Transportation Network Design Research Articles

Active Safety Planning Method for Transportation Network Design

Traffic accidents are frequent although various countermeasures are introduced. Traffic safety cannot be fundamentally improved if it is not considered in the transportation network design stage. Although it is well known that traffic safety is one of the most important concerns of the public, traffic safety is not adequately accommodated in transportation planning. This paper considers traffic safety as a major criterion in designing a transportation network. It is a kind of proactive measure rather than reactive measure. A bilevel programming model system is proposed where the upper level is the urban planners’ decision to minimize the estimated total number of traffic accidents, and the lower level is the travelers’ response behaviors to achieve transportation system equilibrium. A genetic algorithm (GA) with elite strategy is proposed to solve the bilevel model. The method of successive averages (MSA) is embedded for the lower level model, which is a feedback procedure between destination choice and traffic assignment. To demonstrate the effectiveness of the proposed method and algorithm, an experimental study is carried out. The results show that these methods can be a valuable tool to design a safer transportation network although efficiency, in terms of system total travel time, is slightly sacrificed.

Urban form and economic sustainability in housing projects

Urban areas in metropolitan cities like Cairo suffer from economic, social, and environmental predicaments. Urban economic sustainability is an approach that reforms the urban performance to gain direct benefits such as minimizing costs and maximizing profits and indirect benefits as better social, environmental, and cultural aspects. This research suggests applying such an approach to enhance Egyptian housing projects. The main research question is how to evaluate the economic sustainability of urban forms?. The study presents a “Sustainable Urban Economy model” (SUE model) linking urban fabric, land use pattern, transportation, and street network design with economic sustainability. Research methods and tools include interviews (Delphi method) with 25 urban planning/design and urban economic experts to refine the model. Results show the most effective components of the urban form on economic sustainability (accessibility and degree of permeability, population density, built, and the impact of sub-indicators on the main components. Moreover, results indicate that the seven most influential indicators are the built-up to total space ratio, mixed-use ratio, built-up ratio, population density, floor area ratio, degree of accessibility, and public transportation. Experts suggested values for the seven indicators to measure how the urban form can achieve high economic, environmental, and social performance in the Egyptian context.

Designing reduced congestion road networks via an elitist adaptive chemical reaction optimization

We propose a new approach for solving a variant of the transportation Network Design Problem (NDP) where network congestion is reduced via traffic pattern redesign. The investigated NDP selects a set of roads to be converted from two-way traffic to one-way traffic. This allows increased traffic flow in the selected roads at the cost of restricting flow in the opposing direction roads. An optimal set of converted roads can result in significant congestion reduction for the network without the high construction costs associated with network capacity expansion. The new approach builds on the recently developed Chemical Reaction Optimization (CRO) metaheuristic and leverages Markov chain traffic assignment to predict traffic flow changes in response to network modifications. We propose an elitist and adaptive version of the CRO metaheuristic allowing it to more efficiently search for optimal solutions. We deploy the proposed approach to the cities of Abu Dhabi and Sioux Falls and report on our results. We also compare the Elitist Adaptive CRO (EACRO) results to those of Genetic Algorithm (GA) to demonstrate its performance. Compared to GA, the proposed approach reduces network congestions faster using fewer objective function evaluations. It also produces lower network congestion designs using long optimization runs. However, we find the algorithm to be sensitive to parameter calibration and dependent on the use of elitism for good performance. We explore the internal dynamics of the proposed algorithm and investigate its search effectiveness to understand its superior performance when compared to GA.

A transportation network evolution model based on link prediction

An effective and reliable evolution model can provide strong support for the planning and design of transportation networks. As a network evolution mechanism, link prediction plays an important role in the expansion of transportation networks. Most of the previous algorithms mainly took node degree or common neighbors into account in calculating link probability between two nodes, and the structure characteristics which can enhance global network efficiency are rarely considered. To address these issues, we propose a new evolution mechanism of transportation networks from the aspect of link prediction. Specifically, node degree, distance, path, expected network structure, relevance, population and GDP are comprehensively considered according to the characteristics and requirements of the transportation networks. Numerical experiments are done with China’s high-speed railway network, China’s highway network and China’s inland civil aviation network. We compare receiver operating characteristic curve and network efficiency in different models and explore the degree and hubs of networks generated by the proposed model. The results show that the proposed model has better prediction performance and can effectively optimize the network structure compared with other baseline link prediction methods.

A hybrid greedy randomized heuristic for designing uncertain transport network layout

The foundations of efficient management are laid on transport networks in various scientific and industrial fields. Nonetheless, establishing an optimum transport network design (TND) is complicated due to uncertainty in the operating environment. As a result, an uncertain network may be a more realistic representation of an actual transport network. The present study deals with an uncertain TND problem in which uncertain programming and the greedy randomized adaptive search procedure (GRASP) are used to develop an original optimization framework and propose a solution technique for obtaining cost-efficient designs. To this end, we originally develop the concept of α-shortest cycle (α-SC) employing the pessimistic value criterion, given a user-defined predesignated confidence level α. Employing this concept and the operational law of uncertain programming, a new auxiliary chance-constrained programming model is established for the uncertain TND problem, and we prove the existence of an equivalence relation between TNDs in an uncertain network and those in an auxiliary deterministic network. Specifically, we articulate how to obtain the uncertainty distribution of the overall optimal uncertain network’s design cost. After all, the effectiveness and practical performance of the heuristic and optimization model is illustrated by adopting samples with different topology from a case study to show how our approach work in realistic networks and to highlight some of the heuristic’s features.

Hub Location, Routing, and Route Dimensioning: Strategic and Tactical Intermodal Transportation Hub Network Design

We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights.

Ateş Böceği Algoritması Kullanılarak Toplu Taşıma Frekans Optimizasyonu ve Parametrelerinin Değerlendirilmesi

ABSTRACT: Over the last few decades, rapidly growing cities in terms of population and land use have led to many transportation-based problems such as longer travel times, traffic congestion, traffic crashes, and air and noise pollution. Increasing the modal share of transit systems appears to be one of the most effective methods to solve transportation-based problems. However, transit systems, particularly in countries having limited resources, should be used efficiently to achieve sustainable urban mobility. Even only adjusting frequencies of transit lines, with no infrastructure investment cost requirements, can provide a more efficient transit system. In this paper, a transit frequency setting model based on the Firefly Algorithm (FA), which is a relatively new metaheuristic, for the transportation network design problems is presented to minimize total user cost under a fleet size constraint. The proposed model is performed on a 10-route Mandl’s Test Network using different combinations of parameters to demonstrate the effect of parameter values on the solution quality. After that, the best solution of 30 solutions obtained by the calibrated parameter values is compared to the existing frequency set of the 10-route transit network. The results show that the FA can obtain better frequency sets by selecting the proper values for the parameters.

Evolution of multimodal final user equilibrium considering public transport network design history

Analysing the properties of a network equilibrium can help to have a better view about network state, robustness, and the effect of any variation in the network. This study investigated the impacts of network design history on day-to-day multimodal user equilibrium. In particular, we analyze the long-term evolution of the network, including opening new multimodal options and its impacts on the final network equilibrium. First, the analysis focuses on static network loading with different successive configurations. Then, a more realistic setting is studied by simulation. A large-scale multimodal network with the flexible opening over time of three possible transport facilities shows that the final equilibrium is not unique; more importantly, significant differences can be observed in public transportation occupancy, while user equilibrium is enforced in all situations. Some solutions prove to be better from the collective viewpoint (shorter total travel time), thus giving new insight into public transport planning.

A branch-and-price algorithm for solving the single-hub feeder network design problem

In liner shipping, containers are generally transshipped in major hub ports in each region, between larger inter-region vessels and smaller feeder vessels. In this paper, we study the problem of designing the feeder vessel network, transporting containers between a regional hub and the surrounding feeder ports. The problem, as modelled, has many similarities with the split delivery vehicle routing problem, but with additional characteristics such as simultaneous pickups and deliveries and weekly departures. The problem also includes fleet sizing with a heterogeneous fleet and allows demand rejection at a penalty cost. We present a branch-and-price framework to solve the problem, where the subproblem is solved by enumerating vessel routes and subsequently assigning commodities by solving a min-cost flow problem for each commodity. The algorithm is tested on instances with up to 12 ports, which are all solved to optimality. Since the problem is similar to the liner shipping network design problem but without transshipments, we further study selected instances from the LINER-LIB instance suite. The Baltic instance is solved to proven optimality and we find the best known solution to the West Africa instance, significantly improving on what can be found in the literature.

Optimal Urban Transit Investment Model and Its Application

We present a new urban transit investment model, integrating transport economic theory regarding optimal investment with transport modeling, planning, and network design. The model expands on the theory of optimal transit network planning and investment, accounting for the effects of the investment on accessibility, level of service, and speed. The model seeks long-term optimal transit investment and optimal road pricing simultaneously in an integrated, unified model. To illustrate the advantages of our approach, we applied our empirical model to two case studies, Tel Aviv and Toronto, integrating our theoretical contribution into practice. Our results demonstrate the model’s ability to indicate the optimal transit mode and investment on a corridor level and the total investment required for the city transit network. The model results were compared to the actual and planned transit networks of Tel Aviv and Toronto and showed the model’s capability to produce a good balance of strategic design and network details. The research concludes that applying the right toll with the applicable transit investment is crucial for obtaining an efficient network and performance. This research can direct planners and policymakers in planning urban transport and provide a comprehensive set of guidelines for optimizing the simultaneous investment in mass transit and the congestion toll toward more sustainable cities and transportation systems.

Public Transportation Network Design and Frequency Setting: Pareto Optimality through Alternating-Objective Genetic Algorithms

The transportation network design and frequency setting problem concerns the optimization of transportation systems comprising fleets of vehicles serving a set amount of passengers on a predetermined network (e.g., public transport systems). It has been a persistent focus of the transportation planning community while, its NP-hard nature continues to present obstacles in designing efficient, all-encompassing solutions. In this paper, we present a new approach based on an alternating-objective genetic algorithm that aims to find Pareto optimality between user and operator costs. Extensive computational experiments are performed on Mandl’s benchmark test and prove that the results generated by our algorithm are 5–6% improved in comparison to previously published results for Pareto optimality objectives both in regard to user and operator costs. At the same time, the methods presented are computationally inexpensive and easily run on office equipment, thus minimizing the need for expensive server infrastructure and costs. Additionally, we identify a wide variance in the way that similar computational results are reported and, propose a novel way of reporting benchmark results that facilitates comparisons between methods and enables a taxonomy of heuristic approaches to be created. Thus, this paper aims to provide an efficient, easily applicable method for finding Pareto optimality in transportation networks while highlighting specific limitations of existing research both in regards to the methods used and the way they are communicated.

Hub network design for hazardous-materials transportation under uncertainty

This paper considers a hazardous materials (hazmat) transportation network design problem based on hub location topology under uncertainty. The control variables include decisions regarding location of hubs and hazmat response teams, along with the routing schemes. The model aims to minimize the total risk in the network. The measure of risk incorporates average response time to hazmat incidents as well as waiting time at hubs. To tackle the uncertainty of the problem, an efficient interactive approach using mean-absolute deviation and possibilistic programming is developed. Two heuristic algorithms based on lower bound procedure and rolling horizon scheme are presented to solve the model in large scale instances. The advantages of the presented methodologies are demonstrated through numerical experiments based on transportation networks from Sioux Falls, SD, and Western New York. We investigate different design schemes and discuss optimal decisions under different configurations.

Liner shipping network - transaction mechanism joint design model considering carbon tax and liner alliance

As a typical system to reduce costs and improve efficiency, the liner shipping system has an important role in economic globalization. In recent years, with the government’s strengthening of carbon emission control and the popularity of liner alliances, the decision-making problems faced by liner companies have become more complicated. Motivated by this setting, this paper investigates a liner shipping network - transaction mechanism joint design problem (LSTD). The government’s levy of carbon taxes on liner companies and liner alliance cooperation strategies are considered. To solve the abovementioned problem, a biobjective optimization model is established. The model takes the total profit of the alliance and the fairness of the distribution of alliance profit as two objectives and optimizes the alliance liner shipping network design scheme (i.e., composition of the fleet, calling sequence of the ports on the routes, and sailing speed of ships) and the alliance transaction mechanism (i.e., slot transaction price among members). To solve the model, the biobjectives are set with different priorities to transform the model into a two-stage model. A genetic algorithm is designed and embedded into the inverse optimization model. Numerical experiments are performed based on China’s actual import/export transportation data. Two sensitivity analyses for different freight rates and carbon tax rates are conducted. The experimental results show that the proposed model and algorithm can effectively solve the LSTD and provide necessary decision support for the operation of the liner alliance.

Shipping network design–infrastructure investment joint optimization model: a case study of West Africa

ABSTRACT Since the proposal of the Belt and Road initiative in March 2015, the Chinese government has continued to increase its investment in transportation infrastructure construction in West African countries. To realize the scientific decision-making process for large-scale investment, this paper establishes a shipping network design–infrastructure investment joint optimization model. The abovementioned model aims to find the minimum generalized transportation cost in the study area and jointly optimizes the construction scheme of the regional inland transportation network (i.e., railway network), the expansion scheme of the port (i.e., container port) throughput capacity, and the operation scheme of the shipping network (i.e., container shipping network) given a limited budget. Based on the framework of the active set algorithm, an algorithm that can explore the local optimal solution of the abovementioned model is designed. Taking China–West Africa transportation as the research object, numerical experiments are carried out to verify the effectiveness of the model and algorithm. Two types of sensitivity analyses for different investment scales and origin-destination demand are designed. The results show that the proposed model and algorithm can effectively solve the abovementioned problem and can provide decision-making support for the Chinese government in formulating reasonable investment schemes for West Africa. This paper is a revised and expanded version of a paper entitled, ‘Shipping network design–infrastructure investment joint optimization model’ presented at the 3rd International Conference of the Yangtze River Research and Innovation Belt, Ningbo, China; 5–7 November 2020.

A Continuous Transportation Network Design Problem with the Consideration of Road Congestion Charging

This paper proposes a biobjective continuous transportation network design problem concerning road congestion charging with the consideration of speed limit. The efficiency of the traffic network and the reduction of pollution in the network environment are improved by designing a reasonable road capacity enhancement and speed limit strategy. A biobjective bilevel programming model is developed to formulate the proposed network design problem. The first target of the upper problem is the optimization of road charging efficiency, and the other target is the total cost of vehicle emissions; these objectives are required to devise the optimal road capacity enhancement scheme, speed limiting schemes for different time periods, and the road pricing scheme. The lower-level problem involving travellers’ route choice behaviours uses stochastic user equilibrium (SUE) theory. Based on the nondominated sorting genetic algorithm, which is applied to solve the bilevel programming model, a numerical example is developed to illustrate the effectiveness of the proposed model and algorithm. The results show that the implementation of congestion charging measures on the congested road sections would help to alleviate road congestion in the transportation network, effectively save transportation infrastructure investment and limited urban land resources, increase fiscal revenue, and open up new sources of funds for urban infrastructure construction.

Coastal transportation system green policy design model based on shipping network design

ABSTRACT As governments of various countries attach importance to green transportation, governments have focused their attention to understanding how they can shift the cargo shipping in coastal areas from land to water. Motivated by this, this paper establishes a green policy design model for coastal transportation systems. The model aims to minimise the comprehensive cost (i.e. the sum of the transportation cost and carbon emission tax) in the research area and optimise the government's shipping network design scheme and waterway freight rate control scheme under the established subsidy level. An active set algorithm is designed that can explore the local optimal solution of the model. Taking the Bohai Rim region of China as the research area, sensitivity analyses of different subsidy scales and origin-destination demand scales are carried out. The results show that the government's policy schemes will lead to a significant shift in the mode of transportation used for cargo.

Integrated Planning Model of Land-Use Layout and Transportation Network Design for Regional Urbanization in China Based on TOD Theory

With the acceleration of urbanization and the development of urban rail transit (URT) in China, an increasing number of metropolises have begun to apply transit-oriented development (TOD) theory in URT planning. However, spatial mismatches between residences and jobs, unbalanced traffic infrastructure utilization, and environmental pollution persist. Moreover, studies of regional-scale TOD theory are lacking in China. In this paper, an integrated model based on TOD theory is presented that optimizes land use and transportation simultaneously for a region covered by URT in China under New-type urbanization, aiming to achieve sustainable development. A max–min ant system (MMAS) algorithm integrated with the Frank–Wolfe and Dial algorithms is proposed. Jianzhou New Town is taken as a case study to validate the effectiveness of the model and algorithm. The superiority of the scheme of this integrated model is shown through comparative analysis. This research provides a new concept for governments and planners about the integrated planning of urbanization and transportation. In addition, this research extends TOD theory to employ an improved multi-objective planning model.

A public transport network design using a hidden Markov model and an optimization algorithm

Transportation Network Design Problem (TNDP) includes making the right choices possible when deciding a collection of design criteria to develop a current transportation network in response to rising traffic demand. Traffic congestion, higher maintenance and fuel prices, delays, accidents, and air emissions stem from the general rise in flow volume. Because of the NP-hard nature of this problem, a hidden Markov model and an Equilibrium Optimizer (EO) are employed in this paper to solve it. Each particle (solution) behaves as a search agent in EO, with its position. To reach the equilibrium condition, the search agents change their focus at random regarding the best-so-far approaches, including equilibrium candidates. A well-defined "generation rate" concept has been shown to elevate EO's capacity in avoiding local minima. This article provides a new method to lower the feasible travel time and the public travel cost using the hidden Markov model and EO algorithm. The suggested method's performance was compared to the performance of other algorithms on a test network. The related numerical outcomes show that it is more effective.

A Simulated Annealing Algorithm for Intermodal Transportation on Incomplete Networks

Growing competition in the world enforces the need for an efficient design of transportation networks. Furthermore, a competitive transportation network should also be eco-friendly. As road transportation is responsible for the largest quantities of CO2 emissions, Intermodal Transportation (IT) might be a potential alternative. From this perspective, intermodal terminals location is a cornerstone for building a sustainable transportation network. The purpose of this paper is to study and efficiently solve the Intermodal Terminal Location Problem on incomplete networks. We model this problem as a mixed integer linear program and develop a simulated annealing algorithm to tackle medium and large instances. The computational results show that the obtained solutions using simulated annealing are competitive and close to the exact solutions found by CPLEX solver for small and medium instances. The same developed algorithm outperforms the best found solutions from the literature using heuristics for larger instances.

Responsive transport network design: minimal investment for desired travel time reduction

ABSTRACT It is crucial for policymakers to satisfy the expectation of network users in practice. It is a so-called responsive transport network design problem. It is curious that this important problem is rarely explored. This paper aims to propose a method to solve this problem. A novel bi-level programming model is formulated where the upper level is to minimize investment cost with desired travel time reduction target, while the lower level is transport system equilibrium that is a closed-loop feedback process between destination choice and traffic assignment. It is well known that the solution of bi-level programming model is challenging. A trial and error algorithm is designed to solve the proposed model. Finally, a computational experiment is carried out to demonstrate the effectiveness and operability of the proposed method and algorithm. Results show that they are useful tools for investment decision making on target-oriented transport network design.