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Differentiating and modeling the installation and the usage of autonomous vehicle technologies: A system dynamics approach for policy impact studies

机译：区分和建模自主车辆技术的安装和用法：政策影响研究的系统动力学方法

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Existing research that forecasts market penetration of installed connected and autonomous vehicle (AV) technologies is often confused with the traffic composition in roadway networks. Users may override AV mode due to arrival time pressure, facility constraint (e.g., "I will have to make a U-turn a mile away if I do not cross the solid double-yellow lines here"), drug and alcohol influence, pleasure, envy (e.g., "why the front car can surpass that slow truck but I can't?"), insufficient law enforcement, driving culture, media and public sentiment, etc. Therefore, the installation and the usage of AV technologies should not be instantaneously assumed ignorable in planning and policy studies. This paper is dedicated to clarifying this confusion by demonstrating that ignoring the difference between the installation and the usage of AV technologies might lead to systematic bias in evaluating policy and investment decisions. Through a system dynamics (SD) model, the complex interactions of relevant factors are captured so that the mixed traffic condition influences traffic law enforcement adjustment effort and system investment decisions, which, in turn, influence the AV technology usage share and the system performance. The case study applies to the greater Washington, D.C. area for demonstrating the feasibility and advantages of the proposed model and for studying policy implications. This paper does not attempt to forecast; instead, it proposes a modeling framework for studying the conditions under which differentiating the installation and the usage of AV technologies might be critical in forecasting the traffic composition trend and system performance for public policy and investment decisions.
机译：预测安装连接和自主车辆（AV）技术的市场渗透的现有研究通常与道路网络中的交通构图混淆。由于到达时间压力，设施约束（例如，“如果我在这里不穿过坚实的双黄线”），吸毒和酒精影响，嫉妒（例如，“为什么前车可以超越那个慢速卡车，但我不能？”），执法不足，驾驶文化，媒体和公众情绪等。因此，安装和使用AV技术的用法不应该在规划和政策研究中瞬间假设忽略。本文致力于通过证明忽略安装与AV技术的使用之间的差异来阐明这种混淆可能导致评估政策和投资决策的系统偏见。通过系统动态（SD）模型，捕获了相关因素的复杂交互，使混合交通状况影响了交通执法调整工作和系统投资决策，反过来，这反过来影响AV技术使用份额和系统性能。案例研究适用于更大的华盛顿，D.C.领域，用于证明所提出的模型的可行性和优势以及研究政策影响。本文不试图预测;相反，它提出了一种用于研究区分安装的条件的建模框架，并且AV技术的使用可能是预测公共政策和投资决策的交通构成趋势和系统绩效至关重要。

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