are then processed by a tailored algorithm to create fixed lines where the paths accumulate the most. Users who do not have an available fixed line (i.e., those whose paths were barely shared) are served by ODRP in the mixed system. Simulations using real-life data from Utrecht, The Netherlands, and the Sunshine Coast, Australia, reveal the merits of our method compared to several benchmarks. Crucially, our method builds a small number of fixed lines while still serving the majority of the demand through them.This study contributes not only to the design of public transport networks, but also to the understanding of the patterns that naturally appear in intrinsically flexible mobility systems.

A Three-Stage Stochastic Model to Improve Resilience with Lateral Transshipment in Multi-Period Emergency Logistics

Driven by the growing threat of natural disasters caused by climate change, there is an urgent need to strengthen the emergency rescue logistics network. However, insufficient research has been conducted on optimizing both pre-disaster preparation and post-disaster response, resulting in lower resilience and inefficiency of emergency logistics management. To this end, this study explores the optimization of emergency rescue resource allocation and transportation network design, considering the uncertainty and multi-period nature of natural disaster rescue. By employing a lateral transshipment strategy, a three-stage stochastic programming model is established, which aims to balance economic benefits with the need for devastations, thereby enhancing the resilience of the logistics network. Numerical experiments verify the effectiveness of the proposed model with different instances and the performance of the lateral transshipment strategy by comparing it with a two-stage stochastic programming model. Sensitivity analysis is performed on the costs of constructing a depot and the penalties for unmet needs. The analysis yielded valuable insights that can be used to enhance emergency rescue operations, supply chain network design, and logistics network design. The research outcome can benefit emergency responders and logistics professionals in optimizing their operations.

A Coupling Model for Measuring the Substitution of Subways for Buses during Snowstorms: A Case Study of Shenyang, China

The development of integrated public transportation networks has received widespread attention in recent years. Especially in global northern cities, improving the substitution of subways for buses could meet population travel demand during snowstorms, which minimizes the impact of snowstorms on the public transportation network. Furthermore, the development of rail transit is conducive to the intensive and efficient use of land resources. Therefore, in this study, we selected a northern Chinese city, Shenyang, as a case study. For obtaining the population travel demand, we collected the actual population flow data in the morning and evening peaks during snowstorms. The network analysis was used to identify the loopholes and key stations in the subway and bus networks, respectively. A coupling model was built to measure the coupling value of each station in the subway and bus networks, according to its population travel demand and supply capacity, which was further used to measure the substitution of subways for buses in the morning and evening peaks during snowstorms. The results indicate that some subway stations were in a coupling state, while their surrounding bus stations were in a decoupling state. These subway stations could replace the bus stations to reduce the impact and damage of snowstorms on public transportation network. However, some subway stations and the surrounding bus stations were all in a decoupling state, which were under great pressure to meet the population commuting demand during snowstorms. This study can provide insight into optimizing public transportation network planning and design in many northern regions and help to coordinate land and transportation utilization.

A Neutrosophic Decision-Making Methods of the Key Aspects for Supply Chain Management in International Business Administrations

The importance of supply chain management in the field of international business administration is investigated in this study. Global businesses rely heavily on effective supply chain management, which coordinates the international transfer of materials, data, and money. The paper illuminates the critical nature of supply chain management on a worldwide scale. Distance, cultural differences, legal constraints, and logistics are only some of the problems and complexity of international supply chain management that are explored in this article. Topics covered include supplier selection and management, demand forecasting, inventory control, transportation, and distribution network design, as well as other techniques used by businesses to improve their worldwide supply chains. The study also discusses how international supply networks are affected by globalization, free trade agreements, and geopolitical considerations. Organizational strategies for overcoming hurdles such as tariffs, quotas, and political instability in international commerce are discussed. This paper used the neutrosophic sets (NSs) to deal with uncertainty in assessment factors of supply chains in international business. The NS is integrated with the DEMATEL method. The neutrosophic DEMATEL is used to show relationships between factors.

Towards a dynamic effective drainage area map for the Canadian Prairie: Sensitivity of contributing area to wetland storage capacity

Wetlands that occupy topographic depressions are a defining feature of the Canadian Prairie. These features control hydrological connectivity as they contain high storage capacity relative to typically available precipitation and runoff. Altering wetland distribution changes the frequency with which areas can become hydrologically connected to the catchment outlet. There are several methods that have proven successful in estimating contributing area response to changes in wetland storage, but none have been applied widely across the Canadian Prairie. The objective of this study was (1) to determine if the rate at which contributing area changes with loss of wetland storage capacity is related to wetland distribution, and if so, (2) to map sensitivity across the region. To do so, a GIS analysis was employed in which iterative measurements were made of contributing area expansion with simulated wetland storage capacity loss. Results show that those catchments with more small wetlands have more sensitive contributing areas to storage capacity loss. Extrapolation of this relationship across the Canadian Prairie shows that areas in western Manitoba and southeastern Saskatchewan are among the most sensitive. These results provide a better understanding of how contributing area may change with loss of wetland storage capacity. This may inform implementation of beneficial management practices, transportation network design, and management of streamflow and nutrient transport to downstream lakes across the region.

Sustainable road network design considering hydrogen fuel cell vehicles

Environmental pollution and energy shortages have brought about an increased focus on new energy vehicles. Hydrogen fuel cell vehicles (HFCVs) have experienced rapid development due to the potential to alleviate energy pressures and reduce pollution emissions. Near zero-carbon emissions offer a promising avenue for promoting sustainable transportation development. To evaluate the impact of HFCVs on the transportation environment, this paper investigates the problem of sustainable transportation network design including HFCVs. Specifically, the problem is formulated as a bi-level multi-objective programming problem, with the upper level aimed at determining the optimal network design scheme considering multiple objectives, while the lower level addresses the mixed traffic flow that comprises both HFCVs and fuel vehicles. To solve the multi-objective sustainable network design problem, an integrated solution framework that combines the particle swarm optimization (PSO) algorithm with the Frank-Wolfe algorithm (FW) is developed. Specifically, the PSO algorithm is utilized to solve the upper-level model and identify the optimal network design schemes, while FW algorithm is adopted to handle mixed traffic flow assignments. Finally, the proposed model and algorithm are implemented in two numerical experiment to demonstrate their effectiveness and efficiency.

Max-type reliability in uncertain post-disaster networks through the lens of sensitivity and stability analysis

The functionality of infrastructures, particularly in densely populated areas, is greatly impacted by natural disasters, resulting in uncertain networks. Thus, it is important for crisis management professionals and computer-based systems for transportation networks (such as expert systems) to utilize trustworthy data and robust computational methodologies when addressing convoluted decision-making predicaments concerning the design of transportation networks and optimal routes. This study aims to evaluate the vulnerability of paths in post-disaster transportation networks, with the aim of facilitating rescue operations and ensuring the safe delivery of supplies to affected regions. To investigate the problem of links' tolerances in uncertain networks and the resiliency and reliability of paths, an uncertainty theory-based model that employs minmax optimization with a bottleneck objective function is used. The model addresses the uncertain maximum reliable paths problem, which takes into account uncertain risk variables associated with links. Rather than using conventional methods for calculating the deterministic tolerances of a single element in combinatorial optimization, this study introduces a generalization of stability analysis based on tolerances while the perturbations in a group of links are involved. The analysis defines set tolerances that specify the minimum and maximum values that a designated group of links could simultaneously fluctuate while maintaining the optimality of the max-type reliable paths. The study shows that set tolerances can be considered as well-defined and proposes computational methods to calculate or bound such quantities - which were previously unresearched and difficult to measure. The model and methods are demonstrated to be both theoretically and numerically efficient by applying them to four subnetworks from our case study. In conclusion, this study provides a comprehensive approach to addressing uncertainty in reliability problems in networks, with potential applications in various fields.

Globalized robust bilevel optimization model for hazmat transport network design considering reliability

Globalized robust bilevel optimization model for hazmat transport network design considering reliability

Optimizing the geometry of transportation networks in the presence of congestion.

Urban transport systems are gaining in importance, as an increasing share of the global population lives in cities and mobility-based carbon emissions must be reduced to mitigate climate change and improve air quality and citizens' health. As a result, public transport systems are prone to congestion, raising the question of how to optimize them to cope with this challenge. In this paper, we analyze the optimal design of urban transport networks to minimize the average travel time in monocentric as well as in polycentric cities. We suggest an elementary model for congestion and introduce a numerical method to determine the optimal shape among a set of predefined geometries considering different models for the behavior of individual travelers. We map out the optimal shape of fundamental network geometries with a focus on the impact of congestion.

Liner shipping network design model with carbon tax, seasonal freight rate fluctuations and empty container relocation

In response to the challenges posed by the shipping carbon tax resulting from the low-carbon environmental policy, the seasonal fluctuation of freight rates in the container liner shipping market, and the reduced flexibility in fleet adjustment, we develop a bi-level programming model to maximize the average revenue per container ship and the total revenue of the fleet.The upper-level model selects the optimal shipping networks for both the off-season and peak season, while also designing the liner fleet for these two networks. The lower-level model optimizes the slot allocation scheme and the empty container storage and transportation scheme to evaluate the revenue of the network calculated by the upper-level model. Three meta-heuristic algorithms are proposed. We take the China-West Europe liner shipping route as the research object, and conduct numerical experiments on different optimization objectives and liner types. Results demonstrate that maximizing the average revenue per container ship involves reducing the carbon tax cost, simplifying the trunk route structure, and increasing the number of feeder routes. These changes lead to reduced satisfaction rates for transportation demands in both China and Europe. If the company seeks to maximize total revenue, they may achieve the opposite result. Therefore, liner companies should reasonably set optimization goals, adjust the network structure and liner operation status in a timely manner, and scientifically allocate container ship and empty containers to achieve the operational goals of reducing carbon emissions and maximizing revenue to cope with the volatile market and the low-carbon regulations.

Formulation and solution technique for agricultural waste collection and transport network design

Agricultural waste management in developing countries has become a challenging issue for rural planners due to the lack of an efficient planning tool. In the countries, farmers burnt agricultural waste at fields after each harvesting season to solve the issue. As a result, it has caused air and water pollution in the rural areas of the countries. In this paper, we present a mixed-integer nonlinear programming model for agricultural waste collection and transport network design that aims to stop burning waste and use the waste to produce bio-organic fertilizer. The model supports rural planners to optimally locate waste storages, and to determine the optimal set of routes for a fleet of vehicles to collect and transport the waste from the storages to the bio-organic fertilizer production facility. In the novel location-assignment-routing problem, the overall objective is to minimize total cost of locating storages, collecting waste from fields and planning vehicle routes. A solution technique is developed to linearise the mixed-integer nonlinear programming model into a model in linear form. In addition, a parallel water flow algorithm is developed to solve efficiently the large-sized instances. The efficiency of the proposed model and algorithm is validated and evaluated on the real case study in Trieu Phong district, Quang Tri province, Vietnam, as well as a set of randomly generated large-sized instances. The results show that our solution approach outperforms the general optimisation solver and tabu search algorithm. Our algorithm can find the optimal or near-optimal solutions for the large-sized instances within a reasonable time.

Humanitarian transportation network design via two-stage distributionally robust optimization

Natural disasters are highly unpredictable, with varying degrees of magnitude, and thus require a reliable and robust humanitarian relief network. Faced with the adverse effects of disasters, we advocate taking pre-disaster preventive actions, e.g., road link strengthening, to mitigate post-disaster disruptions to road networks. In this paper, we study a highly integrated humanitarian relief network design problem, in which the network strengthening plan and inventory pre-positioning scheme are optimized cooperatively. The proposed problem is formulated as a two-stage distributionally robust optimization (DRO) model, in which the first and second stages correspond to pre- and post-disaster relief operations, respectively. By leveraging prior data and the Wasserstein metric, a tailored ambiguity set is constructed to capture both node- and link-wise uncertainties. We demonstrate that the two-stage DRO model over the Wasserstein ambiguity set has an equivalent reformulation that can be solved via the L-shaped method with bilinear subproblems. Based on observations of the problem structure and the spirit of separation optimization, both exact and heuristic approaches are proposed to address the bilinear subproblems. Via the case study of the Yushu earthquake, we highlight the value of jointly optimizing network strengthening and inventory pre-positioning decisions. Specifically, with the same investment budget, integrating network strengthening into inventory pre-positioning can easily achieve an approximately 60% reduction in shortage penalties. Furthermore, our method can produce reliable solutions, based on which we explore several managerial implications.