人非混行状态下行人心理边界的影响因素分析

汤天培; 袁泉; 袁美宁; 陈志宇; 林欣蓉

doi:10.3963/j.jssn.1674-4861.2024.04.015

人非混行状态下行人心理边界的影响因素分析

doi: 10.3963/j.jssn.1674-4861.2024.04.015

汤天培^{1, 2,},
袁泉^3, ,,
袁美宁¹,
陈志宇¹,
林欣蓉¹

1.
南通大学交通与土木工程学院江苏南通 226019
2.
东南大学交通学院南京 211189
3.
清华大学车辆与运载学院北京 100084

基金项目:

国家自然科学基金项目 72101128

国家自然科学基金项目 52472360

中国博士后科学基金面上项目 2023M730560

详细信息

作者简介:
汤天培（1987—），博士，副教授. 研究方向：交通安全、智能汽车人机交互等. E-mail: tangtianpei@ntu.edu.cn

通讯作者:
袁泉（1974—），博士，教授. 研究方向：交通安全、智能汽车等. E-mail: yuanq@tsinghua.edu.cn

中图分类号: X910
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- 被引次数: 0
出版历程
- 收稿日期: 2023-09-07
- 网络出版日期: 2024-11-25

Determinants of Pedestrians' Psychological Boundary in the Coexistence of Pedestrian and Non-motorized Vehicles

1.
School of Transportation and Civil Engineering, Nantong University, Nantong 226019, Jiangsu, China
2.
School of Transportation, Southeast University, Nanjing 211189, China
3.
School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China

摘要

摘要: 探究人非混行状态下的行人心理边界影响机制，有助于从交通设计、交通管理等维度针对性地提高人非共板道路上行人的出行安全性和舒适性。为克服现有研究仅考虑行人自身人际空间范围期望的不足，根据心理包络理论，从行人期望的安全活动边界（subject mental envelope，SME）和行人期望非机动车的约束活动边界（object mental envelope，OME）2个维度，重构人非混行状态下的行人心理边界。通过引入感知威胁、感知亲密和个人特征，新增优先权作为潜在影响因素，构建人非混行状态下的行人心理边界影响机制模型。采用偏最小二乘法结构方程分析进行模型适配性检验和路径分析，模型的SRMR值为0.035，SME和OME的R²分别为0.728和0.773，表明模型拟合水平可接受，且解释力较强。研究结果表明：感知威胁和感知亲密对OME的影响大于对SME的影响，表明当行人认为非机动车有潜在威胁且不友好时，其更期望能约束非机动车的骑行范围；优先权对OME正向影响程度大于SME，表明当行人认为自己的优先权高于非机动车时，其更期望进一步约束非机动车的活动边界；非机动车交通量仅显著影响OME；OME正向影响SME，表明扩展引入的OME可进一步解释行人对自身安全活动空间的需求；个人特征中性别和身高对SME和OME均有显著影响，而每周平均骑行次数仅显著影响OME。
- 交通安全 /
- 行人 /
- 心理边界 /
- 心理包络 /
- 结构方程模型
Abstract: Investigating the mechanisms influencing pedestrians' psychological boundary in the coexistence of pedestrians and non-motorized vehicles is crucial for improving pedestrian safety and comfort in traffic design and management. This study addresses the limitations of previous studies, which mainly focus on pedestrians'interpersonal spatial expectations. To this end, this study redefines psychological boundaries between pedestrians and non-motorized vehicles based on the mental envelop theory from the two key dimensions: the subject mental envelope (SME) anticipated by pedestrians and the object mental envelope (OME) imposed by pedestrians concerning non-motorized vehicles. Next, the proposed model incorporates perceived threat, perceived closeness, and personal characteristics, and priority as a potential influencing factor. The structural equation model with partial least squares is employed to assess model fit and conduct path analysis. The estimation results show that the SRMR value is 0.035, and R² values for SME and OME are 0.728 and 0.773, respectively, indicating good fit and strong explanatory power. Further, results show that perceived threat and closeness significantly affect both SME and OME, with stronger effects on OME, which suggests that pedestrians perceive non-motorized vehicles as a threat and demand stricter boundaries for their riding areas. Priority demonstrates a greater positive impact on OME than that on SME, suggesting that pedestrians, when feeling prioritized, expect stricter constraints on non-motorized vehicles. The traffic volume of non-motorized vehicles has a substantial impact on OME. OME also positively influences SME, suggesting that the expanded OME can further elucidate pedestrians' requirements for their safe-activity space. Notably, gender and height of a pedestrian significantly impact both SME and OME, while average weekly cycling frequency significantly affects OME exclusively.
- traffic safety /
- pedestrian /
- psychological boundary /
- mental envelope /
- structural equation model

HTML全文

图 1 人非共板道路

Figure 1. Shared sidewalk

下载: 全尺寸图片幻灯片

图 2 SME和OME的定义

Figure 2. Definition of SME and OME

下载: 全尺寸图片幻灯片

图 3 人非混行状态下SME和OME的强弱变化

Figure 3. The strength changes of SME and OME under mixed pedestrian and non-motor vehicle conditions

下载: 全尺寸图片幻灯片

图 4 行人心理边界影响机制模型

Figure 4. A influence mechanism model of pedestrian mental boundary

下载: 全尺寸图片幻灯片

图 5 结构方程模型结果

Figure 5. SEM results

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表 1 变量题项设计

Table 1. Item design for variables

潜变量	显变量	题项内容
安全活动边界SME	SME1	步行时，我不希望非机动车侵入我的个人空间。
安全活动边界SME	SME2	步行时，只要我的个人空间不被侵入，非机动车可以使用其余的道路空间。
约束活动边界OME	OME1	步行时，当我附近出现非机动车时，我希望非机动车能远离我或是停下来。
约束活动边界OME	OME2	步行时，我希望非机动车能被限制在离我很远的道路空间里，这样我可以自由地使用其余的道路空
感知威胁PT	PT1	遇到非机动车时，你会感到有压力吗？
	PT2	你认为非机动车比你强势吗？
	PT3	遇到非机动车时，你感到危险吗？
感知亲密PC	PC1	你认为非机动车友好吗？
感知亲密PC	PC2	你熟悉非机动车的行驶规律或是特点吗？

下载: 导出CSV

表 2 样本分布特征

Table 2. Respondent characteristics of the survey

个人特征	类型	数量	占比/%
性别	男	127	47.2
性别	女	142	52.8
	> 19~25	60	22.3
年龄/岁	> 25~40	67	24.9
年龄/岁	> 40~55	64	23.8
	> 55~65	63	23.4
	> 65	15	5.6
	≤160	52	19.3
身高/cm	> 160~170	75	27.9
身高/cm	> 170~180	78	29.0
	> 180	64	23.8
	≤5	97	36.1
每周平均骑行次数	> 5~10	119	44.2
	> 10	53	19.7
合计		269	100.0

下载: 导出CSV

表 3 信度和效度分析

Table 3. Reliability and validity testing

潜变量	显变量	标准化因子载荷	Cronbach's α	CR	AVE
SME	SME1	0.918	0.818	0.917	0.846
SME	SME2	0.922	0.818	0.917	0.846
OME	OME1	0.913	0.796	0.907	0.830
OME	OME2	0.909	0.796	0.907	0.830
	PT1	0.875
PT	PT2	0.880	0.860	0.915	0.781
	PT3	0.896
PC	PC1	0.914	0.809	0.913	0.840
PC	PC2	0.918	0.809	0.913	0.840

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表 4 区别效度检验

Table 4. Discriminant validity testing

潜变量	SME	OME	PT	PC
SME	0.920
OME	0.778^***	0.911
PT	0.847^***	0.812^***	0.884
PC	-0.818^***	-0.795^***	-0.837^***	0.916
注“：^*”为p < 0.001“；^”为p < 0.01；“^*”为p < 0.05；对角线为AVE值平方根。

下载: 导出CSV

表 5 模型路径分析

Table 5. Model path analysis

假设	路径关系			标准化路径系数	P值	假设是否成立
H1a	感知威胁	→	SME	0.359	0.000	是
H2a	感知亲密	→	SME	-0.268	0.000	是
H3a	优先权	→	SME	0.112	0.047	是
H4a	交通量	→	SME	0.013	0.442	否
H5a	性别	→	SME	0.099	0.047	是
H6a	年龄	→	SME	0.006	0.844	否
H7a	身高	→	SME	-0.092	0.047	是
H8a	每周平均骑行次数	→	SME	-0.017	0.532	否
H1b	感知威胁	→	OME	0.411	0.000	是
H2b	感知亲密	→	OME	-0.309	0.000	是
H3b	优先权	→	OME	0.227	0.000	是
H4b	交通量	→	OME	0.201	0.001	是
H5b	性别	→	OME	0.151	0.024	是
H6b	年龄	→	OME	0.002	0.955	否
H7b	身高	→	OME	-0.088	0.045	是
H8b	每周平均骑行次数	→	OME	-0.080	0.048	是
H9	OME	→	SME	0.131	0.029	是

下载: 导出CSV

参考文献(27)

[1]	任福田, 肖秋生, 薛宗惠. 城市道路与规划设计[M]. 北京: 中国建筑工业出版社, 1999. REN F T, XIAO Q S, XUE Z H. Urban road and planning design[M]. Beijing: China Building Industry Press, 1999. (in Chinese)
[2]	魏雯, 杜雨萌, 董傲然, 等. 基于CIDAS数据与集成学习的电动两轮车骑行者伤害致因分析[J]. 交通信息与安全, 2022, 40(2): 45-52, 62. doi: 10.3963/j.jssn.1674-4861.2022.02.006 WEI W, DU Y M, DONG A R, et al. An analysis of factors affecting injury of electric two-wheeler riders based on CIDAS data and ensemble learning[J]. Journal of Transport Information and Safety, 2022, 40(2): 45-52, 62. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2022.02.006
[3]	汤天培, 陈丰, 郭赟韬, 等. 基于强化敏感性理论的电动自行车风险骑行行为影响因素[J]. 交通信息与安全, 2021, 39 (3): 25-32. doi: 10.3963/j.jssn.1674-4861.2021.03.004 TANG T P, CHEN F, GUO Y T, et al. Influencing factors of electrical bikes' risky riding behaviors based on reinforcement sensitivity theory[J]. Journal of Transport Information and Safety, 2021, 39(3): 25-32. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.03.004
[4]	PHAM T Q, NAKAGAWA C, SHINTANI A, et al. Evaluation of the effects of a personal mobility vehicle on multiple pedestrians using personal space[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 2028-2037. doi: 10.1109/TITS.2014.2388219
[5]	DIAS C, IRYO-ASANO M, NISHIUCHI H, et al. Calibrating a social force based model for simulating personal mobility vehicles and pedestrian mixed traffic[J]. Simulation Modelling Practice and Theory, 2018, 87: 395-411. doi: 10.1016/j.simpat.2018.08.002
[6]	NISHIUCHI H, SATO T, ARATANI T, et al. An analysis of Segway behavior focusing on safety distance for pedestrians and gaze of riders[C]. 17^th ITS World Congress, Busan: TRIO, 2010.
[7]	MORALES Y, AKAI N, MURASE H. Personal mobility vehicle autonomous navigation through pedestrian flow: a data driven approach for parameter extraction[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid: IEEE, 2018.
[8]	NAKAGAWA C, IMAMURA K, SHINTANI A, et al. Simulations of the relationship between a personal mobility vehicle and pedestrians[C]. IEEE International Systems Conference SysCon, Vancouver: IEEE, 2012.
[9]	HASEGAWA Y, DIAS C, IRYO-ASANO M, et al. Modeling pedestrians' subjective danger perception toward personal mobility vehicles[J]. Transportation Research Part F: Traffic Psychology and Behaviour, 2018, 56: 256-267. doi: 10.1016/j.trf.2018.04.016
[10]	袁泉, 晏楠飞, 郝威. 基于心理安全距离的行人风险评价及预警算法研究[J]. 中国公路学报, 2022, 35(1): 109-118. YUAN Q, YAN N F, HAO W. Pedestrian risk assessment and early warning algorithm based on psychological safety distance[J]. China Journal of Highway and Transport, 2022, 35(1): 109-118. (in Chinese)
[11]	VASSALLO C, OLIVIER A H, SOUÈRES P, et al. How do walkers avoid a mobile robot crossing their way?[J]. Gait & Posture, 2017, 51: 97-103.
[12]	VASSALLO C, OLIVIER A H, SOUÈRES P, et al. How do walkers behave when crossing the way of a mobile robot that replicates human interaction rules?[J]. Gait & Posture, 2018, 60: 188-193.
[13]	QIAN M, JIANG J. COVID-19 and social distancing[J]. Journal of Public Health, 2020, 30: 259-261.
[14]	DE VOS J. The effect of COVID-19 and subsequent social distancing on travel behavior[J]. Transportation Research Interdisciplinary Perspectives, 2020, (5): 100121.
[15]	SUN C, ZHAI Z. The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission[J]. Sustainable Cities and Society, 2020, 62: 102390. doi: 10.1016/j.scs.2020.102390
[16]	ZOU L, YAI T. A proposal of envelope theorem on the mixed traffic of pedestrians and various mobilities[J]. Asian Transport Studies, 2022, (8): 100050.
[17]	HECHT H, WELSCH R, VIEHOFF J, et al. The shape of personal space[J]. Acta Psychologica, 2019, 193: 113-122. doi: 10.1016/j.actpsy.2018.12.009
[18]	IACHINI T, COELLO Y, FRASSINETTI F, et al. Body space in social interactions: a comparison of reaching and comfort distance in immersive virtual reality[J]. PloS One, 2014, 9(11): e111511. doi: 10.1371/journal.pone.0111511
[19]	IACHINI T, PAGLIARO S, RUGGIERO G. Near or far? It depends on my impression: moral information and spatial behavior in virtual interactions[J]. Acta Psychologica, 2015, 161: 131-136. doi: 10.1016/j.actpsy.2015.09.003
[20]	MEAD R, MATARIĆ M J. Robots have needs too: how and why people adapt their proxemic behavior to improve robot social signal understanding[J]. Journal of Human-Robot Interaction, 2016, 5(2): 48-68. doi: 10.5898/JHRI.5.2.Mead
[21]	KIM Y, KWAK S S, KIM M S. Am I acceptable to you? effect of a robot's verbal language forms on people's social distance from robots[J]. Computers in Human Behavior, 2013, 29(3): 1091-1101. doi: 10.1016/j.chb.2012.10.001
[22]	IACHINI T, COELLO Y, FRASSINETTI F, et al. Peripersonal and interpersonal space in virtual and real environments: effects of gender and age[J]. Journal of Environmental Psychology, 2016, 45: 154-164. doi: 10.1016/j.jenvp.2016.01.004
[23]	EVA P, ANDREA K. Determination of priority stream volumes for capacity calculation of minor traffic streams for intersections with bending right-of-way[J]. Transportation Research Procedia, 2019, 40: 875-882. doi: 10.1016/j.trpro.2019.07.123
[24]	SANTOS-GONZÁLEZ I, CABALLERO-GIL P, RIVERO-GARCÍA A, et al. Priority and collision avoidance system for traffic lights[J]. Ad Hoc Networks, 2019, 94: 101931. doi: 10.1016/j.adhoc.2019.101931
[25]	HAIR JR J, HAIR JR J F, HULT G T M, et al. A primer on partial least squares structural equation modeling (PLS-SEM)[M]. Thousand Oaks: Sage Publications, 2021.
[26]	FORNELL C, LARCKER D F. Evaluating structural equation models with unobservable variables and measurement error[J]. Journal of Marketing Research, 1981, 18(1): 39-50. doi: 10.1177/002224378101800104
[27]	WANG C, YAO X, SINHA P N, et al. Why do government policy and environmental awareness matter in predicting NEVs purchase intention? moderating role of education level[J]. Cities, 2022, 131: 103904. doi: 10.1016/j.cities.2022.103904