Transportation Network Optimization Research Articles

Invulnerability of the Urban Agglomeration Integrated Passenger Transport Network under Emergency Events.

Urgent natural environmental events, such as floods, power failures, and epidemics, result in disruptions to the traffic system and heavy disturbances in public requirements. In order to strengthen the ability of the transport network to handle urgent natural environmental issues, this paper simulates the disruption situation of traffic stations in the urban agglomeration by attacking nodes, and evaluates the ability of the transport network to resist disruptions (i.e., invulnerability). Firstly, the model of the urban agglomeration integrated passenger transport network is established based on complex network theory. The highway network, railway network, and coupling network are combined into a multi-layer network space structure, and the edge weight is calibrated by travel time and cost. Secondly, the invulnerability simulation process including multiple attack modes under random and deliberate attack strategies is sorted out. By improving the traditional network efficiency indicator, the network impedance efficiency indicator is proposed to measure network performance, and the network relative impedance efficiency indicator is used to evaluate network invulnerability and identify key nodes. Finally, Chengdu-Chongqing urban agglomeration is taken as a case study. The results show that the network does not collapse quickly and it shows certain invulnerability and robustness under continuous random attacks. Network performance and invulnerability are not necessarily positively correlated. The failure of individual nodes that are small in scale but act as transit hubs may significantly degrade the network performance. The identified key nodes have significance for guiding the construction, maintenance, and optimization of the urban agglomeration passenger transport network, which is conducive to promoting public safety.

Air transport network optimization: analysis and selection of new markets

Air transport network optimization: analysis and selection of new markets

Optimizing Intermodal Transport and Hub Location Problem for ECOWAS in the Context of Improving Intra-African Trade

Trade within Africa is at an all-time low, with a lack of an optimal intermodal transportation network and a high cost of business serving as a deterrent to trade. This research studies Intra-African Trade within ECOWAS using a hierarchical spatial aggregation process to identify 27 nodes. Distance-based weighted centrality measures employed TOPSIS to model a ranked node centrality of Economic Community of West African States (ECOWAS) intermodal transport of railway, road, and waterway networks. The ten highest-ranking nodes identified from the mixed-integer linear program adopted as candidate hubs, thus selecting Ibeshe ferry terminal in Lagos and Tema Harbour in Accra for waterway; Thies, Ibadan, Conakry, Monrovia and Thies for railway; and Thies and Ibadan for road. We conclude with sensitivity analysis and a discussion of management implications for ECOWAS. This paper demonstrated that a limited number of transshipment hubs would encourage interregional trade and cut point-to-point transportation costs and lead to economic growth and development.

Mathematical Modeling Methods and Their Application in the Analysis of Complex Signal Systems

Mathematical models are effective means of answers established by humans to solve real-world problems. Complex wireless communication can establish information interaction between vehicles, in order to reduce the delay time of the coordination control optimization timing scheme in coordination delay time. For smart car driving, a complex signal system, this study first establishes a relevant mathematical model. It is used to compare three mathematical models commonly used today. The results obtained under the same conditions show that the mathematical model is better in dealing with the complex signal system in terms of transmission accuracy in all segments. A number of vehicles in different states of the traffic system are selected, and the relevant data are collected to plot ROC curves using the mathematical model. It can be concluded that the freer and more complex the movement behavior of the vehicle, the greater the load it imposes on the road and the system. The results of the confusion matrix show that the model can effectively reduce the pressure on the road and the signal system. With the starting objective of smooth operation of public transportation, the target values are optimized by layering, and finally, the regional roadway capacity is effectively converged. Then, the mathematical model optimization of complex wireless systems and intelligent transportation networks is quantitatively evaluated. The optimized timing scheme through coordinated control achieves the expected effect in coordinated control of delay time and also reduces the average delay time of all intersections of the road network.

Grain market of Kazakhstan: analysis of the situation, competitive advantages

The largest area of Kazakh agribusiness is grain subcomplex with its own commodity market, which is the basis of the domestic AIC. The goal is an economic analysis of functioning of agricultural sector of economy, identifying the causes of fluctuations in production and grain yields. In the work such scientific methods are used – logical, statistical, bibliometric, factorial. The results of the study – it is shown that the Covid pandemic, global inflation and anti-russian sanctions had a negative impact on the activities of agricultural entities; it is substantiated that the country has significant sown areas and key territorial advantages in grain production specialization, as well as flour production; grain export restrictions were identified due to the distance from sea routes; analytical review of the bibliography of foreign scientific achievements of grain economy is presented; the dynamics and problems in grain industry are considered, taking into account the decrease in the inflow of foreign capital and disruption of value added organization chains; the role of investments in agro-industrial production is determined; economic assessment of the main statistical indicators of AIC is given; the reasons that affect the level of productivity, depending on the climatic and technical conditions of large agricultural producers and small peasant (private) farms in various regions of the Republic of Kazakhstan, as well as factors affecting its decline are indicated. Conclusions – the main ways to quickly resolve the current situation from the standpoint of state measures of financial support to increase the supply of ma rketable grain in the republic are summarized. Efficient organization and development of grain products market to ensure food security is a key area of economy of Kazakhstan. Important issue is the optimization of the country's transport and logistics infrastructure network.

A novel fixed-node unconnected subgraph method for calculating the reliability of binary-state networks

Network reliability is a widely used index to evaluate the operational quality of a network system. However, existing accurate algorithms used to calculate network reliability has high time complexities, which requires much calculation time. To reduce the time requirement of accurate reliability calculation of a binary-state network, a fixed-node unconnected subgraphs algorithm (FUSA) is proposed based on the knowledge of graph theory and the enumeration method. During network reliability calculation, this algorithm only searches for subgraphs and does not generate all MCs/MPs or all network state vectors, which greatly reduces computational complexity. In addition, relative complement set and excluded arcs set are introduced in the algorithm to optimize subgraph generation. On this basis, a quick fixed-node unconnected subgraphs algorithm (QFUS) is proposed. Compared with traditional algorithms, FUSA and QFUS have a lower time complexity. Numerical examples show that the calculation efficiency of QFUS is improved by at least 50 times compared with FUSA and QFUS is more efficient than other direct algorithms in solving the reliability problem of binary-state networks. The results of our research may promote the development of transportation network optimization and planning.

A Transportation Network Optimization Model for Livestock Manure under Different Terrains Considering Economic and Environmental Benefits

Optimizing the path of livestock manure used for farmland is a hugely significant issue, which not only improves the utilization efficiency of manure but also reduces the cost of the transportation of manure. However, some factors such as different terrains and the density of surrounding farmland may lead to more difficulty in further improving the resource utilization rate. Therefore, this paper aims to establish a transport network optimization model for a complex livestock manure distribution scheme. Using basic information from livestock and poultry farms, cultivated land, water areas and forestland in Xinzhou District, Wuhan City, Hubei Province, the relationship between farmland and livestock farms is divided into farm-intensive and water-intensive farmland areas by using the Voronoi diagram subdivision method. Then, according to the supply–demand balance of manure and crop demand, an optimization model is proposed to discuss the manure return scheme for these two types of terrain. The results show that our model can help significantly improve manure utilization efficiency under different terrain situations, which is proposed comprehensively, considering the economic and environmental benefits.

Determination of Optimal Distribution and Transportation Network

Today, transportation network optimization has become one of the significant aspects of supply chain planning, and even a slight rise in productivity can significantly reduce costs of distribution of wood in the transportation network. In the forest based industry, given that transportation is the main cost of raw wood supply, using transportation planning, distribution should be done in a way so as to minimize the overall wood displacement. Such planning must meet the needs of all demand centers and the distribution supplier points must be used to their full capacity. Accordingly, the present study strived to find an optimal solution for transportation and distribution of raw wood from the main supplier points to small and large centers of wood and paper industries in Iran. This optimization simultaneously focuses on several products and is at the macroeconomic level of the country wood market. To achieve this goal, linear programming – Transportation Simplex Algorithm was used. The results show a significant fall in transportation costs and a more organized wood distribution network than the current situation. This cost reduction can be attributed to decisions about the optimal distribution of wood types, determining transport routes, and opting for the right type of truck supplier based on load tonnage and distance. This plummet in transportation costs plunges the cost of wood and wood products, which will surge competition in the business and will be of interest to manufacturers, distributors, customers and stakeholders in general.

An Improved Ant Colony Algorithm for Urban Bus Network Optimization Based on Existing Bus Routes

Adding new lines on the basis of the existing public transport network is an important way to improve public transport operation networks and the quality of urban public transport service. Aiming at the problem that existing routes are rarely considered in the previous research on public transportation network planning, a public transportation network optimization method based on an ant colony optimization (ACO) algorithm coupled with the existing routes is proposed. First, the actual road network and existing bus lines were abstracted with a graph data structure, and the integration with origin–destination passenger flow data was completed. Second, according to the ACO algorithm, combined with the existing line structure constraints and ant transfer rules at adjacent nodes, new bus-line planning was realized. Finally, according to the change of direct passenger flow in the entire network, the optimal bus-line network optimization scheme was determined. In the process of node transfer calculation, the algorithm adopts the Softmax strategy to realize path diversity and increase the path search range, while avoiding premature convergence and falling into local optimization. Moreover, the elite ant strategy increases the pheromone release on the current optimal path and accelerates the convergence of the algorithm. Based on existing road network and bus lines, the algorithm carries out new line planning, which increases the rationality and practical feasibility of the new bus-line structure.

Multicommodity routing optimization for engineering networks.

Optimizing passengers routes is crucial to design efficient transportation networks. Recent results show that optimal transport provides an efficient alternative to standard optimization methods. However, it is not yet clear if this formalism has empirical validity on engineering networks. We address this issue by considering different response functions—quantities determining the interaction between passengers—in the dynamics implementing the optimal transport formulation. Particularly, we couple passengers’ fluxes by taking their sum or the sum of their squares. The first choice naturally reflects edges occupancy in transportation networks, however the second guarantees convergence to an optimal configuration of flows. Both modeling choices are applied to the Paris metro. We measure the extent of traffic bottlenecks and infrastructure resilience to node removal, showing that the two settings are equivalent in the congested transport regime, but different in the branched one. In the latter, the two formulations differ on how fluxes are distributed, with one function favoring routes consolidation, thus potentially being prone to generate traffic overload. Additionally, we compare our method to Dijkstra’s algorithm to show its capacity to efficiently recover shortest-path-like graphs. Finally, we observe that optimal transport networks lie in the Pareto front drawn by the energy dissipated by passengers, and the cost to build the infrastructure.

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.

Investigating the Potential of Data Science Methods for Sustainable Public Transport

The planning and implementation of public transport involves many data sources. These data sources in turn generate a high volume of data, in a wide variety of formats and data rates. This phenomenon is reinforced by the ongoing digitization of public transport; new data sources have continuously emerged in public transport in recent years and decades. This results in a great potential for the application and utilization of data science methods in public transport. Using big data methods and sources can, or in some cases already does, contribute to a better understanding and the further optimization of public transport networks, public transport service and public transport in general. This paper classifies data sources in the field of public transport and examines systematically for which use cases the data are used or can be used. These steps contribute by structuring ongoing discussions about the application of data science in the public transport domain and illustrate the potential of the application of data science for public transport. We present several use cases in which we applied data science methods, such as machine learning and visualization to public transport data. Several of these projects use data from automated passenger information systems, a data source that has not been widely studied to date. We report our findings for these use cases and discuss the lessons learned, to inform future research on these use cases and discuss their potential. This paper concludes with a summary of the typical problems that occur when dealing with big public transport data and a discussion of solutions for these problems. This discussion identifies future work and topics worth investigating for public transport companies as well as for researchers. Working on these topics will, in our opinion, support the improvement of public transport towards the efficiency and attractiveness that is needed for public transport to play its essential role in future sustainable mobility. The application of these methods in public transport requires the collaboration of domain experts with researchers and data scientists, calling for a mutual understanding. This paper also contributes to this understanding by providing an overview of the methods that are already used, potential new use cases, data sources, challenges and possible solutions.

Hub-and-Spoke Transportation Network Optimization for China Railway Express with Consideration of Carbon Trading Prices

Hub-and-Spoke Transportation Network Optimization for China Railway Express with Consideration of Carbon Trading Prices

A review of computational intelligence methods for traffic management systems

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

A review of computational intelligence methods for traffic management systems

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

Modeling of Mono-Commodity Multi-Location Linear Programming Techniques for Determining Optimum Transportation Network of a Manufacturing Industry

This research on Modeling and Application of Mono-Commodity Multi-Location Linear Programming Techniques For Determining Optimum Transportation Network was carried out at a Manufacturing Industry in Lagos, which comprises of two plants, three depots and twenty retailers axis. The model was analyzed using Micro Soft Excel Software. The analysis to determine the optimal transportation network was carried out in two phases by considering numbers of truckload transported and each commodity from plants to depots and depots to retailers and their optimals. It was discovered that the existing practices transportation cost for truckloads moving from plant to retailers is N3,544,000,000,000 and when optimized, cost is N1,932,650,000,000 while considering each product the optimized transportation cost is N1,871,065,369,000. This implies that the transportation network generated considering each product will yield 47.2% gain in profit than existing network. Hence, it is recommended that mono-commodity multi-location transportation network be used. Keywords: [EXCEL Software, Mono-Commodity Multi-Location Model, Transportation Cost, Transportation Model, Transportation Network.].

Optimization of regional transport networks based on a mathematical model of passenger preferences

This article presents the results of developing a model for a regional passenger transport network aimed at solving the logistical problem of constructing rational intermodal routes in a defined closed loop. The tools of graph theory and linear mathematical programming have been applied to build the model algorithm, which allows finding solutions for weighted graphs, in the absence of negative weight links, while keeping information about the sequence of hub points on the selected paths. The proposed solutions are versatile enough to be scalable for regions with different network topologies. The model is adapted to dynamically changing and extensible systems, allowing it to be practically applied to justify options for the future development of infrastructure in different modes of transport.

Development of transportation cost functions and optimization of transportation networks for solar-aided utilization of CO2

The present work aims to establish a methodology for the optimization of CO2 transportation networks by developing transportation levelized-cost functions for the mixture CO2:N2 (96:4%) within the ranges {25–2000 kg/s} and {50–2000 km}, locating the optimal solar cities (locations-sinks) to receive CO2 for further transformation into fine chemicals, and mapping the optimal routes which present the minimum transportation and utilization cost. Potential economies of scale of merging same-route sinks into a joint pipeline are scrutinized by selecting 17 CO2 sources from thermal power and combustion stations in the central EU region aggregating almost 3000 kg/s of CO2; these amounts are optimally allocated to 16 available sinks, which combine maximum solar capacity with the least distance to major ports. This study makes use of transport cost correlations, while the solar potential of the most promising solar cities is calculated in detail based on solar intensity databases and solar power plants cost assessment. Mixed-integer nonlinear programming (MINLP) techniques are incorporated in the optimization procedure under sinks capacity constraints. A major conclusion of this work is that for the mass flowrates considered and the sources locations chosen in the current study it is economically beneficial to transfer CO2 directly to the final sinks from the original sources rather than via intermediate sources. The MINLP formulation derives values of solar utilization cost of over €7B and transportation cost of over €2B per year covering transportation pipelines distances of 34,000 km: precise economic estimates of the proposed methodology regarding CO2 transportation and utilization might have significant implications on energy and environmental policy issues on European or global scale.

The Canadian Pacific – Kansas City Southern Railway Merger and the Optimal Railroad Network

This project evaluates the optimal transport network in North America by first analyzing the proposed $25 billion merger between the Canadian Pacific Railway and the Kansas City Southern Railway. Then this project studies different sequences of mergers and find the optimal path of mergers to form the transport network in North America. Current simulation results suggest that average over all origin–destination markets, the proposed merger between Canadian Pacific Railway and the Kansas City Southern Railway will decrease the average shipment cost by 6.27%. Among different local markets, regions near or utilize the route from Des Moines, IA – Kansas City, MO – Joplin, MO will have the largest efficiency gain from the proposed merger.

RETRACTED ARTICLE: IoT perception and public transportation network optimization based on big data algorithms

Big data uses network information technology in an extremely large number of information systems to quickly process and analyze this huge data information. In this way, the Internet of things system has the characteristics of rapidness, high efficiency, low density, and strong authenticity. Cohesion analysis technology refers to the process of effectively combining data mining and machine learning algorithms, a technology for fast and complete processing. The main working mechanism of cluster analysis is to classify similar objects according to the correlation of different data in the objects. Big data and Internet of things technology methods and traffic data have applicability, providing new ways to use big data in traffic and Internet of things. Through the analysis of various road traffic problems, the possibility of optimizing traffic through big data and the Internet of things is created, which will bring very good ideas and references for the development of intelligent transportation. Today, with the rapid development of the Internet of things, with the emergence and popularization of new hardware and the popularization and development of smart electronic terminals and mobile devices, traffic information–related data has also doubled. It is an inevitable requirement of development to use big data system for scientific and standardized management of traffic management departments, so that the traffic management system can be more optimized and reasonable, so as to completely solve the current phenomenon of road traffic congestion. To improve the transportation management of the latest information in the transportation system requires the use of information technology and data management systems. The big data system in the transportation field is an effective way to improve intelligent transportation management and transportation.