Search

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

How to provide a harmonious and fair competition environment for trade activities depends on the effective management of government departments and organizations. How to play a real protective role in trade activities requires the optimization of government functions and the enhancement of economic management capabilities. Therefore, timely forecasting changes in import and export trade and formulating targeted preferential policies are of great significance for evaluating the development of the national market economy, promoting the development of the national economy, and further enhancing the country's economic capacity. This chapter also establishes an early warning model of Shenzhen's import and export transactions based on GMDH network. On the basis of expounding the particle swarm optimization algorithm and GMDH algorithm, the optimization mode, method, and process of GMDH network based on particle swarm optimization are also expounded. First of all, in China's domestic macroeconomic environment, national policy environment indicators and foreign macroeconomic environment indicators, eight indexes related to international import and export transactions are selected, and the autocorrelation test and main component analysis of the indexes are carried out. Finally, the simulation results show that compared with a single GMDH network, the GMDH neural network after particle swarm optimization can get better prediction conclusions. The research results of this paper show that the GMDH network can more accurately predict the trend of Shenzhen's international import and export transactions because of its adaptive structure, creating a new way for international import and export transaction forecasting. [ABSTRACT FROM AUTHOR]

With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins' algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy. [ABSTRACT FROM AUTHOR]