Urban transportation network design and traffic control problems fall within the scope of infrastructural engineering sciences which become increasingly more important in ever-growing societies of today. In highly populated old cities where establishing new links are facing many human-related, social, economic, and political problems, a workaround for addressing traffic problems is to expand the capacity of existing links, so as to not only control the traffic, but also reduce the urban environmental pollutions caused by vehicles stuck in traffic and decrease the time wasted in traffic to accelerate routines of the society. In the present research, an urban transportation network design model is presented with the aim of enhancing travel time reliability by expanding the capacity of existing network links at minimum possible cost. A significant assumption taken in the present study is that demands in normal condition and peak traffic hours are treated separately, so as to prevent possible problems by congestion management. In the present study, the uncertainty associated with demand for travel, travel time, and the flow passing through different links are taken into consideration. Travel time reliability calculations are carried out assuming that the demand for travel and travel time follow lognormal distributions. In order to solve this bi-level model, particle swarm optimization algorithm was used. Incorporation of the inertial coefficients dynamics, personal learning, and communal learning into the algorithm contributes to the convergence of this algorithm for solving the bi-level model.

城市交通网络设计和交通控制问题属于基础工程科学的范畴，而基础工程科学在当今不断发展的社会中变得越来越重要。在人口稠密的老城市中，建立新链接面临许多与人类有关的，社会，经济和政治问题，解决交通问题的一种解决方法是扩大现有链接的容量，从而不仅控制交通，而且减少因车辆阻塞而造成的城市环境污染，并减少浪费的时间，以加速社会生活。在本研究中，提出了一种城市交通网络设计模型，旨在通过以最小可能的成本扩展现有网络链接的容量来提高旅行时间的可靠性。本研究中的一个重要假设是，正常情况下的需求和交通高峰时段的需求要分开处理，以防止拥塞管理可能引起的问题。在本研究中，考虑了与旅行需求，旅行时间以及通过不同链接的流量相关的不确定性。假设旅行和旅行时间的需求服从对数正态分布，则进行旅行时间可靠性计算。为了解决该双层模型，使用了粒子群优化算法。将惯性系数动力学，个人学习和公共学习纳入算法有助于该算法收敛于求解双层模型。