Abstract Recently developed technologies are changing mobility dramatically. Autonomous and interactive vehicles enable a coordination of the sat-nav devices of traveling vehicles aimed at assigning paths with the goal of eliminating congestion and, more in general, of reducing the total travel time in traffic networks. In this paper we tackle the problem of finding a traffic assignment that minimizes the total travel time on a network, while guaranteeing that the paths of users with the same origin and destination have similar path traversal times. While previous approaches have identified the eligible paths a priori, we propose two mixed integer nonlinear programming models, along with their mixed integer linear approximations, that identify paths that satisfy the desired level of fairness while minimizing the total travel time on the network. The two models differ for the unfairness measure adopted. Computational results show that the total travel time spent in the network is very close to the minimum possible, that is the one obtained by the system optimum solution, while guaranteeing to each user a very low level of experienced unfairness. A heuristic algorithm is also proposed which is shown to generate high quality solutions.

中文翻译：

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以有限的交通网络不公平性来最小化总旅行时间

摘要 最近开发的技术正在极大地改变移动性。自主和交互式车辆能够协调行驶车辆的卫星导航设备，旨在分配路径，以消除拥堵，更一般地说，减少交通网络中的总旅行时间。在本文中，我们解决了寻找交通分配的问题，以最小化网络上的总旅行时间，同时保证具有相同起点和终点的用户的路径具有相似的路径遍历时间。虽然以前的方法已经先验地确定了合格的路径，但我们提出了两个混合整数非线性规划模型，连同它们的混合整数线性近似，它们识别满足所需公平水平的路径，同时最小化网络上的总旅行时间。两种模式因采用的不公平措施而异。计算结果表明，在网络中花费的总旅行时间非常接近可能的最小值，即系统最优解所获得的时间，同时保证每个用户体验到的不公平程度非常低。还提出了一种启发式算法，该算法被证明可以生成高质量的解决方案。