• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Arabian Journal for Science and Engineering >Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms
【24h】

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

机译：进化算法计算总线快速传输站的最佳间距和密度

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stationsas well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed andoptimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm(GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costsnamely the operator’s cost, i.e., related to costs on service provider’s end, and the user’s cost, i.e., related to costs on commuters’end. A functional numerical example with the commuters’ demand is worked out by minimizing the cost function, whichdemonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergencerate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysiskeeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computationalexperiments reveal that the optimal cost is substantially affected by the variations in the commuters’ demand, commuters’walking speed, and value of the users’ access and in-vehicle time. On the contrary, the acceleration/deceleration delays at abus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost.
机译：在本研究中，开发了一种可行的机制来确定总线快速传输（BRT）站的最佳数量以及沿着服务走廊的各自地点。为了实现这一点，开发了一种数学模型通过使用三种不同的进化算法优化，即粒子群优化（PSO），遗传算法（GA）和差分进化（DE），并进行结果。总成本函数由两个主要成本组成即经营者的成本，即与服务提供商结束的成本有关，以及用户的成本，即与通勤者的成本相关结尾。通过最小化成本函数来解决具有通勤者的需求的功能数值示例，从而使成本函数最小化展示了框架的适用性。在我们的案例研究中，PSO在收敛的基础上淘汰了GA和DE速度。由于我们的工作证明了PSO的稳健性，而与GA和DE相比，我们进行了灵敏度分析保持PSO作为我们的基准算法，以研究各种参数对最佳成本的影响。计算实验表明，最佳成本基本上受通勤者需求，通勤者的变化影响步行速度和用户访问和车载时间的价值。相反，加速/减速延迟公交车站，公交车运营成本和前往对最佳成本产生不足的影响。

著录项

  • 来源
    《Arabian Journal for Science and Engineering》 |2021年第5期|5179-5202|共24页
  • 作者

    Muhammad Umair; Sabih ur Rehman; Aimal Sohail; Afaq Khattak;

  • 作者单位

    Department of Civil Engineering Faculty of Engineeringand Technology International Islamic University SectorH‑10 Islamabad Pakistan;

    Department of Civil Engineering Faculty of Engineeringand Technology International Islamic University SectorH‑10 Islamabad Pakistan;

    Department of Civil Engineering Faculty of Engineeringand Technology International Islamic University SectorH‑10 Islamabad Pakistan;

    Department of Civil Engineering Faculty of Engineeringand Technology International Islamic University SectorH‑10 Islamabad Pakistan;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Bus rapid transit; Particle swarm optimization; Differential evolution; Genetic algorithm;

    机译：巴士快速运输;粒子群优化;差分演变;遗传算法;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号