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In order to study the traffic jams and congestion and to understand the forming process of traffic congestion. On the basis of synchronization theory of complex network, this paper investigated the stability of an extended car-following model under honk environment. By using the Lyapunov stability theory and designing the appropriate controller, the car-following model is quickly stabilized and the stability condition of the model is obtained. In addition, the stability of the extended car-following model under honk environment is studied when the vehicle is subjected to external disturbance. Finally, the effects of the designed controller on the stability of the extended car-following model under honk environment and the influence of honk effect on the traffic flow are verified by using the Matlab simulation technology. The conclusions of this paper can provide a theoretical reference for the prevention and control of traffic congestion.

The paper established a conventional bus traffic network and a subway network by using the method of space R based on the existing public traffic network model. Regarding these two networks as the ...

Artificial bee colony (ABC) algorithm is widely known for its distinguished exploration ability. However, its exploitation ability is relatively poor. To solve the problem, we propose a novel combinatorial search strategy, whose guided vector can be freely switched between a random vector and the global best vector. It can help improve the exploitation ability of ABC. At the same time, a random vector is beneficial to regulate the enhanced exploitation ability. In addition, both of them can pass information on to a current vector instead of only perturbing a current vector itself. The two guided vectors are chosen with a probability depending on the ratio of fitness information of a current vector to that of the global-best vector. Thus, one of the two guided vectors can be adaptively selected to direct the search. In addition, a mechanism of frequency of perturbation is employed to enhance the scale of information sharing between a current vector and a guided vector for each onlooker bee. Moreover, a modified greedy selection mechanism is designed to choose a child vector inspired by simulated annealing. Furthermore, the search strategy of multiple scouts is also employed in the last stage. Based on all these modifications, an improved ABC (IABC) is proposed. Finally, a few experiments are carried on 58 benchmark problems, including CEC2014 benchmark problems. The computational results exhibit the merit of IABC.

In order to study the traffic jams and congestion, this paper investigated the stability of a car-following model on the basis of synchronisation theory of complex network. By using the Lyapunov st...

Artificial bee colony (ABC) algorithm is a relatively new biologically-inspired optimization algorithm. According to its solution search equation, it can be seen that ABC is good at exploration but poor at exploitation. Inspired by the gravity model, an attractive force model is proposed for choosing a better neighbor of a current individual to improve the exploitation ability of ABC. Then we propose a novel solution search equation, in which the chosen neighbor plays an important role in guiding the searching process in the employed bee phase. Next, a random guiding search is introduced in the onlooker bee phase to balance the foregoing exploitation. Subsequently, multiple solution search equations, a scheme of perturbation frequency, and a multiple scouts search strategy in view of opposition-based learning are also incorporated into the proposed algorithm, called ABCG, to further reach a good compromise between the exploitation and the exploration. Finally, ABCG is tested on a great number of benchmark functions . The experimental results show that ABCG is effective for solving the complex benchmark problems and it can be considered as a competitive approach.

Urban public transport is an effective way to solve urban traffic problems and promote sustainable development of urban traffic. A scientific operation scheduling system has an important guiding significance for optimizing the configuration of urban public transport capacity resources, improving the level of operation organization and management, and providing for the sustainability of the transportation system. According to the inhomogeneous distribution of passenger flow along transit lines, this study develops a combinational scheduling model in which the enterprise supplies zonal service based on regular service. The objective function minimizes the sum of passenger travel cost and operation cost, and the simulated annealing algorithm is designed to solve the optimization model. This paper abstracts an ideal example by taking a real-world case of Bus Line 131 in Lanzhou, China. The numerical example is used to verify the validity of the model and algorithm. Results show that the combinational operation scheme can effectively satisfy passengers&rsquo, demand and reduce the total cost by 7.03% in comparison with the regular operation system. The optimal combinational system with the lowest total cost can increase the vehicle load factor and improve the utilization ratio.

The paper proposes a novel permanent magnet synchronous machine (NPMSM) with one radial permanent magnet synchronous machine (RPMSM) in the center and two axial permanent magnet synchronous machines (APMSMs) on both sides. The two APMSMs take advantage of the stator end winding spaces of the RPMSM and provide additional output power without increasing the whole machine volume. The emphasis of this study is on the unique rotor structure design and its merits for producing additional output power. The 3D finite element method (FEM), coupling with the equivalent circuit method is used in the study for designing the NPMSM. And the NPMSM prototype test results match well with the calculated results.

Purpose – Critical links in traffic networks are those who should be better protected because their removal has a significant impact on the whole network. So, the purpose of this paper is to identify the critical links of traffic networks. Design/methodology/approach – This paper proposes the definition of the critical link for an urban traffic network and establishes mathematical model for determining critical link considering the travellers’ heterogeneous risk-taking behavior. Moreover, in order to improve the computational efficiency, the impact area of a link is quantified, a partial network scan algorithm for identifying the critical link based on the impact area is put forward and the efficient paths-based assignment algorithm is adopted. Findings – The proposed algorithm can significantly reduce the search space for determining the most critical links in traffic network. Numerical results also demonstrate that the structure of efficient paths has significant impact on identifying the critical links. Originality/value – This paper identifies the critical links by using a bi-level programming approach and proposes a partial network scan algorithm for identifying critical links accounting for travellers’ heterogeneous risk-taking behavior.

Artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.

For deep learning cloud computing platforms, file system is a fundamental and critical component. Hadoop distributed file system (HDFS) is widely used in large scale clusters due to its high performance and high availability. However, in deep learning datasets, the number of files is huge but the file size is small, making HDFS suffer a severe performance penalty. Although there have been many optimizing methods for addressing the small file problem, none of them take the file correlation in deep learning datasets into consideration. To address such problem, this paper proposes a Pile-HDFS (PHDFS) based on a new file aggregation approach. Pile is designed as the I/O unit merging a group of small files according to their correlation. In order to effectively access small files, we design a two-layer manager and add the inner organization information to data blocks. Experimental results demonstrate that, compared with the original HDFS, PHDFS can dramatically decrease the latency when accessing small files and improve the FPS (Frames Per Second) of typical deep learning models by 40%.

Artificial bee colony (ABC) has a good exploration ability against its exploitation ability. For enhancing its comprehensive performance, we proposed a multistrategy artificial bee colony (ABCVNS for short) based on the variable neighborhood search method. First, a search strategy candidate pool composed of two search strategies, i.e., ABC/best/1 and ABC/rand/1, is proposed and employed in the employed bee phase and onlooker bee phase. Second, we present another search strategy candidate pool which consists of the original random search strategy and the opposition-based learning method. Then, it is used to further balance the exploration and exploitation abilities in the scout bee phase. Last but not least, motivated by the scheme of neighborhood change of variable neighborhood search, a simple yet efficient choice mechanism of search strategies is presented. Subsequently, the effectiveness of ABCVNS is carried out on two test suites composed of fifty-eight problems. Furthermore, comparisons among ABCVNS and several famous methods are also carried out. The related experimental results clearly demonstrate the effectiveness and the superiority of ABCVNS.

In this paper, we study the Quay Crane (QC) assignment for incoming vessels by introducing hard time windows into the container terminal. Given a nonlinear programming model, a set of 0-1 variables is employed to describe the starting position of the operation for each QC in a certain vessel. We also employ another set of 0-1 variables to locate the ending operation position for each QC in the same vessel. The research objective is to minimize the maximum operation time of each QC. This is a typical NP-hard problem, so a genetic algorithm is applied to the searching of the optimum solution in real-time applications. We carried out a series of experiments in order to evaluate our model. The results show that the proposed model and algorithm can signiflcantly raise the operating e‐ciency of the container terminal.

An urban public transportation super-network model is established in this article. Compared with three existing complicated urban public transportation network models (the C-space network, L-space network, and P-space network), our urban public transportation super-network model first incorporated both station networks and bus line networks explicitly, which connects two non-homogeneous nodes (station and line nodes) through the notion of super networks. The proposed model reflects the interaction and mutual influences between the internal features and internal structures of the urban public transportation super network. And then the function projective synchronization of the dynamic public transportation super-network model is proven, and the high stability of the public transportation super network is confirmed by the Lyapunov stability theorem. Finally, the classical Lorenz chaotic system is applied in a numerical example to illustrate the proposed model and algorithm. The impact of departure frequency, line crowdedness, passenger transport density, and station dwell time on synchronous ability of the urban public transportation super-network model is analyzed. So, this study is a discussion on the urban public transportation super-network balance in order to offer theoretical basis for people to research bus dispatching and bus lines optimization.

On the basis of traditional weighted network, we study a new complex network model with multi-weights, which has one or several different types of weights between any two nodes. According to the method of network split, we split the complex network with multi-weights into several different complex networks with single weight, and study its global synchronization. Taking bus lines as the network nodes, a new public traffic roads network model with multi-weights is established by the proposed network model and space R modeling approach. Then based on the Lyapunov stability theory, the criteria is designed for the global synchronization of the public traffic roads networks with multi-weights. By changing the different weights and taking the Lorenz chaotic system for example, some numerical examples are given to discuss the balance of the whole public traffic roads network.

With an increased operating speed in Chinese railways in recent years, the number of passengers traveling by trains has been significantly increasing. The research issue is regarding how passengers select their routes when there are no direct trains. Previous studies have dealt with the route selection problem as a multi-objective optimization. The study began by setting up a transportation network which encompasses the departure and terminal stations along with important intermediate stations. Then, six key factors are analyzed and formulated using a multi-objective model, consisting of the train-running time, railway fare, transfer frequencies, distances between transfer stations, transfer interval time, and travel comfort. Furthermore, a two-phase algorithm is employed to solve the model. A rapid searching algorithm for feasible routes based on the train timetable is established, then the weight vector is assigned by introducing the information entropy to obtain satisfied routes. In the end, the two-phase algorithm is tested respectively for railway passengers from Lanzhou to Beijing (with direct trains) and from Lanzhou to Changchun (without direct trains), and the results show that the proposed model and solution algorithm are efficient for obtaining satisfactory routes.

We study coordination mechanisms through penalty schemes to cooperate the behavior of two firms as successive segment carriers to make transport plan separate in freight intermodal market. Based on the different cost structure and service level constraint to two firms, we compare the decision making in two possible decision systems, that is, centralized system and decentralized system. In a centralized system—the first best case as a benchmark is contrasted with decentralized system. In the decentralized system, a Stackelberg game model is formulated between two firms. Some discordant decisions would be made by firmI's overestimate motivation and firmII's undersupply motivation. Our primary objective is to design penalty schemes to coordinate the interactions for two firms. The study shows in a decentralized system, setting suitable penalty schemes can coordinate the two firms' decision. We also study the feasible range of penalty parameters, and some important managerial insights are then deduced. In the end, a numerical example is provided to verify the validity of results, some concluding remarks are presented subsequently.

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions.

In this paper, we present improved genetic algorithm for solving the fuzzy multiobjective solid transportation problem in which the coefficients of objective function are represented as fuzzy numbers. The ranking fuzzy numbers with integral value are used in the evaluation and selection. The proposed algorithm is incorporated with problem-specific knowledge and conductive to find out the set of nondominated points in the criteria space based on decision maker degree of optimism.

Design of vertical section of rolling down zone on hump affects rolling condition of car, and efficiency, ability and safety of shunting operating. Taking three factors, say, minimizing rolling time difference between front car and the latter, height of hump and the number of retarders as objectives, considering engineering technology criterion that should be satisfied, a Multi-objective optimization model is established and NSGA-II multi-objective genetic algorithm is adopted to obtain Patero Set in this paper. The model and algorithm are shown by a cast and both of them are reasonable.

The selection of path set has a great influence on stochastic traffic assignment. By considering path selection character of road users and traffic restriction in real road network, the concept of efficient paths is redefined. Moreover, a vertices-outdegree-based genetic algorithm (GA) is designed which adopts positive integer coding method. Furthermore, the probabilities of crossover and mutation are adjusted by adaptive algorithm and the process of selection is adopted simulated annealing algorithm to maintain the diversity and convergence of population. Thereby, this algorithm does not need to repair chromosome and can cover the shortages of priority - based GA. While finding efficient path sets of multi-OD (Origination-Destination) at the same time, this method can improve the computation efficiency by decoding simultaneously. At last, a case study is given for verifying the efficiency.

Determining the appropriate locations for inspection stations in a traffic network is a special kind of flow interception problem. It has very important applications in the fields of traffic management and police defense management. It is of great significance to research this problem. Based on the analysis of the characteristics of traffic flow, and on the assumption that the interarrival times of the cars follow Poisson distribution and the service time follows negative exponential distribution, this paper intends to research that with the case of the constraints of the expected car queue length, the expected car waiting time, the server busy rate and inspection station capacity taken into account, and based on queueing theory, a non-linear integer programming model for the problem of inspection station location is formulated. Finally, a numerical example is given.

With the development of ATIS(Advanced traveler information system), it is valuable to provide travel plans for the trip makers. For the bus trip makers, transfer times are always considered firstly. However, in most of cases, only the information about the bus routes, serving time, bus stops on these bus routes, etc, are offered ATIS. Considering the stochastic bus traveling time and transfer waiting time, the non-linear 0-1 programming model and the hyper- grahp model for optimal riding routes with transfer times limited under the random circumstance are formulated in this paper. And the improved priority-based encoding genetic algorithm is also designed. Finally, we give a numerical example.

Car-group-collection-and-delivery schedule of through trains distributed to several loading or unloading places is related directly to the car economic efficiency. Moreover large amount of calculation should be processed which is very difficult when the number of the loading or unloading places is large by use of the traditional algorithm. Taking the uncertainty of car group collection and delivery operation times and freight operating times into account, a fuzzy linear regression prediction model and an uncertain mathematical model of car-group-collection-and- delivery schedule for through trains are formulated and a Partheno-genetic algorithm (PGA) is put forward to resolve the model, besides, analyzed by using an example. The simulation results show that the algorithm is very efficient and has practical application.

The k-tree problem is to find a tree with k vertices in a given graph such that the total cost is minimum and is known to be NP-hard. In this paper, the k-tree problem with fuzzy weights is firstly formulated as the chance-constrained programming by using the possibility measure and the credibility measure. Then an oriented tree and knowledge-based hybrid genetic algorithm is designed for solving the proposed fuzzy programming models.

In order to resolve the parking problem in the new district, a new site selection method for public parking lot is put forward. Firstly, the future parking demands are forecast by parking generation rate model. Secondly, the preliminary locations and quantity of the parking lot are planned by the analytical hierarchy process. Finally, the multi-objective site selection model is established and the genetic algorithm is designed. Based on the multi-objective model and the genetic algorithm, the final location and scale of parking lots are determined. Taking the new district of Lanzhou as an example, the locations and the berths of the parking lot are studied by the new method. The example shows the three-step method is feasible and it can help decision-makers plan the public parking lot in the new district.

This paper describes an implementation of genetic algorithm to solve bicriteria solid transportation problem (BSTP), which is the extension of our previous work. The criteria space approach for bicriteria linear program is embedded in the structure of genetic algorithms (GA) for finding the nondominated points in the criteria space. The proposed algorithm is coded with C language. The computation results show that the proposed algorithms is efficient for solving the bicriteria solid transportation problem. >

This study focuses on how to determine the optimum transportation route for hazardous materials under the certain, fuzzy or stochastic environment. On the basis of analysing the transportation route selection problem of hazardous materials TRSP-HM, three objectives are presented and the multi-objective routing programming model MRPM for hazardous materials transportation HMT is put forward under the certain environment, and an improved label algorithm is proposed to solve the MRPM. After defining the maximum-chance optimum route and the α-optimum routes, the multi-objective routing chance-constrained programming model MRCPM and multi-objective routing dependent-chance programming model MRDPM for HMT under the fuzzy or stochastic environment are established respectively. Then, the integration intelligent algorithm is developed to solve the proposed models, which integrates the fuzzy simulation, neural networks, stochastic simulation and genetic algorithm. Finally, the proposed models and algorithms are successfully tested with the help of two real cases.

In order to insure transportation safety and reach destination rapidly, the best transportation route of hazardous materials is studied with multi-attribute decision-making theory. The train loaded with the hazardous material is one kind of dynamic hazardous source, once it encounters the accident, it can cause the huge threat to the people's lives and properties. The scientific and reasonable choice for transportation route is the essential condition which safeguards the transportation security of hazardous materials. Under the hybrid language environment, the triangular fuzzy number and the interval number are used to express the uncertain variable, then the Lagrange function is established and the weighting vector is obtained by calculating partial derivative, and the best transportation route is ascertained through calculating the route projection operator value which displays on the ideal projection. The example confirms the decision-making method is feasible. The method is helpful for the relevant department to choose the reasonable transportation route under the hybrid language environment.

In order to select the best vehicle scheduling route for hazardous materials, a new method based on uncertain operator is proposed. On the foundation of establishing the indices of hazardous materials' vehicle scheduling routes, some basic theories of Uncertain Linguistic Hybrid Aggregation ULHA operator are used and the multi-attribute vehicle scheduling route decision-making method based on ULHA is set up. Finally, an example shows the method is feasible.

When selecting an optimal route for hazardous materials transportation, many factors are needed to be considered. Through minimising transportation risk and operation distance, multi-objective travelling salesman problem MO-TSP model for hazardous materials transportation route is established. The natural chromosome encoding is used to encode and the roulette and optimal saving strategy are combined for selection, the order crossover is used for crossover operation to improve the traditional genetic algorithm. Then the improved genetic algorithm is used to solve MO-TSP model of hazardous materials transportation route. Finally, the correctness and effectiveness of the model and algorithm are verified with a case. This approach can help decision-makers determine reasonable transportation route for the hazardous materials transportation.

On the basis of analysing the unusual objectives of hazardous material transportation HMT, a new multi-objective optimisation model for hazardous material transportation MOM-HMT is established, which takes into account transportation risk, operation time, the number of sensitive population, risks fairness and multi-batch transportation simultaneously. Then a fuzzy adaptive weighted genetic algorithm FAWGA is set up to solve the MOM-HMT by designing priority-based encoding method, partial matching crossover, fuzzy logic control and adaptive weighted assignment mechanism. Finally, the model and algorithm are applied to a real case. The study results show the new model is feasible and the improved genetic algorithm is more effective than the standard genetic algorithm and the improved ant colony algorithm.

In order to overcome the drawback of basic particle swarm optimisation algorithm, such as being subject to fall into local optimisation and being poor in performance of precision, a chaos multi-population particle swarm optimisation algorithm has been proposed, and the algorithm has been effectively used in dealing with the optimisation of transportation continuous network design problem. In this algorithm, multi-population parallel tactics are introduced to improve the global optimising ability and the chaotic search which behaves well in local searching is introduced to improve the solution. The transportation continuous network design problem is solved based on chaos multi-population particle swarm optimisation algorithm. Two examples are presented to compare the proposed method with some existing algorithms. The simulation results show the new particle swarm optimisation algorithm can effectively alleviate the problem of premature convergence and strengthen the global searching ability. The algorithm presented in the paper can apply to solve the large scale traffic design problems.

The location problem is closely related to people's lives and property, and the harmonious development of society. Based on the discussion of location principle, to meet the demand of emergency rescue point as large, the mathematical model for location problem of emergency service facilities is established. And then the Genetic-Simulated Annealing Algorithm (GSAA) for solving this model is designed, which enriches searching behaviour in optimisation process, and has strong abilities of exploration and exploitation in large real search spaces. In this algorithm, genetic algorithm controls the search direction, and simulated annealing algorithm plays an important role in local optimum convergence. Case study shows that the algorithm is efficient, and it not only gets optimal location results quickly and accurately, but also clearly gets the afflicted points that are covered by the selected facilities. So the model and the algorithm have strong practicability.

The derivative problems from the classical shortest path problem (SPP) are becoming more and more important in real life[1]. The dissimilar shortest paths problem is a typical derivative problem. In Vehicles Navigation System(VNS),it is necessary to provide drivers alternative paths to select. Usually, the path selected is a dissimilar path to the jammed path. In fact, ”dissimilar” is fuzzy. Considering traffic and transportation networks in this paper, we put forward to the definition of dissimilar paths measure that takes into account the decision maker's preference on both the road sections and the intersections. The minimum model is formulated in which not only the length of paths but also the paths dissimilar measure is considered. And a genetic algorithm also is designed. Finally, we calculate and analyze the dissimilar paths in the traffic network of the middle and east districts of Lanzhou city in P.R. of China by the method proposed in this paper.