Transportation Network Design Research Articles

Quantum computing for transport network design problems

Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.

Mathematical program with equilibrium constraints approach with genetic algorithm for joint optimization of charging station location and discrete transport network design

ABSTRACT This paper focuses on the joint optimization of the charging station location problem (CSLP) and discrete network design problem (DNDP) in a transportation network. We present a variational inequality (VI) formulation to describe the user equilibrium (UE) state of gasoline vehicles (GVs) and electric vehicles (EVs). Based on the mixed-UE model, a mathematical program with equilibrium constraints (MPEC) model is formulated for integrating the decisions of deploying EV charging stations (EVCSs) and adding new links to minimize the total travel cost (TTC) of all vehicles. A modified genetic algorithm is developed to tackle the MPEC model with an adaptive path generation procedure to address the mixed-UE model. Finally, we conduct numerical experiments to identify the efficacy of the proposed models and algorithms. Specifically, we propose a two-step optimization model and explore a performance comparison between the joint and two-step optimization approaches, while the joint optimization exhibits superiority in minimizing the TTC.

Lagrangean relaxation algorithms for fixed-charge capacitated relay network design

We consider a fixed-charge capacitated relay network design problem (CRNDP), which has widespread applications in the design of telecommunication and transportation networks, where relay points are presented as signal regenerators, truckload driver and/or tractor exchange stations or electrical vehicle recharging station. Our CRNDP integrates the key features of single allocation hub location problem (SAHLP) and capacitated network design problem (CNDP). Typical range restrictions in truckload transportation, electrical vehicles as well as for keeping signal strength in telecommunications, and further limitations of flow on the links of an underlying network in these mediums motivate the use of distance constraints and link capacity constraints in the design of a relay network for this diverse set of applications. To efficiently solve our model, we propose a Lagrangean relaxation algorithm, where the upper bounds are enhanced by a heuristic during the iterations and the lower bound is improved by the introduction of surrogate constraints. The extensive computational studies are conducted to examine the performance of the algorithm and the results show that the solution method we propose can efficiently solve CRNDP and obtain good-quality solutions, even for very large size instances.

Choice and equity: A critical analysis of multi-modal public transport services

Equity and justice have long been central in transport policy. The emphasise on the importance of individuals' access to social and economic opportunities stresses the link between accessibility and concepts like agency and freedom of choice. It calls for a nuanced and comprehensive understanding of accessibility that considers specific contextual factors that limit people's transport choices, such as the design of public transport networks and services, government interventions, and competition between public transport companies. To promote people's potential mobility and freedom of choice, it is crucial to initially assess the spatial distribution of transport options. In this article, we synthesise the findings from existing scholarship on transport choice, reinforcing the need for (re)considering choice in transport equity. We propose a metric to evaluate the availability of transport choices by considering the trade-off between time and money. By analysing household survey responses on transport choices and transit service datasets, we investigate the spatial disparities in the availability of transport choices within a multimodal public transportation system in Hong Kong and explore how adjusting unappealing choices can potentially enhance transport equity.

A Battery Electric Vehicle Transportation Network Design Model with Bounded Rational Travelers

With governments worldwide emphasizing environmental protection and the global focus on carbon reduction, the battery electric vehicle (BEV) industry has developed rapidly. An urban transportation network with BEVs as the main form of transportation will soon become mainstream. Motivated by the abovementioned background, a BEV transportation network design problem is investigated, and a network design model is established. The model aims to minimize the system travel time of BEV transportation networks and optimize the government’s lane expansion scheme (the location and number of lanes) under a limited budget. To consider the travel characteristics of BEV drivers, the charging time, range anxiety, and bounded rationality factors are simultaneously incorporated into the model. A heuristic algorithm is designed based on the active set algorithm to obtain the local optimal solution to the actual-scale problem. Moreover, a cutting-plane method is used to convert the original problem into a different form, and a column generation technique is embedded in the abovementioned algorithm to avoid the enumeration of paths. Sensitivity analyses of different levels of rationality of BEV drivers and government investment scales are performed. The experimental results demonstrate that the model and algorithm can effectively solve the problem and provide decision support for the government in formulating transportation infrastructure construction policies.

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.

Cyclists' exposure to traffic-generated air pollution in multi-modal transportation network design problem.

Moving toward sustainable transportation is one of the essential issues in cities. Bicycles, as active transportation, are considered an important part of sustainable transportation. However, cyclists engage in more physical activity and air intake, making the quality of air that they inhale important in the programs that aim to improve the share of this mode. This paper develops a multi-modal transportation network design problem (MMNDP) to select links and routes for cycling, cars, and buses to decrease the exposure of cyclists to traffic-generated air pollution. The objective functions of the model include demand coverage, travel time, and exposure. The study also examined the effect of having exclusive lanes for bicycles and buses on the network. In the present study, the non-dominated storing genetic algorithm (NSGA-II) solves the upper-level and a method of successive average (MSA) unravels the lower level of the model. A numerical example and four scenarios evaluate the trade-off between different objective functions of the proposed model. The results reveal that considering exposure to air pollution in our model results in a slight increase in travel time (4%) while the exposure to traffic-generated air pollution for cyclists was reduced significantly (47%). Exclusive lanes also result in exposure reduction in the network (60%). In addition, the demand coverage objective function performs well in increasing the total demand in the network by 47%. However, more demand coverage leads to a rise in travel time by 28% and exposure by 58%. The model also showed an acceptable result in terms of exposure to traffic-generated air pollution compared to the model in the literature.

Fleeting Route Design with Uncertainty

During COVID-19, the air industry has shrunk due to drastically reduced demand and flight bans. Air transport network optimization is significant in fleet routing design, which helps the air company to make a decision to open the new routes while maximizing the profit. We want to study when an area is subjected to stability shocks that prevent air trac from entering, how air planners comprehensively consider the factors to design new fleeting routes while optimizing the profit. In this research, we start by developing a baseline model and then a networked model, both theoretical, to simulate the situation for an air company and provide optimization results. We find a simple situation to determine: if a route has a positive maximized expected profit, we will decide to open the route. The optimal results obtained from the model are proved to be optimal by mathematics analysis in the study. Several potential future study directions relevant to the research are discussed, including stochastic demand and network connection, which could be better explain the situation of air transport network design and thus be more applicable in reality.

RESTORATION OF THE DESTROYED INFRASTRUCTURE BASED ON THE USE OF DIGITAL DATA

Digitization of destroyed infrastructure is an important task in geodesy and cartography. It has significant practical potential in various areas, such as recovery after natural disasters, urban planning, design of transport networks and infrastructure. 
 Digitization of the destroyed infrastructure allows reducing the time and costs of data collection and analysis, and it allow obtaining more accurate information about the state of the infrastructure. This is important when planning and implementing projects for the restoration and development of territories. 
 Studying the possibilities of digitization of the destroyed infrastructure, as well as identifying key challenges and developing strategies for the restoration and development of infrastructure based on the use of processed digitized data is the task of this research. Possibilities and advantages of digitization of destroyed infrastructure in this article were considered. The main methods of vectorization of destroyed objects based on the comparative analysis of images investigated. The scale of the destruction and restoration of the infrastructure of the city of Irpin was determined. 
 The results of the digitization of destroyed infrastructure can be useful for various industries related to infrastructure planning, rehabilitation and development. They can help reduce the time and cost of data collection and analysis. The results can be presented as evidence of damage caused by an aggressor country.

Bibliometric Review of Route Optimization in Maritime Transportation: Environmental Sustainability and Operational Efficiency

Route optimization in maritime transportation provides enormous cost saving and efficient resource allocation for companies. Shipping companies need to create an optimized route for shipping fleets. That is why route optimization has gained considerable and increasing attention in maritime transportation literature over the past few years. This research aims to identify the theoretical cornerstones, current research trends, and possible research avenues for future studies by reviewing 256 peer-reviewed route optimization articles in maritime transportation literature. For this purpose, first, theoretical cornerstones of route optimization in maritime transportation literature were examined by co-citation analysis and three clusters were identified: fleet routing and deployment; liner shipping rotation; and optimization of fuel consumption and vessel speeds. Second, current research trends of route optimization in maritime transportation literature were uncovered via bibliographic coupling analysis. The analysis results revealed that current research trends could be considered in four clusters: route planning; routing under different weather conditions; liner shipping network and service design; and environmental speed optimization. Finally, author-keyword analysis was performed to specify knowledge gaps and recommend future research areas.

Liner-Shipping Network Design with Emission Control Areas: A Real Case Study

In recent years, liner-shipping companies have faced a traditional trade-off between cost and emission (CO2 and SOX) reduction. This study considers this element to construct a liner-shipping network design model which includes a package-cargo transport plan, route allocation, and route design. The objective is to maximize profit by selecting the ports to be visited, the sequence of port visits, the cargo flows between ports, and the number/operating speeds of vessels. In addition, emission control areas (ECAs) exist in the liner network. With reference to the idea of the column generation algorithm, this study proposed a heuristic algorithm based on empirical data through a real case calculation and selected the optimal scheme, which is in-line with both economic and environmental benefits. The results show that the model and optimization method are feasible and provide an effective solution for the liner network design of shipping companies, while also considering environmental factors. In addition, the effects of the number of ECAs, inter-port origin-destination (OD) demand, freight rate, fuel price, and carbon prices on the design of transport networks are discussed to provide a reference for the operation of shipping companies and government decision-making.

Uncertain Programming Model for the Cross-Border Multimodal Container Transport System Based on Inland Ports

The importance of inland ports in promoting current cross-border trade is increasingly recognized. In this work, we aim to design the entire network for the cross-border multimodal container transport system based on inland ports. Unlike previous studies, we consider strong uncertainty in cross-border transportation demand to be caused by a variety of realistic factors such as the global economic situation, trade policies among countries, and global epidemics, etc. To handle the demand uncertainty, we develop an uncertain programming model for the considered cross-border multimodal container transportation network design problem to minimize the expectation of the total costs, including carbon emissions, by imposing two types of chance constraints for capacity limitations. Under mild assumptions, we further convert the proposed uncertain model into its equivalent deterministic one, which can be solved by off-the-shelf solvers such as CPLEX, Gurobi, and Lingo. Finally, we illustrate the applicability of the proposed model by taking the Huaihai Economic Zone-Europe multimodal container transport system as a real-world case study. The computational results provide valuable suggestions and policy guidance regarding four issues: the inland port locations, the transportation route choices, the strategies for reducing the total cost, and the schemes for improving network performance against uncertain demand.

Correction to: an efficient environmentally friendly transportation network design via dry ports: a bi-level programming approach

Correction to: an efficient environmentally friendly transportation network design via dry ports: a bi-level programming approach

A simulation-based optimization approach for designing transit networks

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

Incorporating travel time means and standard deviations into transportation network design problem: a hybrid method based on column generation and Lagrangian relaxation

ABSTRACT Travel times in real-world transportation networks are affected by many disruptions. When we conduct the network design optimization, the traffic condition and its resulting travel time variability should be taken into account. However, most of the previous network design optimizations adopted the lengths or expected travel times of links. Based on travel time means and standard deviations, we develop an arc-based model that is a nonlinear and concave integer program. By the Dantzig-Wolfe reformulation, we transform it into an equivalent column-based model that is an integer linear program with a large number of variables. Based on the column-based model, we develop a hybrid method based on column generation and Lagrangian relaxation. The restricted master problem can be settled by the linear programming solvers. The pricing subproblems incorporate independent reliable shortest path problems and a knapsack problem. In numerical experiments, the proposed method can generate feasible solutions with good integrality gaps.

Liner alliance shipping network design model with shippers' choice inertia and empty container relocation

<abstract><p>Liner companies have responded to escalating trade conflicts and the impact of the COVID-19 pandemic by forming alliances and implementing streamlined approaches to manage empty containers, which has strengthened the resilience of their supply chains. Meanwhile, shippers have grown more sensitive during these turbulent times. Motivated by the market situation, we investigate a liner alliance shipping network design problem considering the choice inertia of shippers and empty container relocation. To address this problem, we propose a bilevel programming model. The upper model aims to maximize the alliance's profit by optimizing the alliance's shipping network and fleet design scheme. The lower model focuses on optimizing the slot allocation scheme and the empty container relocation scheme. To ensure the sustainable operation of the alliance, we develop an inverse optimization model to allocate profits among alliance members. Furthermore, we design a differential evolution metaheuristic algorithm to solve the model. To validate the effectiveness of the proposed model and algorithm, numerical experiments are conducted using actual shipping data from the Asia-Western Europe shipping route. The results confirm the validity of the proposed model and algorithm, which can serve as a crucial decision-making reference for the daily operations of a liner shipping alliance.</p></abstract>

Transshipment network design for express air cargo operations in China

We introduce a novel multimodal (ground and air transportation) network design model with transshipments for the transport of express cargo with heterogeneous service classes (i.e., next morning delivery, and next day delivery). We formulate this problem using a novel path-based mixed-integer program which seeks to maximize the demand (weight) served. We investigate the value of the proposed transshipment network under various operational conditions and by benchmarking against a direct shipment network and a network with a single transshipment point which mimics a classical star-shaped hub-and-spoke network. Our extensive computational study with real-world data from ShunFeng (SF) Express reveals that the integration of ground and air transportation improves the coverage and that transshipment enables serving a large number of origin–destination pairs with a small number of cargo planes. Importantly, we show that by simplifying handling, i.e., employing cross-docking rather than time-consuming sortation, a transshipment network can transport express cargo fast enough to meet demanding delivery deadlines. Finally, we find that increasing the efficiency of intra-city operations and extending the nightly operating time window are the most effective operational adjustments for further improving the performance of the proposed transshipment network.

On the liner shipping network design: the Maritime Silk Route case study

The Maritime Silk Route is a crucial channel for economic exchanges between China and Europe. In the year 2018, the sea route between Asia and Europe moved more than 24 million TEU. In this context, the optimal design of liner services is of crucial importance. We present a general framework for the optimal design of these services. First, we develop a gravity model representing the spatial interaction between ports. Then we obtain a minimum spanning tree by means of Kruskal optimisation and finally we use the Louvain method to determine the communities within the network. Two scenarios are discussed. The first gives more importance to distances, while the second gives more importance to port throughputs. The paper discusses the main differences between the two cases and highlights which ports gain or lose connections depending on the market environment. The resulting graphs are a good reference for designing better services.

Optimization for Bi-Objective Express Transportation Network Design

Optimization for Bi-Objective Express Transportation Network Design

Optimization for bi-objective express transportation network design under multiple topological structures

With the rapid development of the courier industry, customers are placing higher demands on the cost and delivery time of courier services. Therefore, this paper focuses on the bi-objective express transportation network design problem (BO-ETNDP) to minimize the operation cost and maximum arrival time. A multi-structure parallel design methodology (MS-PDM) is proposed to solve the BO-ETNDP. In this methodology, all topological structures commonly used in designing transportation networks are sorted out. For each topological structure, a novel bi-objective nonlinear mixed-integer optimization model for BO-ETNDP is developed considering the impact of the hub’s sorting efficiency on the operation cost and arrival time. To solve these models, a preference-based multi-objective algorithm (PB-MOA) is devised, which embeds the branch-and-cut algorithm and Pareto dominance theory in the framework of this ranking algorithm. In the case study, the applicability of the proposed methodology is verified in a real-world leading express company. The results show that our methodology can effectively avoid the limitation of solving the BO-ETNDP with a specific structure. Besides, the suitable topology for designing express transportation networks in different scenarios are explored through the sensitivity analysis.