Transportation Network Design Research Articles

Multiobjective Environmentally Sustainable Road Network Design Using Pareto Optimization

AbstractIn this article, a bi‐level transportation network design problem with environmental considerations is investigated. To explicitly reflect various requirements of environmental sustainability from planners, total emissions costs and total excessive noise cost are minimized along with total system travel time while performing optimal capacity expansion. To leave space for additional information on decision making for planners and provide a more generalized description of solution optimality, the Pareto optimization approach is adopted. The study proposes a multiobjective variant of a new metaheuristic named Chemical Reaction Optimization as the optimization tool to solve the formulated network design problem. Pareto front approximations have been successfully acquired for each scenario tested using the Sioux Falls network. The results show that the relations between pairwise objectives can change significantly (e.g., from conflicting to aligned) with regard to different demand situations. The newly proposed multiobjective metaheuristic succeeds to produce better approximations of the Pareto front than NSGA‐II with comparable runtime for the considered road network.

Integrating uncertainty considerations into multi-objective transportation network design projects accounting for environment disruption

Few previous works integrated both uncertainty and environment disruption into traffic network design problems (NDPs). This study aims to address this gap. First, the mathematical framework of the strategic user equilibrium (StrUE) traffic assignment under volatility of both total travel demand and link capacity is analyzed. Second, we incorporate the StrUE traffic assignment into a network design project and propose a multi-objective bi-level NDP program. Two objective functions are formulated, which are respectively to minimize the expected total system travel time and minimize the expected total system off-gas emissions under StrUE. Third, we develop two tailored solution methods – an enumerative algorithm and a metaheuristic method based on a genetic algorithm. Finally, systematic evaluation of the performance of the proposed approach is conducted. The results highlight that ignoring uncertainty considerations can result in sub-optimal design solutions in terms of expected network performance. Also, the two objectives, to minimize system-level travel time and vehicle emission, are conflicting for certain design scenarios.

Public transportation network design: a geospatial data-driven method

Public transportation network design: a geospatial data-driven method

Emergency transportation network design problem: Identification and evaluation of disaster response routes

Since disasters have considerable effects on transportation networks, the functionality of an emergency transportation network can play an important role in mitigation phase, especially in developing countries that sometimes suffer the sad experience of almost complete destruction of several cities. Transportation related disaster response activities typically include search and rescue, emergency medical care and fire-fighting trips. In this paper, the emergency transportation network design problem is proposed to determine the optimal network to perform emergency response trips with high priority in the aftermath of earthquakes. The problem has three objective functions designated to identify the optimal routes for emergency vehicles considering the length, the travel time and the number of paths as performance metrics of network vulnerability. A combined approach for considering the three objectives including weighted sum and lexicographic methods is used. The proposed model is solved using a branch-and-cut solution method. The suggested method is tested on the well-known Sioux-Falls network as well as on the real-world network of Tehran metropolis, Iran. Computational experiments are conducted to examine the effects of varying the maximum network length, and the relative weights of other objectives.

Fuzzy multi-objective approach for optimal selection of suppliers and transportation decisions in an eco-efficient closed loop supply chain network

Establishment of a closed loop supply chain (CLSC) network has attracted immense significance due to government policies and societal demand for environmental consciousness. However, to enhance the financial and ecological impact of the network, the forward and reverse supply chain networks must integrate well so that decisions taken in both areas complement each other. In this study, we propose an eco-efficient CLSC design for extending the existing supply chain of an Indian firm that assembles inkjet printers. The network design is configured as a multi-objective model in a multi-period setting and is mathematically formulated into a mixed integer programming problem with fuzzy objectives. Fuzziness provides flexibility to the decision makers because they must accommodate the conflicting nature of the objectives. The fuzzy multi-objective model incorporates the firm's economic and environmental concerns into the decision making process by selecting environmentally responsible suppliers to procure components based on sustainable criteria, choosing appropriate recovery options for end-of-use (EOU) inkjet printers, and planning an efficient transportation network design for reducing the carbon emission of the distribution and collection activities. The uniqueness of the proposed fuzzy CLSC optimization model lies in providing an integrated decision making framework that can aid the manufacturer in making crucial strategic, tactical, and operational decisions of optimal selection of suppliers, component order allocation, recovery flow allocation, and vehicle routing planning. The novelty of the model also lies in simultaneously minimising the overall cost of the activities undertaken, maximizing the performance of the component suppliers and minimising the carbon emissions of the associated transportation activities. A weighted fuzzy mathematical programming approach is utilised for generating a fuzzy, properly efficient solution as the desired compromised solution for the CLSC network problem configuration. The relevance of the model is justified using a real data set derived from a case study of the firm based in the northern capital region (NCR) of India. The findings indicate that the proposed integrated CLSC network model enables the firm in gaining sustainably from the numerous electronic product reuse opportunities in the Indian market. Further, while costly to begin with, choosing suppliers with higher sustainable performance and vehicles with lesser emission rate could substantially enhance firm's sustainable image and result in higher profits in the future.

A Hybrid Estimation of Distribution Algorithm for Multi-Objective Multi-Sourcing Intermodal Transportation Network Design Problem Considering Carbon Emissions

The increasing concern on global warming is prompting transportation sector to take into account more sustainable operation strategies. Among them, intermodal transportation (IT) has already been regarded as one of the most effective measures on carbon reductions. This paper focuses on the model and algorithm for a certain kind of IT, namely multi-objective multi-sourcing intermodal transportation network design problem (MO_MITNDP), in which carbon emission factors are specially considered. The MO_MITNDP is concerned with determining optimal transportation routes and modes for a series of freight provided by multiple sourcing places to find good balance between the total costs and time efficiencies. First, we establish a multi-objective integer programming model to formulate the MO_MITNDP with total cost (TTC) and maximum flow time (MFT) criteria. Specifically, carbon emission costs distinguished by the different transportation mode and route are included in the cost function. Second, to solve the MO_MITNDP, a hybrid estimation of distribution algorithm (HEDA) combined with a heterogeneous marginal distribution and a multi-objective local search is proposed, in which the from the Pareto dominance scenario. Finally, based on randomly generated data and a real-life case study of Jilin Petrochemical Company (JPC), China, simulation experiments and comparisons are carried out to demonstrate the effectiveness and application value of the proposed HEDA.

Competitive Liner Shipping Network Design

We present a solution method for the liner shipping network design problem which is a core strategic planning problem faced by container carriers. We propose the first practical algorithm which explicitly handles transshipment time limits for all demands. Individual sailing speeds at each service leg are used to balance sailing speed against operational costs, hence ensuring that the found network is competitive on both transit time and cost. We present a matheuristic for the problem where a MIP is used to select which ports should be inserted or removed on a route. Computational results are presented showing very promising results for realistic global liner shipping networks. Due to a number of algorithmic enhancements, the obtained solutions can be found within the same time frame as used by previous algorithms not handling time constraints. Furthermore, we present a sensitivity analysis on fluctuations in bunker price which confirms the applicability of the algorithm.

Activity-Based Approach for the Design of Sustainable Area and Cordon Pricing Schemes

Background Vehicle-generated emissions remain a serious threat to public health, and a major contributing factor to climate change. In response to this situation, several cities around the world have successfully reduced vehicle emissions by implementing area and cordon-based pricing (ACP). Despite these successes, designing ACP schemes continues to be a challenging task given the complexities of estimating the multidimensional effects of this type of strategy. Existing transportation network design methodologies focus primarily on congestion-related goals, and employ an aggregate representation of travel demand. However, the assessment of the public health impacts of vehicle emissions requires information on disaggregate travel and activity participation behavior, as well as spatially and temporally varying pollutant concentrations; existing methodologies do not meet the planning needs of agencies interested in determining the optimal design of ACP schemes that explicitly account for both congestion and public health. Methods We propose an optimization model that can be used by transportation planning agencies to determine optimal charging boundary locations and toll levels with the objective of reducing the health impacts of vehicular traffic and improve mobility. The proposed ACP design problem is formulated as a bi-level, simulation-based optimization model. The problem's upper-level is composed of the policy makers’ goals (i.e., health and mobility objectives). The travelers’ responses to the policy maker's decisions, as well as the resulting system-wide impacts, are analyzed in the lower-level. The lower-level model system is composed of five sub-models: (a) a disaggregate activity-based travel behavior model to simulate the agents’ behavioral response to ACP specifications, (b) a traffic assignment model to estimate the distribution of traffic in the network, (c) a traffic emissions model, (d) a pollutant dispersion model, and (e) a health impact assessment model to estimate the changes in public health risks resulting from ACP schemes. The proposed methodology is computationally expensive given the type of models being combined. Therefore, we present a multi-objective, surrogate-based optimization approach that is intended to accelerate the discovery of good solutions to the design problem. Results An illustrative application of the proposed methodology is presented using Chicago as a case study. As expected, preliminary results suggest that there is a tradeoff between improving travel mobility objectives and reducing traffic's public health impacts. Conclusions The proposed methodology can be an important tool for transportation planners. Further research is necessary to refine the different parameters and strategies used in the solution algorithm.

5G transport network requirements for the next generation fronthaul interface

To meet the requirements of 5G mobile networks, several radio access technologies, such as millimeter wave communications and massive MIMO, are being proposed. In addition, cloud radio access network (C-RAN) architectures are considered instrumental to fully exploit the capabilities of future 5G RANs. However, RAN centralization imposes stringent requirements on the transport network, which today are addressed with purpose-specific and expensive fronthaul links. As the demands on future access networks rise, so will the challenges in the fronthaul and backhaul segments. It is hence of fundamental importance to consider the design of transport networks alongside the definition of future access technologies to avoid the transport becoming a bottleneck. Therefore, we analyze in this work the impact that future RAN technologies will have on the transport network and on the design of the next generation fronthaul interface. To understand the especially important impact of varying user traffic, we utilize measurements from a real-world 4G network and, taking target 5G performance figures into account, extrapolate its statistics to a 5G scenario. With this, we derive both per-cell and aggregated data rate requirements for 5G transport networks. In addition, we show that the effect of statistical multiplexing is an important factor to reduce transport network capacity requirements and costs. Based on our investigations, we provide guidelines for the development of the 5G transport network architecture.

Designing a Reliable and Dynamic Multimodal Transportation Network for Biofuel Supply Chains

This paper presents a cost-efficient and reliable supply chain network design model for biomass to be delivered to biofuel plants. Biomass is bulky, so transportation modes such as rail and barge can be used to deliver this product. For this reason, this study focuses on multimodal supply chain designs for biofuel. Biomass supply is highly seasonal, but the high production seasons for biomass in the Southeast United States often coincide with or are followed by hurricanes, and drought seasons, both of which impact transportation. The dynamic multimodel transportation network design model this paper presents enables this supply chain to cope with biomass supply fluctuations and to hedge against natural disasters. The mixed-integer nonlinear programming model proposed is an 𝒩𝒫-hard problem, and we develop an accelerated Benders decomposition algorithm and a hybrid rolling horizon algorithm to solve this problem. We tested the performance of the algorithm on a case study using data from the Southeast United States. The numerical experiments show that this proposed algorithm can solve large-scale problem instances to a near optimal solution in a reasonable time. Numerical analyses indicate that, under normal conditions, the minimum cost model outperforms the reliable models. However, under disaster scenarios, the minimum cost model is 2.65% to 9.20% more expensive than the reliable and static model and 6.28% to 17.73% more expensive than the reliable and dynamic model. Thus, the reliable and dynamic multimodal network design decisions can aid biofuel supply chain management decisions, especially when considering the potential impacts of natural disasters.

A Bio-Inspired Approach to Traffic Network Equilibrium Assignment Problem.

Finding an equilibrium state of the traffic assignment plays a significant role in the design of transportation networks. We adapt the path finding mathematical model of slime mold Physarum polycephalum to solve the traffic equilibrium assignment problem. We make three contributions in this paper. First, we propose a generalized Physarum model to solve the shortest path problem in directed and asymmetric graphs. Second, we extend it further to resolve the network design problem with multiple source nodes and sink nodes. At last, we demonstrate that the Physarum solver converges to the user-optimized (Wardrop) equilibrium by dynamically updating the costs of links in the network. In addition, convergence of the developed algorithm is proved. Numerical examples are used to demonstrate the efficiency of the proposed algorithm. The superiority of the proposed algorithm is demonstrated in comparison with several other algorithms, including the Frank-Wolfe algorithm, conjugate Frank-Wolfe algorithm, biconjugate Frank-Wolfe algorithm, and gradient projection algorithm.

A dual approximation-based quantum-inspired genetic algorithm for the dynamic network design problem

In this paper, we formulate a dynamic transportation network design model in which traffic dynamics are modeled by the cell transmission model. In the formulation, transportation planners decide on the optimal capacity expansion policies of existing transportation network infrastructure with limited resources, while road users react to the capacity changes by selfishly choosing routes to maximize their own profit. Owing to the problem complexity, a majority of the research efforts have focused on tackling this problem using meta-heuristics. In this study, we incorporate a series of dual-variable approximation techniques into the paradigm of a quantum-inspired genetic algorithm (QIGA) and devise an efficient evaluation function based on these techniques. The proposed QIGA contains a series of enhancements compared to conventional genetic algorithms (GAs) and can be considered as a better alternative when solving problems with a complex solution space. The QIGA is applied to a synthetic network, a subnetwork of a real-world road network, and a realistic network to justify its theoretical and practical value. From the numerical results, it is found that in the same computational time, the QIGA outperforms the conventional GA by 3.86–5.63% in terms of the objective value, which can be significant, especially when network expansion of a large urban area is considered. Technical, computational, and practical issues involved in developing the QIGA are investigated and discussed.

A multilevel spatial interaction model of transit flows incorporating spatial and network autocorrelation

While it is well known that ignoring spatial dependence often results in misspecification of models, travel demand models almost never account for this phenomenon. The aim of this paper is to empirically demonstrate the importance of accounting for potential spatial dependence between observations in the specification of spatial interaction models. As a case study, we analyze travel flows on the public transport system in an urban region in the Netherlands. We develop five distinct spatial interaction models (SIMs) of increasing complexity, each encompassing a lower and upper level model. At the lower level, the attractiveness of neighborhoods for boarding and alighting is modeled based on spatial and transit supply characteristics. At the upper level, spatial interactions among zones are modeled taking into account competing origins, competing destinations as well as network characteristics. We systematically compare more traditional SIM formulations with a SIM that explicitly accounts for spatial and network autocorrelation. The results show a substantial difference between the former models and the latter, in terms of the estimated total marginal impacts of the different variables and the pattern of the error terms. The results of our study underscore that the failure to incorporate autocorrelation effects in travel models is likely to influence model outcomes, which in turn may have profound implications for the very design of public transport networks in cities and regions.

Investigating transport network vulnerability by capacity weighted spectral analysis

Transport networks operating at or near capacity are vulnerable to disruptions, so flow bottlenecks are potent sources of vulnerability. This paper presents an efficient method for finding transport network cuts, which may constitute such bottlenecks. Methods for assessing network vulnerability found in the literature require origin-destination demands and path assignment. However, in transport network planning and design, demand information is often missing, out of date, partial or inaccurate. Capacity weighted spectral partitioning is proposed to identify potential flow bottlenecks in the network, without reference to demand information or path assignments. This method identifies the network cut with least capacity, taking into account the relative sizes of the sub-networks either side of the cut. Spectral analysis has the added advantage of tractability, even for large networks, as shown by numerical examples for a five-node illustrative example, the Sioux Falls road network and the Gifu Prefecture road network.

Cooperative liner shipping network design by means of a combinatorial auction

Cooperation in the ocean liner shipping industry has always been important to improve liner shipping networks (LSN’s). As tight cooperations like alliances are challenged by antitrust laws, looser forms of cooperation among liner carriers might become a reasonable way to increase efficiency of LSN’s. Our goal is to facilitate a loose form of cooperation among liner carriers. Therefore, we introduce a coordination mechanism for designing a collaborative LSN based on a multi round combinatorial auction. Via the auction, carriers exchange demand triplets, i.e. orders which describe the transport of containers between ports. A standard network design problem which includes ship scheduling and cargo routing decisions is used as isolated network design problem of an individual carrier. A carrier has to solve this isolated problem repeatedly during the auction so that the carrier is able to decide which demand triplets to sell, on which demand triplets to bid, and what prices to charge. To solve these problems we propose a variable neighborhood search based matheuristic. The matheuristic addresses the isolated planning problem in four phases (construct ship cycles, modify cycles, determine container flow, and reallocate ships to cycles). Our computational experiments on a set of 56 synthetic test instances suggest that the introduced combinatorial auction increases profits on average compared to isolated planning significantly by 4%. The more diverse the original assignment of demand triplets and ships to carriers is, the higher the potential for collaboration; for 18 diverse instances, the profits increase on average by 10%.

Optimization in liner shipping

Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.

Heuristic urban transportation network design method, a multilayer coevolution approach

The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users’ route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.

A road network design model considering horizontal and vertical equity: Evidences from an empirical study

Most of the models for solving the transportation Network Design Problem (NDP) often neglect equity goals that, assuming the societal function of transportation systems, should play an important role. The NDP should be addressed to solutions in which the outcomes of the design (costs and/or benefits) are distributed as fairly as possible among the potential users or class of users. In this paper, an Equity Based NDP is proposed, where the optimal layout of a road network is determined by minimizing the total system cost under flexible equity constraints while taking into account the route choice behavior of network users.The considered equity goals are represented by a proper outcome indicator, specified for uncertain and/or approximate variables. The consequent NDP model is then a multi-objective fuzzy programming model that allow determining the most equitable solution aiming at reducing the realization costs according to a set of constraints. An application of the proposed method is given to solve the equity network design optimization and the results are discussed.

Facing equity in transportation Network Design Problem: A flexible constraints based model

In transportation planning, solutions designed to meet objectives of equity and social inclusion have to be achieved. From this standpoint, most of Network Design Problem (NDP) models aim at identifying the optimal layout of transportation networks by deterministic bi-level problems formulation to reflect the different goals of at least two decision makers (the network users and the planner).Considering the societal function of transport systems, the NDP, even at operational level, should be addressed also to the research of solutions in which the outcomes of the design (costs and/or benefits) are distributed as much as possible among the potential users or classes of users.Traditional approaches often neglect equity goals that, conversely, should play an important role or define some flows rebalancing problem. In this paper an Equity Based NDP is proposed, where the optimal layout of a road network is determined by minimizing the total system cost under flexible constraints, (jointly with rigid thresholds) by solving a single programming problem. It considers both horizontal and vertical equity criteria in the form of an equity constraint specified for uncertain variables or approximate reasoning environment: this results in a multi-objective fuzzy programming model that aims at maximizing user satisfaction according to all constraints, while taking into account the route choice behaviour of network users.A sensitivity analysis has been performed on a test network, and a comparison with a literature equity based model has been carried out; then, the proposed method has been applied to a real sized network, where an application of equity network design optimization is presented and the results discussed.

Discrete intermodal freight transportation network design with route choice behavior of intermodal operators

We consider a discrete intermodal network design problem for freight transportation, in which the network planner needs to determine whether or not to build up or expand a link to minimize the total operating cost of carriers and hub operators under a general route choice model of intermodal operators. We formulate the problem as a mixed-integer nonlinear and non-convex program that involves congestion effects, piecewise linear cost functions, and a fixed-point constraint. We develop a series of relaxed and equivalent models to reduce the hardness of the problem and provide theoretical results to show the equivalences. We present two solution methods to solve the problem with one returning heuristic solutions and the other generating a globally optimal solution. We offer two numerical experiments to test the two solution algorithms and also shed light on their performance comparisons.