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首页> 外文期刊>Transportation research >Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility
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Solving the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP): A model to explore the impacts of self-driving vehicles on urban mobility

机译：解决用户最佳私人拥有的自动驾驶汽车分配问题（UO-POAVAP）：探索自动驾驶汽车对城市机动性影响的模型

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摘要

Interest in vehicle automation has been growing in recent years, especially with the very visible Google car project. Although full automation is not yet a reality there has been significant research on the impacts of self-driving vehicles on traffic flows, mainly on interurban roads. However, little attention has been given to what could happen to urban mobility when all vehicles are automated. In this paper we propose a new method to study how replacing privately owned conventional vehicles with automated ones affects traffic delays and parking demand in a city. The model solves what we designate as the User Optimum Privately Owned Automated Vehicles Assignment Problem (UO-POAVAP), which dynamically assigns family trips in their automated vehicles in an urban road network from a user equilibrium perspective where, in equilibrium, households with similar trips should have similar transport costs. Automation allows a vehicle to travel without passengers to satisfy multiple household trips and, if needed, to park itself in any of the network nodes to benefit from lower parking charges. Nonetheless, the empty trips can also represent added congestion in the network. The model was applied to a case study based on the city of Delft, the Netherlands. Several experiments were done, comparing scenarios where parking policies and value of travel time (VTT) are changed. The model shows good equilibrium convergence with a small difference between the general costs of traveling for similar families. We were able to conclude that vehicle automation reduces generalized transport costs, satisfies more trips by car and is associated with increased traffic congestion because empty vehicles have to be relocated. It is possible for a city to charge for all street parking and create free central parking lots that will keep total transport costs the same, or reduce them. However, this will add to congestion as traffic competes to access those central nodes. In a scenario where a lower VTT is experienced by the travelers, because of the added comfort of vehicle automation, the car mode share increases. Nevertheless this may help to reduce traffic congestion because some vehicles will reroute to satisfy trips which previously were not cost efficient to be done by car. Placing the free parking in the outskirts is less attractive due to the extra kilometers but with a lower VTT the same private vehicle demand would be attended with the advantage of freeing space in the city center. (C) 2016 Elsevier Ltd. All rights reserved.
机译：近年来，对汽车自动化的兴趣一直在增长，尤其是在非常引人注目的Google汽车项目中。尽管尚未实现完全自动化，但对自动驾驶车辆对交通流量（主要是对城市间道路）的影响进行了大量研究。但是，当所有车辆都实现自动化时，很少关注城市交通的状况。在本文中，我们提出了一种新方法来研究如何用自动车辆代替私人常规车辆如何影响城市的交通延误和停车需求。该模型解决了我们称为“用户最佳私人拥有自动驾驶汽车分配问题”（UO-POAVAP）的问题，该问题从用户平衡的角度动态分配城市道路网络中其自动驾驶汽车的家庭出行，在这种情况下，平衡出行相似的家庭应该有类似的运输费用。自动化使车辆可以在没有乘客的情况下行驶，从而满足多次家庭出行的需要，并且如果需要，可以将其停放在任何网络节点中，以从较低的停车费用中受益。但是，空行程也可能表示网络中增加了拥塞。该模型已应用于基于荷兰代尔夫特市的案例研究。完成了一些实验，比较了更改停车政策和旅行时间（VTT）值的方案。该模型显示出良好的均衡收敛性，相似家庭的一般出行费用之间的差异很小。我们可以得出结论，车辆自动化降低了一般性的运输成本，满足了更多的乘车旅行需求，并且由于必须搬移空车而导致交通拥堵加剧。一个城市可以为所有路边停车收费，并创建免费的中央停车场，以使总运输成本保持不变或降低。但是，随着流量竞争访问这些中央节点，这将加剧拥塞。在旅行者体验到较低的VTT的情况下，由于增加了车辆自动化的舒适性，汽车模式所占份额增加了。然而，这可能有助于减少交通拥堵，因为一些车辆将改道以满足以前无法通过汽车实现成本效益的出行方式。由于额外的公里数，在郊区设置免费停车位的吸引力较小，但由于VTT较低，因此可以满足私人车辆需求，同时可以释放市中心的空间。 （C）2016 Elsevier Ltd.保留所有权利。

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