Transportation Network Design Problem (TNDP) includes making the right choices possible when deciding a collection of design criteria to develop a current transportation network in response to rising traffic demand. Traffic congestion, higher maintenance and fuel prices, delays, accidents, and air emissions stem from the general rise in flow volume. Because of the NP-hard nature of this problem, a hidden Markov model and an Equilibrium Optimizer (EO) are employed in this paper to solve it. Each particle (solution) behaves as a search agent in EO, with its position. To reach the equilibrium condition, the search agents change their focus at random regarding the best-so-far approaches, including equilibrium candidates. A well-defined "generation rate" concept has been shown to elevate EO's capacity in avoiding local minima. This article provides a new method to lower the feasible travel time and the public travel cost using the hidden Markov model and EO algorithm. The suggested method's performance was compared to the performance of other algorithms on a test network. The related numerical outcomes show that it is more effective.

交通网络设计问题 (TNDP) 包括在决定一系列设计标准以开发当前交通网络以响应不断增长的交通需求时做出正确的选择。交通拥堵、更高的维护和燃料价格、延误、事故和空气排放源于流量的普遍增加。由于该问题的 NP 难性质，本文采用隐马尔可夫模型和平衡优化器 (EO) 来解决该问题。每个粒子（溶液）在 EO 中都表现为搜索代理，具有其位置。为了达到平衡条件，搜索代理随机改变他们对迄今为止最佳方法的关注，包括平衡候选。一个明确定义的“生成率”概念已被证明可以提高 EO 避免局部最小值的能力。本文提供了一种利用隐马尔可夫模型和EO算法降低可行出行时间和公共出行成本的新方法。建议方法的性能与测试网络上其他算法的性能进行了比较。相关的数值结果表明它更有效。