骑行者智能手机使用行为的影响因素与风险情景

doi:10.3969/j.issn.1674-8484.2022.01.003

汽车安全与节能学报 ›› 2022, Vol. 13 ›› Issue (1): 48-54.DOI: 10.3969/j.issn.1674-8484.2022.01.003

骑行者智能手机使用行为的影响因素与风险情景

袁泉¹(), 齐悦瑞¹, 于迪², 徐学才³

1.清华大学汽车安全与节能国家重点实验室,北京100084,中国
2.东北林业大学机电工程学院,哈尔滨 150046,中国
3.华中科技大学土木与水利工程学院,武汉430074,中国

收稿日期:2021-07-01 修回日期:2021-12-09 出版日期:2022-03-31 发布日期:2022-04-02
作者简介:袁泉(1974–),男(汉族),黑龙江,高级工程师。E-mail:yuanq@tsinghua.edu.cn。
基金资助:
国家自然科学基金资助项目(52072214)

Contributing factors and risky scenario of cyclists’ smart phone using behavior

YUAN Quan¹(), QI Yuerui¹, YU Di², XU Xuecai³

1. State Key Laboratory of Automotive Safety and Energy , Tsinghua University, Beijing 100084, China
2. School of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150042, China
3. School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2021-07-01 Revised:2021-12-09 Online:2022-03-31 Published:2022-04-02

摘要/Abstract

摘要：

对校园内部骑行者使用手机行为的原因和情景,开展了基于人机工程学的综合研究。通过当面采访、实地录像记录,统计了骑行中使用手机的比例;通过问卷方法,调查了骑车人使用手机的频度及原因、以及对于骑行中使用手机行为影响的认识。构建Bayes二项互补双对数概率模型,综合对骑行中使用手机行为的因素,分析了行驶环境、行驶时间、天气等客观条件和骑行者的性别、习惯、心情等主观因素。结果表明：所调查的校园内骑行者使用手机的概率为3.2%,骑行情景多发生在路段和工作日时间。该研究结果可为骑行文明规范的引导和相关安全法规的设定提供了依据和方向。

关键词: 道路交通安全, 骑行者安全, 风险情景判定, Bayes互补双对概率模型

Abstract:

A comprehensive research was carried out on the causes and scenarios of cyclists’ use of mobile phones in campus based on ergonomics. Face-to-face interviews and field video recordings were employed to summarize the proportion of cyclists using mobile phones. Questionnaire survey method was used to figure out the frequencies and reasons of cyclists using mobile phones, as well as their understanding of the impact of using mobile phones in cycling. By extracting the dataset, a Bayes binomial complementary pair probit model was proposed to analyze the factors of using mobile phone in cycling, such as riding environment, riding time, weather and subjective factors (e.g. gender, habits and mood of cyclists). The results show that the probability of using mobile phone in campus is 3.2%, and most of the riding scenes occur on road sections and working days. The findings provide the basis and direction for the guidance of riding civilization norms and the setting of relevant safety regulations.

Key words: road traffic safety, cyclists’ safety, risk scenario determination, Bayes clog-log probit mode

中图分类号:

U484

袁泉, 齐悦瑞, 于迪, 徐学才. 骑行者智能手机使用行为的影响因素与风险情景[J]. 汽车安全与节能学报, 2022, 13(1): 48-54.

YUAN Quan, QI Yuerui, YU Di, XU Xuecai. Contributing factors and risky scenario of cyclists’ smart phone using behavior[J]. Journal of Automotive Safety and Energy, 2022, 13(1): 48-54.

图/表 9

参考文献 21

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位置编号	记录时段	道路类型	道路宽度/ m	通行宽度/ m	风力/级	人数	样本数	样本占比/%
1	夜晚	平坦窄道	3	2.3	0~1	209	15	7.18
2	早晨	平坦宽道	6	6	2~3	238	7	2.94
3	早晨		9	8	2~3	187	12	6.42
4	早晨		9	9	≥6	139	8	5.76
…	…	…	…	…	…	…	…	…
合计						3 717	119	3.2

位置编号	记录时段	道路类型	道路宽度/ m	通行宽度/ m	风力/级	人数	样本数	样本占比/%
1	夜晚	平坦窄道	3	2.3	0~1	209	15	7.18
2	早晨	平坦宽道	6	6	2~3	238	7	2.94
3	早晨		9	8	2~3	187	12	6.42
4	早晨		9	9	≥6	139	8	5.76
…	…	…	…	…	…	…	…	…
合计						3 717	119	3.2

位置编号	包括外卖人员				不包括外卖人员
位置编号	人数合计	样本数量	样本占比/%	人数	人数合计	男性	女性	样本占比/%
1	8	7	88	201	8	3	5	3.98
2	4	3	75	234	4	1	3	1.71
3	8	6	75	179	6	2	4	3.35
4	6	6	100	133	2	1	1	1.50
…	…	…	…	…	…	…	…	…
合计	80	47	59	3 637	72	31	41	1.98

位置编号	包括外卖人员				不包括外卖人员
位置编号	人数合计	样本数量	样本占比/%	人数	人数合计	男性	女性	样本占比/%
1	8	7	88	201	8	3	5	3.98
2	4	3	75	234	4	1	3	1.71
3	8	6	75	179	6	2	4	3.35
4	6	6	100	133	2	1	1	1.50
…	…	…	…	…	…	…	…	…
合计	80	47	59	3 637	72	31	41	1.98

频率	骑行者自用手机		看到其他骑行者使用手机
频率	人数	比例/%	人数	比例/%
几乎不	51	27.27	10	5.35
很少	62	33.16	59	31.55
常常	44	23.53	67	35.83
很多	18	9.63	38	20.32
非常多	12	6.42	13	6.95

骑行者智能手机使用行为的影响因素与风险情景

Contributing factors and risky scenario of cyclists’ smart phone using behavior

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	Bayesian 概率模型				Bayesian互补双对概率模型
	均值	标准偏差	MCSE	95%BCI	均值	标准偏差	MCSE	95%BCI
发生位置	47.470^*	35.144	1.457	(4.412,129.254)	51.809^*	35.760	1.661	(5.227, 135.242)
发生日	46.357^*	35.175	1.458	(3.111, 128.744)	50.469^*	35.745	1.656	(3.448,132.985)
常数	-46.909^*	35.151	1.456	(-128.853, -3.573)	-51.680^*	35.752	1.660	(-134.789,-4.836)

评价标准	Bayes概率模型	Bayes互补双对概率模型
对数边际似然估计	-24.306	-23.767
偏差信息准则,DIC	28.720	28.239

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