An Urban Bus Network Generation Algorithm Based on Particle Swarm Optimization and Force Field Properties

Abstract

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.

Soft computing in business: exploring current research and outlining future research directions

“…Topic 14- “Logistics System Optimization” deals with the research exploiting soft computing for traffic optimization (Zhang et al ., 2021), efficient routing of vehicles (Haixiang et al ., 2021; Wang et al. , 2021b; Xu and Lyu, 2021), traffic flow prediction (Wang et al ., 2021), path optimization in urban transportation networks (Liu et al ., 2021a, b) and sustainable reverse logistics network design (Hashemi, 2021), urban transportation network management (Chen et al ., 2022e).…”

“…DNN provides high accuracy, but its black box exacerbates its complexity (Geetha and Deepa, 2022; El Bilali et al ., 2022). Evolutionary computation can handle multi-objective formulations, but it requires a relatively large amount of computational time (Chen et al ., 2022a, b, c, d, e, f, g, h). FL can deliver quick, streamlined and effective responses, but stability analysis cannot be performed using FL, and it is difficult to predict the outcome in advance (Mastrocinque et al ., 2022).…”

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