ABSTRACT

Transportation is a significant issue due to providing people to participate in human activities. Due to an increase in population, the need for transportation has also been increased. Therefore, more traffic is visible on streets that produce more issues related to mobility like noise pollution, air pollution, and accidents. This study pays attention to an impressive transit network design in urban areas. Because of the NP-hard nature of this problem, a shark smell optimisation (SSO) algorithm based on fuzzy logic is employed. A developed system is utilised to produce, optimise, and analyse frequencies and routes of transit in the level of a network. Its target is maximising the direct travellers per unit length, i.e., subject to route length, direct traveller density, and nonlinear rate constraints (a route length ratio to the shortest road interval between the beginning and destination). Since designing an urban transport network issue is in heterogeneous environments is involved, this article provides a new method for lowering the feasible urban travel time, the urban traffic, and the feasible urban travel cost using a well-known SSO algorithm. According to the results, the proposed method has higher efficiency compared to the previous methods. In addition, the results showed that the proposed technique offers fewer transfers and travel time.

摘要

由于提供人们参与人类活动，交通是一个重要问题。由于人口的增加，对交通的需求也增加了。因此，街道上可见更多的交通，从而产生更多与流动性相关的问题，如噪音污染、空气污染和事故。本研究关注城市地区令人印象深刻的交通网络设计。由于该问题的 NP 难性质，采用了基于模糊逻辑的鲨鱼气味优化 (SSO) 算法。开发的系统用于在网络级别生成、优化和分析公交频率和路线。其目标是最大化单位长度的直接旅客，即受路线长度、直接旅客密度、和非线性速率约束（路线长度与起点和目的地之间最短道路间隔的比率）。由于设计城市交通网络的问题涉及异构环境，本文提供了一种使用著名的 SSO 算法降低可行城市出行时间、城市交通量和可行城市出行成本的新方法。结果表明，与以往的方法相比，该方法具有更高的效率。此外，结果表明，所提出的技术提供了更少的换乘和旅行时间。以及使用著名的 SSO 算法的可行城市旅行成本。结果表明，与以往的方法相比，该方法具有更高的效率。此外，结果表明，所提出的技术提供了更少的换乘和旅行时间。以及使用著名的 SSO 算法的可行城市旅行成本。结果表明，与以往的方法相比，该方法具有更高的效率。此外，结果表明，所提出的技术提供了更少的换乘和旅行时间。