Review on impact of autonomous driving on travel behaviors
Article Text (Baidu Translation)
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摘要: 从小汽车出行总需求、出行方式选择、在途时间利用三方面梳理了自动驾驶影响下的出行行为研究现状，分析了用于研究自动驾驶对出行行为影响的数据基础与研究方法，总结了影响自动驾驶环境下出行方式选择的关键因素，指出了出行行为研究存在的问题和未来发展方向。研究结果表明：出行总需求的相关研究主要关注当前服务不足人口的潜在出行，大多通过需求假设分析潜在的变化，在假设的可靠性和结果的准确度方面还存在不足；出行方式选择的相关研究显示车辆服务和出行属性、社会人口和家庭属性、出行习惯属性、居住地和环境属性、个人心理和偏好属性等是影响出行方式选择的关键因素，考虑到不同的研究对象、场景设计与分析方法，性别、年龄、持有驾照、家庭结构等因素对出行行为的具体影响还有待进一步检验；人们对自动驾驶时代在途时间利用的方式和受益程度的认知存在较大的不确定性与异质性，亟需理论模型来进一步讨论潜在的时间利用变化；基于自动驾驶对出行行为影响相关研究的局限性，提出了建立自动驾驶汽车的规范化描述和丰富数据采集方式，开展横向与纵向对比研究，加强各影响因素异质性的考量，辨析自动驾驶时代各类出行行为间的相互影响机制的改进方向。Abstract: The research status of travel behaviors under the influence of autonomous driving was analyzed from three aspects: vehicle travel demand, travel mode choice, and travel time utilzation. The data foundations and methodologies of existing studies on the impact of autonomous driving on travel behaviors were analyzed. The key factors affecting the choice of travel mode in the autonomous driving environment, existing challenges, and future development directions were discussed. Research results indicate that the studies on vehicle travel demand estimation primarily focus on the potential travel of underserved population and usually analyze the potential demand changes through assumptions, which have some deficiencies in terms of the reliability of the assumptions and the accuracy of the results. The studies on travel mode choice show that vehicle service and travel attributes, social demographic and family attributes, travel habits attributes, residential and environmental attributes, personal psychology and preference attributes are key influencing factors. Taking into account of different research objects, scenario design, and analysis methods, the influence of the factors such as gender, age, vehicle license holding, and family structure on travel behaviors remains to be examined. There is great uncertainty and heterogeneity in people's perceptions of the ways and benefits of the use of travel time in the era of autonomous driving, and there is a great need for theoretical models to further discuss the potential change of the use of travel time. Based on the limitations of existing studies on the impact of autonomous driving on travel behaviors, several future improvement directions are provided, including establishing standardized description of autonomous vehicles, enriching data collection methods, carrying out horizontal and longitudinal comparative studies, strengthening the consideration of the heterogeneity of influencing factors, and identifying the interaction mechanism among various travel behaviors.
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图 1 影响出行方式选择的关键因素与相关代表性文献
Figure 1. Key factors affecting travel mode choice and related representative literatures
图 2 出行方式选择代表性文献所考虑的关键因素的分布
Figure 2. Distribution of key factors considered in representative literatures on travel mode choice
图 3 代表性文献的发表时间和研究地区分布
Figure 3. Distributions of publication time and study location of representative literatures
表 1 自动驾驶对小汽车出行总需求的影响
Table 1. Impact of autonomous driving on total vehicle travel demand
文献 数据基础 研究方法 主要结论 Harper等[3] 2009 NHTS 需求假设 19岁及以上人群VMT增加2%~14% Brown等[11] 2009 NHTS 需求假设 16~85岁人群VMT增加40% Schoettle等[12-13] 自拟问卷调查和2009 NHTS 需求假设 18~39岁人群VMT增加10.6% Wadud等[14] 2009 NHTS 弹性分析与需求假设 出行费用减少使整体VMT增加4%(低等级AV情景)或60%(L4级AV情景)，由新增出行人口带来的出行使VMT增加2%~10% Harb等[15] 受访者 描述统计 Pudāne等[16] 焦点小组成员 定性分析 
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表 2 关于出行方式选择代表性文献的分类
Table 2. Classification of representative literatures on travel mode choice
分类项目 类别 文献数量 文献比例 选择集的组成 全部为小汽车出行方式 7 0.26 包括各类交通方式 20 0.74 选择场景的选取 日常出行 10 0.37 最后一公里 3 0.11 长距离出行 2 0.07 通勤出行 7 0.26 休闲出行 3 0.11 其他 8 0.30 特定问题的关注 在途利用方式 3 0.11 交通管理政策 1 0.04 有无驾驶人 1 0.04 监控方式 1 0.04 分析文献总数 27
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表 3 关于出行方式选择代表性文献的数据基础与研究方法
Table 3. Data foundations and research methodologies of representative literatures on travel mode choice
文献 调查方式 研究对象 选择场景的来源 研究方法 Saeed等[17] 在线问卷调查 1 922名美国民众(成年人，以驾驶人或者乘客的身份使用汽车去上班或上学) 虚拟场景 随机参数Logit模型 Haboucha等[19] 在线问卷调查 721名居住在以色列和北美的民众 虚拟场景 因子分析与嵌套Logit Kernel模型 Etzioni等[20] 在线问卷调查 1 669名来自6个不同国家的民众 基于受访者陈述的一段出行 混合多项Logit模型 Wicki等[21] 在线问卷调查 773名瑞士沙夫豪森州民众(了解自动驾驶巴士路测) 现实生活中的一段出行 综合选择与潜在变量模型 Winter等[22] 在线问卷调查 796名荷兰民众 虚拟场景 带有潜在类的嵌套Logit模型 Yap等[23] 在线问卷调查 761名荷兰民众(年满18岁，每月至少出行2次) 虚拟场景 因子分析与混合Logit模型 Cai等[24] 在线问卷调查和实地问卷调查 1 477名新加坡民众 现实生活中的一段出行 Logit Kernel模型 Gurumurthy等[25] 在线问卷调查 2 588名美国民众 其他 多项Logit模型 Krueger等[26] 在线问卷调查 512名悉尼的通勤者(成年，必须有工作或正在上学；每周至少通勤3次；必须是租房或有住房抵押贷款) 虚拟场景 混合多项Logit模型 Ashkrof等[27] 在线问卷调查 663名荷兰民众(年满18岁，持有驾照，每月至少使用一次汽车) 虚拟场景 探索性因子分析和带有面板效应的混合Logit模型 Pakusch等[28] 定性访谈 25名德国的千禧一代 其他 专题分析 Booth等[29] 在线问卷调查 1 624名澳大利亚民众(年龄16岁及以上) 其他 普通最小二乘多元回归分析 Carrese等[30] 在线问卷调查 201名罗马民众 虚拟场景 多项Logit模型 Abe等[31] 在线问卷调查 1 962名日本民众(20~74岁) 基于受访者陈述的一段出行 面板混合有序Logit (OL) 模型 黄浩[32] 在线问卷调查和实地问卷调查 558名中国民众 虚拟场景 多指标多因子(MIMIC) 模型与混合Logit模型 Piatkowski[33] 在实地体验前后填写在线问卷 551名内布拉斯加州民众 其他 二元Logistic回归模型 Krueger等[34] 在线问卷调查 435名澳大利亚民众 基于受访者陈述的一段出行 k-means聚类法与混合Logit模型 Wang等[35] 在线问卷调查 1 142名新加坡民众(20~85岁) 基于受访者陈述的一段出行 回归分析与混合Logit模型 Gao等[36] 在线问卷调查 502名成年美国民众 虚拟场景 混合Logit模型 Abe[37] 在线问卷调查 2 300名日本民众(0~74岁，居住在距东京都最近的火车站1~5 km的范围内) 基于受访者陈述的一段出行 多项Logit模型和混合Logit模型 Pakusch等[38] 在线问卷调查 302名德国民众 其他 Bradley-Terry-Luce (BTL)模型 Kim等[39] 邮寄纸质版问卷并提供在线问卷链接 2 890名美国佐治亚州民众 虚拟场景 因子分析和潜在类聚类分析 Zhou等[40] 在线问卷调查 1 433名澳大利亚民众 基于受访者陈述的一段出行 随机参数Logit模型 Alhajyaseen等[41] 在线问卷调查 315名卡塔尔民众 虚拟场景 多项Logit模型 Etzioni等[42] 在线问卷调查 714名成年以色列民众 虚拟场景 探索性因子分析与MIMIC模型和潜在类多项Logit模型 Sweet[43] 在线问卷调查 393名中国民众 虚拟场景 k-means聚类法与混合Logit模型 姚荣涵等[44] 在线问卷调查 1 684名住在大多伦多和汉密尔顿地区的民众(有工作，通勤上班以及家庭保有汽车) 虚拟场景 混合Logit模型
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1. Impact of autonomous driving on total travel demand by sedan cars
2. Classification of representative literature on travel mode choice
3. Data bases and research methods of representative literature on travel mode choice
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