Transportation Network Design Research Articles

Simulating Transport Networks With a <italic>Physarum</italic> Foraging Model

Designing effective transport networks can be considered as one of the most debated problems in the area of computational intelligence. Some nature-inspired algorithms have shown excellent abilities in the adaptive network construction. In this aspect, a unique creature, called Physarum , has exhibited the computing capacity to optimize protoplasmic networks connecting distributed food sources. This inspires our work to design a Physarum foraging platform for constructing transport networks. Specifically, the traditional Physarum foraging model is adapted to construct transport networks in China. In order to get close to the real scenario, practical data are collected to build the environment of the Physarum foraging model and the structure of real transport networks. Some measurements in the domain of complex networks, such as average path length, network efficiency, topology robustness, and functional robustness, are used for performance comparison. The experimental results demonstrate that Physarum foraging models excel in constructing highly efficient and robust networks, which can be utilized for directing the design of transport networks in the real world.

Review of Urban Transportation Network Design Problems Based on CiteSpace

This paper provides a comprehensive review of urban transportation network design problems according to CiteSpace, including main problem classifications, mathematical models, and solution methods obtained from CiteSpace clusters. The review attempts to present the systematic picture of urban transportation network design and show the future directions of it.

Application of Bat Algorithm for Transport Network Design Problem

The requirement of the road services and transportation network development planning came into existence with the development of civilization. In the modern urban transport scenario with the forever mounting amount of vehicles, it is very much essential to tackle network congestion and to minimize the travel time. This work is based on determining the optimal wait time at traffic signals for the microscopic discrete model. The problem is formulated as a bilevel model. The upper layer optimizes the travel time by reducing the wait time at traffic signal and the lower layer solves the stochastic user equilibrium. Soft computing techniques like Genetic Algorithms, Ant Colony Optimization, and many other biologically inspired techniques prove to give good results for bilevel problems. Here this work uses Bat Intelligence to solve the transport network design problem. The results are compared with the existing techniques.

Exploring human genome feature for improving genetic algorithm performance

Genetic algorithm (i.e., GA) has longtermly obtained an extensive recognition for solving the optimization problem. Its pipelines process, which involves several operations, has been applied in many NP-hard problems, including the transportation network design problem (i.e., TNDP). As part of evolutionary computation methods, GA is inspired by Darwinian evolution, which is relied on the genetic operators (i.e., recombination, and mutation). On other side, the considerably achievement has been acquired by the genome researches, which offers an opportunity to deeply explore the recombination and mutation processes. This paper then presents variants of GA, which are inspired by the recent genome evidence of genetic operators. This exploration expectantly extends the benefit of evolution-based algorithm, which has been shown by the previous finding of GA. For examining the performance of proposed GA, the numerical experiment is involved for solving the TNDP. The performance comparisons show that the variation of crossover rate within a certain group of population provide better result than the standard GA.

Multi-objective transport network design with a reversible simulated annealing algorithm

Multi-objective transport network design with a reversible simulated annealing algorithm

Preface: operations research for transportation

Transportation is a broad multidisciplinary science concerned with the study of transportation network design, vehicle application engineering, as well as transportation operations and management. Due to the breadth of the topic and the increasing complexity of the decision processes involved in managing transportation operations, transportation has become a fertile field of study for operations research. Inspired by this, we have compiled this special issue of the Annals of Operations Research comprising of 24 new research contributions and two systematic literature reviews supporting and categorizing the current state of transportation research.

Subloop-based reversal of port rotation directions for container liner shipping network alteration

Container liner shipping network alteration is a practical manner of shipping network design, which aims to make minor modifications to ameliorate the existing network. In a generic liner shipping network with butterfly ports, each ship route is separated into a set of subloops on the basis of its structure and internal butterfly ports. Reversing the subloop directions has an impact on the network-wide cost including inventory cost, transshipment cost, and slot-purchasing cost. This paper proposes a new destination-based nonlinear model for the subloop-based reversal of port rotation directions with the objective of minimizing the overall network-wide cost. We prove that the addressed problem is NP-hard. Next, the model is transformed to an equivalent mixed-integer linear programming model. Based on the structure of the reformulated model, we develop a Benders decomposition (BD) algorithm and a metaheuristic method to solve practical-size instances. Three acceleration strategies are incorporated into the BD algorithm, which are adding Pareto-optimal cuts, updating big-M coefficients and generating combinatorial Benders cuts. Case studies based on three small examples and an Asia-Europe-Oceania liner shipping network with a total of 46 ports are conducted. Results show that the problem could be efficiently solved by the accelerated BD algorithm and the optimization of subloop directions is conducive to decreasing the network-wide cost especially the inventory cost.

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.

Adaptive traffic signal control with equilibrium constraints under stochastic demand

This study develops a methodology to model transportation network design with signal settings in the presence of demand uncertainty. It is assumed that the total travel demand consists of commuters and infrequent travellers. The commuter travel demand is deterministic, whereas the demand of infrequent travellers is stochastic. Variations in demand contribute to travel time uncertainty and affect commuters’ route choice behaviour. In this paper, we first introduce an equilibrium flow model that takes account of uncertain demand. A two-stage stochastic program is then proposed to formulate the network signal design under demand uncertainty. The optimal control policy derived under the two-stage stochastic program is able to (1) optimize the steady-state network performance in the long run, and (2) respond to short-term demand variations. In the first stage, a base signal control plan with a buffer against variability is introduced to control the equilibrium flow pattern and the resulting steady-state performance. In the second stage, after realizations of the random demand, recourse decisions of adaptive signal settings are determined to address the occasional demand overflows, so as to avoid transient congestion. The overall objective is to minimize the expected total travel time. To solve the two-stage stochastic program, a concept of service reliability associated with the control buffer is introduced. A reliability-based gradient projection algorithm is then developed. Numerical examples are performed to illustrate the properties of the proposed control method as well as its capability of optimizing steady-state performance while adaptively responding to changing traffic flows. Comparison results show that the proposed method exhibits advantages over the traditional mean-value approach in improving network expected total travel times.

5G-TRANSFORMER: Slicing and Orchestrating Transport Networks for Industry Verticals

This article dives into the design of the next generation mobile transport networks to simultaneously support the needs of various vertical industries with diverse range of networking and computing requirements. Network slicing has emerged as the most promising approach to address this challenge by enabling per-slice management of virtualized resources. We aim to bring the network slicing paradigm into mobile transport networks by provisioning and managing slices tailored to the needs of different vertical industries. Our technical approach is twofold: (i) enabling vertical industries to meet their service requirements within customized slices; and (ii) aggregating and federating transport networking and computing fabric, from the edge up to the core and cloud, to create and manage slices throughout a federated virtualized infrastructure. The main focus of the article is on major technical highlights of vertical-oriented slicing mechanisms for 5G mobile networks.

Walkability index across trip purposes

Development of non-motorized modes of transportation has recently become a common trait among urban planners toward sustainable transportation, and walkability has been introduced as one of the most interesting concepts in the past two decades. A number of researchers put efforts to develop walkability indices (WIs) to show the status of walkability for specific zones. However, further investigation has not been conducted to account for the effect of trip purposes on WI. The literature presented transportation network design (design), land use diversity (diversity) and population density (density) as main built environment criteria and previous studies have used these criteria to develop different indices to capture walkability. In this paper, the concept of WI is developed based on the zonal walk share. Therefore, a WI has been developed and then calibrated across three specific trip purposes (i.e., job, educational and shopping) in addition to all of the trips on 112 Traffic Analysis Zones (TAZs) of the city of Rasht, Iran. According to the result of this study, diversity was found to be the most prominent criterion with the WIs. Furthermore, WI was discovered more beneficial for describing the term of walk share in shopping trips than other trip purposes.

Liner shipping network design with emission control areas: A genetic algorithm-based approach

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO2 and SOx) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.

Integrated optimization of urban agglomeration passenger transport hub location and network design

Because of the influence of comprehensive hub construction on network flow, this paper adopts the bi-level programming model (BPM) to solve the problem about the integrated optimization of multi-hub location and network layout. The upper level model is designed to minimize the network operating cost, network transformation cost, and hub construction cost; the lower level is the user equilibrium distribution model at fixed demands. Besides, this paper designs the improved genetic algorithm to solve the bi-level programming model and validates the model validity and algorithm feasibility through the computing example. The model has a high application value for solving the problem of comprehensive optimization of intercity passenger transport hub location and intercity passenger transport network design in the process of urban agglomeration development.

Optimal Design of Transportation Networks with Automated Vehicle Links and Congestion Pricing

We propose a bi-level network design model comprising automated vehicle (AV) links and congestion pricing to improve traffic congestion. As upper-level road planners strive to minimize total travel-time costs by optimizing both the network design and the congestion pricing, lower-level travelers make choices about their routes to minimize their individual travel costs. Our proposed model integrates a network design and congestion pricing to improve traffic congestion and we use a relaxation-based method to solve the model. We conducted a series of numerical tests to analyze the proposed model and solution method. Our results indicate that network design is more effective than congestion pricing when the AV market penetration is high and the opposite is true when AV penetration is low. More importantly, we find that a network design of automated vehicle links with congestion pricing is superior to a single network design or congestion pricing, especially when both AVs and conventional vehicles have a relatively large market penetration.

Predictive Impact Analysis for Designing a Resilient Cellular Backhaul Network

Backhaul transport network design and optimization for cellular service providers involve a unique challenge stemming from the fact that an end-user's equipment (UE) is within the radio reach of multiple cellular towers: It is hard to evaluate the impact of the failure of the UE's primary serving tower on the UE, because the UE may simply switch to get service from other nearby cellular towers. To overcome this challenge, one needs to quantify the cellular service redundancy among the cellular towers riding on that transport circuit and their nearby cellular towers, which in turn requires a comprehensive understanding of the radio signal profile in the area of the impacted towers, the spatial distribution of UEs therein, and their expected workload (e.g., calls, data throughput). In this work, we develop a novel methodology for assessing the service impact of any hypothetical cellular tower outage scenario, and implement it in an operational system named Tower Outage Impact Predictor (TOIP). Our evaluations, using both synthetic data and historical real tower outages in a large operational cellular network, show conclusively that TOIP gives an accurate assessment of various tower outage scenarios, and can provide critical input data towards designing a reliable cellular backhaul transport network.

A polyhedral approach to the generalized minimum labeling spanning tree problem

The minimum labeling spanning tree problem (MLSTP) is a combinatorial optimization problem that consists in finding a spanning tree in a simple graph G, in which each edge has one label, by using a minimum number of labels. It is an NP-hard problem that was introduced by Chang and Leu (Inf Process Lett 63(5):277–282, 1997. https://doi.org/10.1016/S0020-0190(97)00127-0 ). Chen et al. (Comparison of heuristics for solving the gmlst problem, in: Raghavan, Golden, Wasil (eds) Telecommunications modeling, policy, and technology, Springer, Boston, pp 191–217, 2008) subsequently proposed a generalization of the MLSTP, called the generalized minimum labeling spanning tree problem (GMLSTP), that allows a situation in which multiple labels can be assigned to an edge. Here, we show how the GMLSTP can be expressed as an MLSTP in a multigraph. Both problems have applications in various areas such as computer network design, multimodal transportation network design, and data compression. We propose a new compact binary integer programming model to solve exactly the GMLSTP and analyze the polytope associated with the formulation. The paper introduces new concepts, gives the polytope dimension, and describes five new facet families. The polyhedral comparison results for the studied polytope show that the new model is theoretically superior to current state-of-the-art formulations.

Mixed-Vehicular Aggregated Transportation Network Design Considering En-route Recharge Service Provision for Electric Vehicles

This paper addresses the transportation network design problem (NDP) wherein the distance limit and en-route recharge of electric vehicles are taken into account. Specifically, in this work, the network design problem aims to select the optimal planning policy from a set of infrastructure design scenarios considering both road expansions and charging station allocations under a specified construction budget. The user-equilibrium mixed-vehicular traffic assignment problem with en-route recharge (MVTAP-ER) is formulated into a novel convex optimization model and extended to a newly developed bi-level program of the aggregated NDP integrating recharge facility allocation (NDP-RFA). In the algorithmic framework, a convex optimization technique and a tailored GA are adopted for, respectively, solving the subproblem MVTAP-ER and the primal problem NDP-RFA. Systematic experiments are conducted to test the efficacy of the proposed approaches. The results highlight the impacts of distance limits and budget levels on the project selection and evaluation, and the benefits of considering both road improvement policy and recharge service provision as compared to accounting for the latter only. The results also report that the two design objectives, to respectively minimize the total system travel time and vehicle miles travelled, are conflicting for certain scenarios.

Use of Bayesian inference method to model vehicular air pollution in local urban areas

Traffic Related Air Pollution (TRAP) studies are usually investigated using different categories such as air pollution exposure for health impacts, urban transportation network design to mitigate pollution, environmental impacts of pollution, etc. All of these subfields often rely on a robust air pollution model, which also necessitates an accurate prediction of future pollutants. As is widely accepted by the heath authorities, TRAP is considered to be the major health issue in urban areas, and it is difficult to keep pollution at harmless levels if the time sequenced dynamic pollution and traffic parameters are not identified and modelled efficiently. In our work here, artificial intelligence techniques, such as Bayesian Networks with an optimized configuration, are used to deliver a probabilistic traffic data analysis and predictive modelling for air pollution (SO2, NO2 and CO) at very local scale of an urban region with up to 85% accuracy. The main challenge for traditional data analysis is a lack of capability to reveal the hidden links between distant data attributes (e.g. pollution sources, dynamic traffic parameters, etc.), whereas some subtle effects of these parameters or events may play an important role in pollution on a long-term basis. This study focuses on the optimisation of Bayesian Networks to unveil hidden links and to increase the prediction accuracy of TRAP considering its further association with a predictive GIS system.

Optimizing express parcel networks and delivery schemes subject to timeliness tolerances

ABSTRACTThe ‘first come first served’ (FCFS) strategy usually adopted by air express companies does not consider delivery time tolerances and induces underuse of some facilities. This study relaxes the time tolerances and tackles the issue of parcel deliveries and transport network design for an express delivery company. In other words, it determines simultaneously the route loops of company-owned cargo aircraft, the amount of cargo space chartered for each link and the parcel delivery paths. Using a case study in which parcels must be delivered between 14 cities served by the SF Air Express Company, we find that FCFS is not the best approach for minimizing costs and that express companies can optimize both their delivery schemes and networks subject to some relaxed time tolerances.

Implications of link-based equity objectives on transportation network design problem

The objective of this study is to propose a novel definition of equity from the perspective of link performance with energy consumption and incorporate equity into the transportation network design problem (NDP). First, we introduce an aggregated equity measure and present the theoretical framework for the equity considering link travel time and energy consumption along with free flow traffic conditions. We demonstrate that a Braess paradox situation exists with regards to the proposed equity measure. Second, we formulate a bi-level modelling framework for the Link-based Equitable NDP ( $$LE$$ -NDP). The model is a multiobjective optimization program, where the upper level aims to minimize the total system travel time and optimize equity levels with respect to both travel time and energy consumption. The lower level then represents the flow response under user equilibrium conditions. To quantify the performance loss incurred relative to the equity criterion, we formulate the function of the price of fairness within the $$LE$$ -NDP. Third, to solve the $$LE$$ -NDP model, we develop a tailored heuristic solution method, which simulates the interaction between planners and travellers. The solution approach uses an $$\varepsilon$$ -constraint method to identify Pareto-efficient solutions, and constraint optimization formulations are presented to solve the resulting single-objective program. Finally, the efficacy of the model and the solution algorithm is validated via case studies on three traffic networks. The results demonstrate that the proposed modelling device is capable of achieving more balanced solutions when the equity metrics are accounted for, and the developed solution method is efficient as a reference method in practice. The results also show the trade-offs between travel time and link-based equity, and indicate that equity metrics in terms of different travel costs, i.e. travel time and energy consumption, are shown to be conflicting design objectives for certain scenarios.