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Minimizing the total travel time with limited unfairness in traffic networks

机译:最大限度地减少交通网络中的有限不公平的旅行时间

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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. (C) 2020 The Author(s). Published by Elsevier Ltd.
机译:最近开发的技术急剧地改变了流动性。自主和交互式车辆能够协调旅行车辆的SAT-NAV设备,其旨在指派具有消除拥塞的路径,并且更一般地减少交通网络中的总旅行时间。在本文中,我们解决了找到流量分配的问题,以最小化网络上的总旅行时间,同时保证具有相同原点和目的地的用户的路径具有相似的路径遍历遍历时间。虽然先前的方法已经确定了合格的路径先验,但我们提出了两个混合整数非线性编程模型,以及它们的混合整数线性近似,该模拟线性近似值识别满足所需的公平程度的路径,同时最小化网络上的总旅行时间。采用的不公平措施的两个模型不同。计算结果表明,网络中所花费的总旅行时间非常接近最低可能,即由系统最佳解决方案而获得的,同时保证每个用户的经验性不足的较低水平。还提出了一种启发式算法,其显示为产生高质量的解决方案。 (c)2020提交人。 elsevier有限公司出版

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