Transportation Network Design Research Articles

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

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

A Simplified Framework for the Equity-Based Spatial Assessment of Alternative Public Transport Networks

Nowadays, approximately 75% of the European population lives in urban areas, and these figures are expected to grow in future. The consequent expansion of cities means that the population might locate its residence far from daily facilities, generating a so-called transport social need. The possibility to easily reach education, health, recreation, and job opportunities is a key factor in fostering and guaranteeing the social inclusion of people in society. Spatial planning plays a fundamental role in filling the gap between people residing in the center and those living in the suburbs. In particular, public transport planning should ensure access to essential services, providing at least the same opportunities as private vehicles. In this context, this study presents a simplified quantitative method to consider equity in transit network design and assessment, considering horizontal and vertical indicators based on the socio-demographic characteristics of a population and the accessibility to main opportunities. The validity of the indicators is tested by applying them to a case study located in the Apulia region (Italy). The approach could be considered a basis for more complex transport-network design optimization oriented to social inclusion in urban areas.

Optimizing inland container logistics through dry ports: A two-stage stochastic mixed-integer programming approach considering volume discounts and consolidation in rail transport

It is essential for container shipping companies to plan for the optimum number and locations of dry ports as well as efficient inland container logistics operations to reduce costs and environmental impacts while improving customer service. The plan for optimum inland container transportation network design must account for demand uncertainty to eliminate financial difficulty due to redundant investment. In this regard, this study proposed a two-stage stochastic mixed-integer programming model for optimizing inland container logistics through dry ports. The model contributes to the state of the art in current research by including a piecewise-linear cost function for railway transportation to account for volume discounts, integrating full container scheduling with transport mode selection and empty container relocation for consolidation, and reflecting the fact that the amount of import and export full containers transported between customers (consignees and consignors) and seaports are exogenously decided. The solution results demonstrated the definite performance superiority of the progressive hedging algorithm over the extensive form solution. Additionally, the value of stochastic solution calculation showed that the application of stochastic solution might result in significant cost savings compared to the application of mean value deterministic solution. The proposed model can be applied for practical necessities to design robust optimum inland container logistics operations using intermodal rail transport with the progressive hedging algorithm as an efficient solution approach.

Mitigating Sustainability Risks in a Road Hazardous Materials Transport Network

Many materials which are important for a sustainable society are hazardous materials (hazmat), and there is the risk of potential threats with undesirable impacts on the sustainability of transport systems. The goal of this study is the mitigation of sustainability risks associated with hazmat transport processes. Moreover, it is necessary to design reliable road hazmat transport networks based on risk mitigation. This research combines route planning of hazmat shipments with location for emergency response centers in a road hazmat transport network. In addition, a new subsidy policy is also proposed. Hazmat carriers are able to choose routes for shipments in accordance with minimum transport costs. As a game scenario, a bi-level programming model is established to describe the problem. A genetic algorithm is developed to solve the problem, and computational experiments are made to testify the effectiveness of the proposed method. The results show the reasonableness of the model and the effectiveness of the algorithm, calibrating the influence of the parameters and the optimization characteristics of certain practical scenarios. Overall, the purpose of this paper is to develop a complete optimization approach for the design of road hazmat transport networks with sustainable risk as the key innovation.

An Urban Bus Network Generation Algorithm Based on Particle Swarm Optimization and Force Field Properties

Due to continuous urban sprawl, large-scale bus network design has become a major challenge in urban transport planning. The continuous increase in urban population and scale makes the factors considered in the urban route network design increasingly complex. Contemporary public transportation network design problems are based more on efficiency goals such as the accessibility and comfort of the transportation network, which increases the difficulty of analyzing the problem. Bus network design is not only an NP-hard (nondeterministic polynomial) problem but also a multivariable and multiobjective problem. This paper focuses on the bivariate and multiobjective bus network design problem of route generation and station selection. This paper proposes an algorithm called the Pseudo Force Field. By combining the idea of Particle Swarm Optimization (PSO) and the properties of the force field, a feasible route generation scheme is provided for the design of the bus network. The algorithm does not need to determine the end station and has a high degree of completion of the demand. This solves the problem of the selection of terminal stations in large-scale road network design. On this basis, the article combines Genetic Algorithm (GA) and Pareto frontier to provide a new route optimization algorithm and proves the effectiveness of the algorithm. The model has achieved theoretical results in the design of the bus route network in the megacity of Shenzhen, China.

Resilience assessment of tunnels: Framework and application for tunnels in alluvial deposits exposed to seismic hazard

Transport infrastructure is the backbone of the economy and society, while at the same time is exposed to multiple hazards. Previous natural disasters, including earthquakes, had a significant impact on transport networks with severe consequences for the users and supply chain. In this context, the resilience assessment of critical assets such as tunnels is of paramount importance for increasing safety and maintaining their functionality in seismic-prone areas. This study presents a practical resilience assessment framework for tunnels subjected to earthquakes. The proposed framework combines fragility and restoration functions, for assessing the robustness of tunnels exposed to different seismic scenarios, and the rapidity of the recovery considering different damage levels. The framework is applied to circular tunnels in alluvial deposits. A life-cycle resilience index is estimated, and the effects of soil conditions, tunnel burial depths, construction quality, and aging of the tunnel lining, on the resilience quantifications are examined and assessed. This effort contributes to the resilience-based design and management of tunnels and underground transport networks, and hence, facilitates decision-making and efficient allocation of resources by consultants, operators, and stakeholders.

Simulation-based optimization of large-scale dedicated bus lanes allocation: Using efficient machine learning models as surrogates

Dedicated Bus Lanes (DBLs) have been implemented in many cities to boost buses’ reliability and to alleviate traffic congestions. However, how to correctly allocate DBLs across a large-scale real-world network is challenging. Simulation-Based Optimization (SBO) methods were utilized in this work to resolve this optimal allocation problem. Traditional discrete SBO methods are intractable when handling high-dimensional, costly, simulation-based Transportation Network Design Problems (TNDPs) using a limited computational budget. Thus, several efficient Machine Learning (ML)-based surrogate models and a Jackknife uncertainty estimator were introduced to existing SBO framework in this work. A number of comparative experiments between proposed methods and frequently-used Gaussian Process (GP)-SBO methods were conducted. A mesoscopic simulation and Dynamic Traffic Assignment (DTA) tool was adopted to evaluate the network performance.The results of numerical studies show that the optimization efficiency of proposed method is significantly higher than that of commonly used GP-based method when dealing with high dimensional problems. A real-world DBLs allocation case study in Guiyang, Guizhou Province, China again proves that efficient ML-based SBO method is capable to take much less CPU runtime to obtain a better solution than traditional method. The optimal DBLs allocation scheme found by one of the efficient ML-based methods raises the network performance by 5.05 %. A total of 1,376 h travel time is saved, and the average travel time per traveler drops by 0.75 min. In conclusion, efficient ML-based SBO method proposed in this study is more promising to handle large-scale, discrete, costly simulation-based DBLs allocation problems within a limited computational budget than common GP-based SBO methods.

Advancing avian road ecology research through systematic review

Linear Transport Infrastructure (LTI) often has important impacts on wildlife and landscape processes. Two reviews provided early insight and nuance into LTI impacts on birdlife but were published when road ecology experiments were still being developed. Four factors were identified to impact birds near roads: habitat quality, species-specific traits, traffic noise, and infrastructure. Although early work identified traffic noise as the main selective force, recent studies lend more support to habitat quality and infrastructure. However, this literature was deemed to possess low inferential strength given inconsistent data collection, inadequate management of confounding variables, limited inclusion of vehicle-free environments, short-term experimental timeframes, and use of methodologies susceptible to bias. A new experimental framework for better evaluation of the impact of roads on birdlife is proposed. This would facilitate the construction of species and/or community profiles useful in the design, construction, and management of transport networks.

Optimization of New Energy Public Transportation Network Based on Ant Colony Algorithm and Low-Carbon Concept

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

Emerging trends and influential outsiders of transportation science

ABSTRACT Fifty years of evolution of transportation research is revisited based on bibliometric indicators of nearly 50,000 articles, the collective publication of all transportation journals. A multitude of objective indicators all consistently determined four major divisions in the field: (i) network analysis and traffic flow, (ii) economics of transportation and logistics, (iii) travel behaviour, and (iv) road safety. Trending themes of research within the abovementioned divisions respectively are: (i) macroscopic fundamental diagram and public transport network design, (ii) nil (no distinct trending topic), (iii) land-use, active transportation, residential self-selection, travel experience/satisfaction, social exclusion and transport/spatial equity, and (iv) statistical modelling of road accidents. Furthermore, clusters of research related to topics of (a) shared mobility, (b) electric mobility, and (c) autonomous mobility constitute trending topics that are each a cross between multiple divisions of the field. These outcomes document major directions to which the transportation research is headed. Additional outcome is determination of influential outsiders, seminal articles published by non-transportation journals that have proven instrumental in the development of transportation science.

A pareto-based multi-objective network design approach for mitigating the risk of hazardous materials transportation

Hazardous materials (HazMat) transportation is one of the indispensable links in the chemical process industry and is closely related to safety, security, and environmental concerns. HazMat transportation routing is a major proactive risk management measure. Most related studies focus on diminishing the transportation risk for one single HazMat shipment. This work addresses a global perspective on the problem where the correlations of multiple simultaneous HazMat shipments are considered. A multi-objective transportation network design model is defined to optimize the objectives of the transportation risk and the transportation cost. A well-designed Pareto-based hybrid heuristic algorithm is proposed for model solving, which integrates a linear decomposition procedure for initial population construction, a Pareto-based scatter search procedure for population evolution, and a variable neighborhood search procedure for solution improvement. Computational results indicate that the proposed algorithm is competitive in solving large-scale instances since it is able to strike a great balance between effectiveness and efficiency. The detailed solution analysis shows that the proposed approach can effectively coordinate multiple simultaneous HazMat transportation processes. Moreover, we find that performing multiple short-distance shipments would significantly reduce the total transportation risk, but results in the higher transportation cost. This work can provide a set of Pareto optimal transportation networks for different industrial application scenarios to achieve the trade-off between economic and safety.

Design of heterogeneous flexible-route public transportation networks under low demand

This paper presents design methods for a flexible-route transit system, in which vehicles travel within predetermined areas to provide door-to-door service. The main advantage of this system is that passengers no longer have to access transit stations in order to gain service. This system is suitable for low and heterogeneous passenger demand distribution as it features a hybrid system layout that includes both a hub-and-spoke network in the peripheral region and a grid network in the central region, along with heterogeneous local routes that address local demand variations. Continuum approximation (C.A.) is used to reduce the computation burden by formulating the design problem with respect to a few decision variables. We compare the performance of the proposed transit system with (i) the typical fixed-route system, (ii) the homogeneous flexible-route grid system, and (iii) the flexible-route grid system with local routes, in hypothetical settings. It is found through our numerical examples that the integration of the three proposed features (i.e. flexible transit, local tubes, and hybrid structure), as compared to counterparts with only two or fewer features, yields lower combined agency and user costs under the assumed low heterogeneous demand distribution. We then apply the design framework to a more realistic case for the City of Changzhi, China. An implementable design for Changzhi is developed, and its performance verified with simulations, demonstrating accuracy and applicability of the proposed continuum approximation model.

Designing sustainable intermodal freight transportation networks using a controlled rail tariff discounting policy – The Iranian case

• A sustainable Intermodal Freight Transportation Network is developed. • The government determines a controlled rail tariff discount for rail companies and compensates the money that they lose due to offering the rail tariff discount by the money that will be collected from fuel saving in road freight transportation system. • The rail tariff discount is customized to the network’s OD flows and rail connections. • Multiple users’ route choice behaviors is modeled by applying a stated preference method. • A real case is studied and some insights are presented. Moving freight by intermodal services instead of road services not only saves a large amount of fuel but also improves sustainability. This paper presents a sustainable Intermodal Freight Transportation Network Design Problem (IFTNDP) in which the effectiveness of a new short-term policy, called controlled rail tariff discounting policy, is evaluated. In the IFTNDP, the government locates intermodal hubs and cooperates with rail companies to offer discounted rail tariffs on the rail connections of intermodal routes. Rail companies are allowed to customize the amount of rail tariff discounting for the network’s OD flows and rail connections. In addition, the government compensates the losses that rail companies incur due to offering the rail tariff discounts by the amount that will be collected from fuel savings in the freight transportation system. Hence, the government can boost the utility of intermodal transportation without investing funds to compensate the losses of rail companies. A stated preference model based on a questionnaire is adapted to identify users’ route choice behaviour. Then, the users’ route choice behavior is incorporated into the IFTNDP by a set of constraints, depending on the road and intermodal travelling times and costs. The model is applied to a real freight network and insights made by undertaking sensitivity analyses. The results demonstrate that the controlled rail tariff discounting policy not only increases the contribution of freight intermodal transportation in moving total freight but also enhances government’s profits from fuel savings after compensating rail companies’ losses.

Active Transport Network Design Based on Transit-Oriented Development and Complete Street Approach: Finding the Potential in Qazvin

Today, automobile dependency constantly causes traffic congestion, delays, reduced access, increased fuel and energy consumption, and environmental emissions. Automobile dependency has caused many direct and indirect transportation problems that may influence our life. Urban planners and transportation engineers seek to improve transport networks considering social issues. One of the most successful solutions for advocating sustainable transport is transit-oriented development (TOD). Another solution that planners encourage to use is designing the roadways based on a complete street approach, which is a system that provides safe, convenient, and comfortable travel and increases accessibility for users of all ages regardless of their transport modes. The present study employed the saturated roads that have heavy traffic most of the time and public transport e-ticket data to investigate the potential complete streets in Qazvin. An online questionnaire was developed using the Analytic Hierarchy Process (AHP) method based on the TOD and the complete street framework to investigate the essential criteria for redesigning the network based on the active transport approach. Thus, after analyzing the six criteria (density, diversity, distance, accessibility, demand management, and design) and eight sub-criteria (pedestrian flow, pedestrian density, connectivity, safety, bike route, bus route, road width, and urban tree canopy index), the proposed active transport network emerged and results show that five high priority streets were identified to be considered as a solution.

Resilient Transportation Network Design under Uncertain Link Capacity Using a Trilevel Optimization Model

This study addresses uncertainty in a transportation network by proposing a trilevel optimization model, which improves resiliency against uncertain disruptions. The goal is to minimize the total travel time by designing a resilient transportation network under uncertain disruptions and deterministic origin-destination demands. The trilevel optimization model has three levels. The lower level determines the network flow, and the middle level assesses the network’s resiliency by identifying the worst-case scenario disruptions that could lead to maximal travel time. The upper-level takes the system perspective to expand the existing transportation network to enhance resiliency. We also propose a formulation for the network flow problem to significantly reduce the number of variables and constraints. Two algorithms have been developed to solve the trilevel model. The results of solving the highway network in Iowa show that the trilevel optimization model improves the total travel time by an average of 41%.

Artificial leaf-vein network optimisation algorithm for urban transportation network design

Artificial leaf-vein network optimisation algorithm for urban transportation network design

Artificial leaf-vein network optimisation algorithm for urban transportation network design

Designing a rational urban transportation network has become one of the hottest topics in the transportation field. Many heuristic algorithms, based on biological laws or swarm intelligence, have been proposed in recent years. However, rare researches paid attention to the perfect topology of the leaf-vein network in nature. In this paper, a definitely new heuristic algorithm is proposed based on the leaf-vein network. By analysing the growth process of leaf veins, the underlying relationship between urban transportation network and leaf-vein network is first investigated. An evolutionary mechanism of the algorithm or called the artificial leaf-vein generation rule, is built by simulating the natural selection of biological evolution and genetic transmission. Given the differences between urban transportation networks and leaf-vein networks, a transformation method between the two networks is also designed. Finally, a benchmark instance, the Sioux Falls network, is set up to demonstrate the performance of the proposed algorithm.

Quantum Computing for Transport Network Design Problems

Quantum Computing for Transport Network Design Problems

OPTIMIZING BUS LINES USING GENETIC ALGORITHM FOR PUBLIC TRANSPORTATION

Abstract. Due to increasing human population, the need for quality public transportation has also increased. This study takes stop density, stop layout, and passenger population of those stops into consideration to offer a better regulated public transportation network design that can satisfy the increased demand. In this study, the boarding data is provided by the public transportation department of the city of Antalya, Turkey. Remaining required data was automatically generated using web services and stored in a PostgreSQL database hosted on a cloud server. After visualizing inputs such as bus routes, stop layout, and passenger density on Google Maps and KeplerGL, with the use of the K-Means algorithm, data was clustered to find ”hot” (i.e. attraction) areas on a macro scale. A novel means of connecting hot spots suggested by the outcome of the Genetic Algorithm was developed. To compare the effectiveness of the proposed approach with the existing network, current bus stops were mapped to the new domain. It was observed that a more efficient system was achieved by higher route efficiency and lower transfer counts.

Container ocean shipping network design considering carbon tax and choice inertia of cargo owners

The quality of the container ocean shipping network design directly affects the revenue of liner companies. In this paper, a container ocean shipping network design problem (COND) is investigated. Considering that the governments of many countries have proposed carbon-neutral initiatives, coupled with the constant fluctuations in the shipping market, the carbon tax and cargo owners’ choice inertia factors are taken into consideration simultaneously. To model the COND, a bi-level programming model is developed to jointly optimize the liner type, calling sequence, calling frequency, and sailing speed. The upper model aims to choose the network design scheme that maximizes the liner company’s revenue. The lower model optimizes the freight scheme of the liner company to evaluate the network design scheme selected by the upper model. To solve the abovementioned model, a genetic algorithm is designed, and a real-world trade shipping route between Asia and Western Europe is introduced to perform numerical experiments. Sensitivity analyses are conducted for different carbon tax rates and transportation demands. The experimental results show that the abovementioned model and algorithm can effectively address the COND and provide certain useful managerial insights for the liner company’s daily operation.