Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

自动驾驶汽车 (AV) 将改变交通供应并影响出行需求。为了评估这些变化，需要扩展现有的旅行需求模型。本文提出了基于四步算法将自动驾驶汽车的特征整合到传统宏观出行需求模型中的方法。它讨论了两个模型扩展。第一个扩展允许通过分配取决于道路类型和车辆能力的特定乘用车单元因素来合并 AV 对交通流性能的影响。第二个扩展使旅行需求模型能够计算由于主动驾驶时间减少而对旅行时间的不同感知引起的需求变化。所提出的方法应用于区域宏观旅行需求模型的用例。基本假设是 AV 被认为是高度自动化的，但不是完全自动化的，并且在部分行程中仍然需要司机。模型结果表明，第一代 AV 可能相当谨慎，可能会降低交通性能。进一步开发的 AV 将提高网络某些部分的性能。随着主动驾驶时间的减少，汽车将变得更具吸引力，从而导致向汽车的模式转变。这两种情况都会导致花费的时间和旅行距离增加。汽车将变得更具吸引力，从而导致向汽车的模式转变。这两种情况都会导致花费的时间和旅行距离增加。汽车将变得更具吸引力，从而导致向汽车的模式转变。这两种情况都会导致花费的时间和旅行距离增加。