Transportation Network Optimization Research Articles

Formation and Optimization of Transport Networks in the Conditions of the Digital Integral Environment for Transportation

The article provides a brief description of the digital integrated transportation support environment. The methods of choosing the base route in the formation of transport networks in the conditions of the formation of a digital integrated environment are presented. It is shown that the means of solving the problem of optimization of transport networks is a developed modeling complex, which consists of a set of models with the necessary degree of detail. The paper presents a functional diagram and a brief description of the complex. The complex functions taking into account the replenishment, improvement and updating of models, allow you to combine different types of models and use the necessary of them when solving specific problems of transport network optimization. The modeling complex allows to significantly simplify the optimization of transportation networks. On the basis of the proposed mathematical apparatus, the software of an automated system has been developed, which is designed to function in a digital integrated environment, a brief theoretical description of which is given.

Public Traffic Congestion Estimation Using an Artificial Neural Network

Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements.

Optimization of Air Logistics Transport Network of YTO Express

In order to improve the overall level and long-term development of YTO Express, according to the development strategy and planning of YTO Express logistics, this paper puts forward the construction of Hub-and-Spoke air logistics transport network with core hub airport, and optimizes YTO Express transport network. Firstly, the cluster analysis function of SPSS is used to divide the region of the air node, and then the center of gravity method is used to select the regional hub point, and the core hub airport and the regional hub airport are determined, so as to design the final transport network, which is conducive to the sustainable development of YTO Express, and improve the proportion of YTO Express in the air field.

Uncertainty in classical systems (with a local, non-stochastic, non-chaotic origin)

Decisions made by two (or more) independent players in a dynamical system can result in an indeterminable, yet non-stochastic, outcome. The mechanism can be presented as a thought experiment in which two pilots attempt to steer a spacecraft but, in stabilizing its pitch and yaw, lose all knowledge of its forward motion. It is useful to think of this pilots’ dilemma as a classical analogue of Schrödinger’s ‘cat in a box’ thought experiment. In both, the outcome of a life or death scenario is indeterminate until it is directly observed, but in place of the probabilistic radioactive source in Schrödinger’s problem, the discontinuous action of the pilots’ decisions create the classical indeterminacy. We discuss the wider implications of the phenomenon, such as predicting indeterminate scenarios in optimal transport or other network problems. The behaviour only becomes apparent when a system’s dynamics is sufficiently taken into account.

Design of a Network Optimization Platform for the Multivehicle Transportation of Hazardous Materials.

With economic development, the volume of hazardous materials is increasing, and the potential risks to human beings and the natural environment are expanding. Road transportation has become the main mode of transportation for hazardous materials. Because of the specific characteristics of hazardous materials, if an accident occurs in the transportation process, it often causes mass casualties, serious property and socioeconomic damage, and damage to the ecological environment. Hence, transportation is an important part of the life cycle of hazardous materials. This paper designs an optimization platform for multidestination, multiterminal, and multivehicle networks that transport hazardous materials. The logistics module in TransCAD software is used to construct this platform. By identifying the effective transportation routes considering the transportation risk, sensitive target population, and transportation time of each road section, the entropy method can be used to fuse and obtain the comprehensive impedance value of each road section. Finally, the optimal transportation network of hazardous materials was obtained by the transportation network optimization algorithm in TransCAD. The platform can display the optimal transport program with data windows, text, and maps. The research results provide a reference for relevant departments to scientifically manage the transport of hazardous materials.

Optimal Transport Networks in Spatial Equilibrium

We study optimal transport networks in spatial equilibrium. We develop a framework consisting of a neoclassical trade model with labor mobility in which locations are arranged on a graph. Goods must be shipped through linked locations, and transport costs depend on congestion and on the infrastructure in each link, giving rise to an optimal transport problem in general equilibrium. The optimal transport network is the solution to a social planner's problem of building infrastructure in each link. We provide conditions such that this problem is globally convex, guaranteeing its numerical tractability. We also study cases with increasing returns to transport technologies in which global convexity fails. We apply the framework to assess optimal investments and inefficiencies in the road networks of European countries.

Collaborative intermodal freight transport network design and vehicle arrangement with applications in the oil and gas drilling equipment industry

Decentralized freight decision making has been proven to be one of the barriers to achieve the optimal cost-saving freight transportation network. This study presents a collaborative intermodal freight network for the transportations of oil and gas drilling equipment, where a freight forwarder serves as a centralized decision-maker to coordinate transportation activities. We formulate the problem as a minimum intermodal transport cost model with a nonlinear objective function. Also, novel path-based decision variables instead of arc-based decision variables are used to formulate the selections of transportation services. A hybrid genetic algorithm and particle swarm optimization algorithm (GA-PSO) in combination with a batch strategy is designed. The experimental results show that the proposed hybrid GA-PSO method has a better performance compared with existing algorithms in terms of the solution quality, and computational time. Furthermore, the proposed approach is applied to real-world instances of O&G drilling equipment in the ‘China Railway Express' network.

Inverse Maximum Capacity Path Problems Under Sum-Type and Max-Type Distances and Their Practical Application to Transportation Networks

The maximum capacity path problem is to find a path connecting two given nodes in a network such that the minimum arc capacity on this path is maximized. The inverse maximum capacity path problem (IMCP) is to modify the capacities of the arcs as little as possible so that a given path becomes maximum capacity path in the modified network. Two cases of IMCP are considered: the capacity of the given path is preserved or not. IMCP is studied and solved both, under any sum-type (e.g., weighted $l_{k}$ norms and sum-type Hamming distance) and max-type distance (e.g., weighted $l_{\infty }$ norm or bottleneck Hamming distance). The obtained algorithms for IMCP are applied to solve a real road transportation network optimization problem.

Emergent dynamics of neuromorphic nanowire networks

Neuromorphic networks are formed by random self-assembly of silver nanowires. Silver nanowires are coated with a polymer layer after synthesis in which junctions between two nanowires act as resistive switches, often compared with neurosynapses. We analyze the role of single junction switching in the dynamical properties of the neuromorphic network. Network transitions to a high-conductance state under the application of a voltage bias higher than a threshold value. The stability and permanence of this state is studied by shifting the voltage bias in order to activate or deactivate the network. A model of the electrical network with atomic switches reproduces the relation between individual nanowire junctions switching events with current pathway formation or destruction. This relation is further manifested in changes in 1/f power-law scaling of the spectral distribution of current. The current fluctuations involved in this scaling shift are considered to arise from an essential equilibrium between formation, stochastic-mediated breakdown of individual nanowire-nanowire junctions and the onset of different current pathways that optimize power dissipation. This emergent dynamics shown by polymer-coated Ag nanowire networks places this system in the class of optimal transport networks, from which new fundamental parallels with neural dynamics and natural computing problem-solving can be drawn.

Optimizing biologically inspired transport networks by control.

Transportation networks with intrinsic flow dynamics governed by the Kirchhoff's current law are ubiquitous in natural and engineering systems. There has been recent work on designing optimal transportation networks based on biological principles with the goal to minimize the total dissipation associated with the flow. Despite being biologically inspired, e.g., adaptive network design based on slime mold Physarum polycephalum, such methods generally lead to suboptimal networks due to the difficulty in finding a global or nearly global optimum of the nonconvex optimization function. Here we articulate a design paradigm that combines engineering control and biological principles to realize optimal transportation networks. In particular, we show how small control signals applied only to a fraction of edges in an adaptive network can lead to solutions that are far more optimal than those based solely on biological principles. We also demonstrate that control signals, if not properly designed, can lead to networks that are less optimal. Incorporating control principle into biology-based optimal network design has broad applications not only in biomedical science and engineering but also in other disciplines such as civil engineering for designing resilient infrastructure systems.

Multimodal Transport Network Optimization Considering Safety Stock under Real-Time Information

A production department and two distribution departments form a multimodal logistics supply network. The production department provides two ordering opportunities within one production cycle, and the real-time updates of demand information are required in the interval between two orders. Based on this, the paper studies the security inventory optimization problem of multimodal transport networks. On the basis of portraying the real-time information update process, the multimodal network optimization model without considering safety stock and the multimodal network optimization model considering safety stock are constructed, respectively, and the transshipment price and demand information of the intermodal network are updated. The parameters are analyzed for sensitivity. The analysis of examples shows that, on the one hand, with the update of demand information, the distribution department adopts the multimodal transport model with safety stock to improve the profit of the distribution department and the supply network. On the other hand, the production sector can meet market demand with a lower production scale, while the distribution department can effectively respond to stochastic demand through a multimodal model with safety stock.

System-Level Optimization of Multi-Modal Transportation Networks for Energy Efficiency using Personalized Incentives: Formulation, Implementation, and Performance

The paper presents the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation system. From the user’s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives, consisting of a combination of departure time, mode, and route. Each alternative is rewarded with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated with the available alternatives to minimize the system-wide energy consumption under a limited token budget. To do so, the alternatives that guarantee the largest energy reduction must be rewarded with more tokens. SO is multi-modal, in that it considers private cars, public transit, walking, car pooling, and so forth. Moreover, it is dynamic, predictive, and personalized: the same alternative is rewarded differently, depending on the current and the predicted future condition of the network and on the individual profile. The paper presents a method to solve this complex optimization problem and describe the system architecture, the multi-modal simulation-based optimization model, and the heuristic method for the online computation of the optimized token allocation. Finally it showcases the framework with simulation results.

Model of Using the Exhaustive Search Algorithm in Solving of Traveling Salesman Problem (TSP) on the Example of the Transport Network Optimization of Primorje-Gorski Kotar County (PGC)

This paper considers and presents a model of the use of an exhaustive search algorithm in solving problems of a salesman on the example of the transportation network optimization of Primorsko Goranska County. By identifying the suboptimal solutions of transportation network, that enable insight into more transportation routes and can be significantly influenced by the reduction of transportation costs and creating increased opportunities for the carrier in the context of flexible designing the structure of the commodity flows, greater utilization of transport capacity, and thus achieve greater profit. In the analyzed example, transportation network of Primorje-Gorski Kotar County (PGC) is divided into two sets (segment): 1) a set of cities (nodes) that are located (placed) on the same traffic direction (transportation route) that represents the optimal transportation route, and 2) a set of cities (nodes) that connect through a variety of transport relationships and that are included in the calculation by using the exhaustive search algorithm. In the example are also defined nodes that are common to both the two sets and that are connecting by the virtual transportation route in the spreadsheet optimization model.

Bilevel Programming Model of Urban Public Transport Network under Fairness Constraints

In this paper, the bilevel programming model of the public transport network considering factors such as the per capita occupancy area and travel cost of different groups was established, to alleviate the urban transportation equity and optimize the urban public transport network under fairness constraints. The upper layer minimized the travel cost deprivation coefficient and the road area Gini coefficient as the objective function, to solve the optimization scheme of public transport network considering fairness constraints; the lower layer was a stochastic equilibrium traffic assignment model of multimode and multiuser, used to describe the complex selection behavior of different groups for different traffic modes in the bus optimization scheme given by the upper layer. The model in addition utilised the noninferior sorting genetic algorithm II to validate the model via a simple network. The results showed that (1) the travel cost deprivation coefficient of the three groups declined from 33.42 to 26.51, with a decrease of 20.68%; the Gini coefficient of the road area declined from 0.248 to 0.030, with a decrease of 87.76%; it could be seen that the transportation equity feeling of low-income groups and objective resource allocation improved significantly; (2) before the optimization of public transport network, the sharing rate of cars, buses, and bicycles was 42%, 47%, and 11%, respectively; after the optimization, the sharing rate of each mode was 7%, 82%, and 11%, respectively. Some of the high and middle income users who owned the car were transferred to the public transportation. It could be seen that the overall travel time of the optimized public transport network reduced, enhancing the attraction of the public transport network to various travel groups. The model improves the fairness of the urban public transport system effectively while ensuring the travel demand of the residents. It provides theoretical basis and model foundation for the optimization of public transit network, and it is a new attempt to improve the fairness of the traffic planning scheme.

Optimal transport network design for both traffic safety and risk equity considerations

Traffic safety planning is one of the most cutting-edge research directions, which regards traffic safety as the main indicator of sustainable transportation. Although traffic safety has been widely recognized, it is rarely considered during the transport network design stage. Moreover, traffic safety performance usually refers to the total number of traffic accidents while the risk equity issues are given much less attention by transportation planners and managers. In this paper, a novel bi-objective bi-level model framework is formulated that explicitly captures the leader-follower nature of the relationship between authorities and travelers. In the upper-level, authorities design transport network by optimizing the impact of investment in terms of both total risk and risk equity. In the lower-level, a sequential four-step model with feedback is formulated to represent travelers' behavior responds to transport network design. The feedback procedure can converge to transport system equilibrium that is further fed back to the upper-level to measure network safety performances. To solve the proposed bi-level model, an efficient algorithm, named Dirichlet allocation algorithm, is designed on the basis of Dirichlet distribution, Method of successive averages (MSA), Frank–Wolfe algorithm and Dijkstra's algorithm. A simulated example using Nguyen-Dupuis network is conducted to verify the effectiveness of the proposed methods. The results show that transport network design has a great impact on traffic safety performances. The optimal network design can reduce traffic accidents and improve risk equity significantly from very beginning. The modeling framework can be a valuable tool for active safety planning. It provides valuable implications for network designers on how to design transport network for traffic safety and risk equity simultaneously.

Stability for the mailing problem

We prove that optimal traffic plans for the mailing problem in Rd are stable with respect to variations of the given coupling, above the critical exponent α=1−1/d, thus solving an open problem stated in the book Optimal transportation networks, by Bernot, Caselles and Morel. We apply our novel result to study some regularity properties of the minimizers of the mailing problem, showing that only finitely many connected components of an optimal traffic plan meet together at any branching point.

Parallel Accelerated Virtual Physarum Lab Based on Cellular Automata Agents

Self-aware and self-expressive physical systems are inspiring new methodologies for engineering solutions of complex computing problems. Among many other examples, the slime mold Physarum Polycephalum exhibits self-awareness and self-expressiveness while adapting to changes in its dynamical environment and solving resource-consuming problems like shortest path, proximity graphs or optimization of transport networks. As such, the modeling of the slime mold’s behavior is essential when designing bio-inspired algorithms and hardware prototypes. The goal of this paper is to combine one of the powerful parallel computational tools, cellular automata (CA) with the adaptive potential of Physarum slime mold. Namely, we propose a CA model and multi-agent approach to imitate the behavior of the plasmodium. We then test the efficacy of the proposed model on graph problems such as the maze problem or the traveling salesman problem (TSP). Finally, the virtual Physarum model is evaluated on a data set for pattern recognition purposes and achieves to form very effectively the letters of the alphabet, especially when compared with real experiments performed to prove the efficacy of the proposed model. Furthermore, to exploit the CA’s inherent parallelism and make the model’s responses faster, both GPU and hardware implementations are proposed and compared. As a result, an accelerated virtual lab is developed which uses a multi-agent CA model to describe the behavior of plasmodium and can be used as an intelligent, autonomous, self-adaptive system in various heterogeneous and unknown environments spanning from different types of graph problems up to real life-time applications.

Design and Optimization of Underground Logistics Transportation Networks

With the burden of city transportation system becoming bigger and bigger, it is imperative to develop reliable and efficient underground logistics. The appropriate location of cargo transshipment centers in underground logistics system is selected using the Set Covering Problem Model, Weighted Set Covering Problem Model, and the reasonable prediction of the freight volume data of major cities to ensure the maximum numbers of service nodes are covered by the least transshipment centers within a reasonable range. The timing of the construction of facilities in the system is proposed, considering the construction cost and cost recovery period of the underground logistics system. The design and optimization plan of the urban underground logistics system, based on the above, is given to achieve the purpose of relieving urban traffic congestion and increasing freight volume.

Optimization of logistics motor transport networks with application of propositional calculus laws

Optimization of logistics motor transport networks with application of propositional calculus laws

Influence of logistics system optimization on a company value

The value of a company is influenced by many factors. It depends on how we evaluate the methods we use and what data we have for the calculation. It is also important for what purpose we will calculate the company value. However, the company's management decisions, their strategy and overall direction have a significant impact on the value of the business. In the article we will deal with the impact on the value of a business's optimization of the company's transport network. Also, whether optimization affects more revenue method or equity method.