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Synthesizing neighborhood preferences for automated vehicles
Transportation Research Part C: Emerging Technologies ( IF 8.3 ) Pub Date : 2020-09-17 , DOI: 10.1016/j.trc.2020.102774
Wenwen Zhang , Kaidi Wang , Sicheng Wang , Zhiqiu Jiang , Andrew Mondschein , Robert B. Noland

Automated Vehicles (AVs) have gained substantial attention in recent years as the technology has matured. Researchers and policymakers envision that AV deployment will change transportation, development patterns, and other urban systems. Researchers have examined AVs and their potential impacts with two methods: (1) survey-based studies of AV preferences and (2) simulation-based estimation of secondary impacts of varied AV deployment strategies, such as Shared AVs (SAVs) and Privately-owned AVs (PAVs). While the preference survey literature can inform AV simulation studies, preference study results have so far not been integrated into simulation-based research. This lack of integration stems from the absence of data that measure preferences towards PAVs and SAVs at the neighborhood level. Existing preference studies usually investigate adoption likelihood without collecting appropriate information to link preferences to precise locations or neighborhoods. This study develops a microsimulation approach, incorporating machine learning and population synthesizing, to fill this data gap, leveraging a national AV perception survey (NAVPS) and the latest National Household Travel Survey (NHTS) data. The model is applied to San Francisco, CA, and Austin, TX, to test the concept. We validate the proposed model by comparing the spatial distributions of synthesized ride-hailing users and observed ride-hailing trips. High correlations between our synthesized user density and empirical trip distributions in two study areas, to some extent, verify our proposed modeling approach.



中文翻译:

综合自动车辆的邻里偏好

随着技术的成熟,近年来自动驾驶汽车(AVs)受到了广泛关注。研究人员和政策制定者预想,视听设备的部署将改变交通,发展模式和其他城市系统。研究人员使用两种方法检查了AV及其潜在影响:(1)基于调查的AV偏好研究,以及(2)基于模拟的各种AV部署策略(如共享AV(SAV)和私人拥有)的次级影响估计AV(PAV)。虽然偏好调查文献可以为AV模拟研究提供信息,但到目前为止,偏好研究结果尚未集成到基于模拟的研究中。这种缺乏整合的原因是缺乏在邻域水平上衡量对PAV和SAV偏好的数据。现有的偏好研究通常会在不收集适当信息以将偏好与精确的位置或邻居联系起来的情况下,调查采用的可能性。这项研究利用国家AV感知调查(NAVPS)和最新的National Household Travel Survey(NHTS)数据,开发了一种结合机器学习和人口综合的微观模拟方法,以填补这一数据空白。该模型被应用于加利福尼亚州旧金山和德克萨斯州奥斯汀,以测试该概念。我们通过比较综合叫车用户的空间分布和观察到的叫车旅行来验证提出的模型。我们在两个研究领域中的综合用户密度与经验行程分布之间的高度相关性在一定程度上验证了我们提出的建模方法。

更新日期:2020-09-18
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