中小学生人群聚集处不安全行为倾向研究
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摘要
为预防中小学生在公共场所的安全事故,以不安全行为为切入点,选取某大学附属学校的中小学生作为调查样本,通过学校大门口处的监控视频获取中小学生上放学时间段内人群活动的数据,利用SPSS软件对中小学生人群聚集处不安全行为的倾向及其影响因素进行了描述统计分析和卡方检验,研究中小学生不安全行为特征表现的相关性和差异性。结果表明:①中小学生在无约束的状态下主要的不安全行为有跑跳、追逐、推搡、路上逗留等;②中小学生中男生比女生更容易发生不安全行为;③早中晚4个上下学时间段内,中小学生发生不安全行为的概率基本一致,约为21.35%;④早中晚4个上下学时间段内,中午放学时间段内学校大门口处中小学生人群密度最大,中小学生发生不安全行为的概率最大;⑤中小学生跑跳、追逐、推搡、路上逗留等不安全行为的发生存在明显的性别倾向,男生发生跑跳、追逐、推搡不安全行为的概率高于女生,女生发生路上逗留不安全行为的概率高于男生。
In order to prevent the accidents of primary and middle school students in public space,this paper takes the unsafe behavior as the starting point,and selects the students of the affiliated primary and middle school of a university as the survey sample.Through the surveillance video of the primary and secondary school students at the school gate,the paper obtains the population activity data of the students during school time,and uses SPSS statistical analysis software to study the behavioral tendency of the primary and middle school students,and statistics on the relevant factors affecting the behavior.The paper also carries out descriptive statistical analysis and chi-square test on the results and obtains the correlation and difference of the performance characteristics of primary and middle school students.The results show that:①The major unsafe behaviors of primary and middle school students in the unconstrained state include jumping,chasing,pushing,staying on the road,etc.;②Boys are more prone to unsafe behavior than girls;③In the morning,noon and evening school hours,the probability of unsafe behaviors is basically the same,about21.35%;④In the morning,noon and evening school hours,the density of people at the school gate is the highest at noon and the unsafe behavior occurred most;⑤The occurrence of unsafe behaviors such as running,jumping,chasing,shoving and lingering on the road has obvious gender tendency.Boys are more likely to engage in unsafe activities such as running,jumping,chasing and shoving than girls.Girls are more likely than boys to linger on the road.
In order to prevent the accidents of primary and middle school students in public space,this paper takes the unsafe behavior as the starting point,and selects the students of the affiliated primary and middle school of a university as the survey sample.Through the surveillance video of the primary and secondary school students at the school gate,the paper obtains the population activity data of the students during school time,and uses SPSS statistical analysis software to study the behavioral tendency of the primary and middle school students,and statistics on the relevant factors affecting the behavior.The paper also carries out descriptive statistical analysis and chi-square test on the results and obtains the correlation and difference of the performance characteristics of primary and middle school students.The results show that:①The major unsafe behaviors of primary and middle school students in the unconstrained state include jumping,chasing,pushing,staying on the road,etc.;②Boys are more prone to unsafe behavior than girls;③In the morning,noon and evening school hours,the probability of unsafe behaviors is basically the same,about21.35%;④In the morning,noon and evening school hours,the density of people at the school gate is the highest at noon and the unsafe behavior occurred most;⑤The occurrence of unsafe behaviors such as running,jumping,chasing,shoving and lingering on the road has obvious gender tendency.Boys are more likely to engage in unsafe activities such as running,jumping,chasing and shoving than girls.Girls are more likely than boys to linger on the road.
引文
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[2]吴宦漳,赵天宇.寒地城市广场人群行为模式及影响因素研究---以哈尔滨市建筑艺术广场为例[C]//城市时代,协同规划---2013中国城市规划年会论文集.北京:中国城市规划学会,2013.
[3]张朋跃.人群状态分析与异常行为检测[D].北京:中国民航大学,2016.
[4]席文波.公共场合旁观人群行为的现象学研究[D].天津:天津师范大学,2015.
[5]孙金龙,王爽,陈文瑛.基于人群恐慌的地铁应急疏散仿真研究[J].安全,2017,38(3):8-11.
[6]杨洁,杨乃,黄婷,等.大型商场内人群择路行为认知规律的研究[J].武汉大学学报(信息科学版),2017,42(3):414-420.
[7]崇信毅.基于视频监控数据的人群行为分析[D].北京:北京交通大学,2012.
[8]姚庆国,郭秀菊,张学睦.基于SEM理论的沟通满意度对煤矿矿工不安全行为的影响研究[J].安全与环境工程,2017,24(6):101-106.
[9]袁斌.不安全行为控制方法对改进化工企业安全生产有效性的研究[J].安全与环境工程,2015,22(6):95-98.
[10]Wang L,Tan T,Ning H,et al.Silhouette analysis-based gait recognition for human identification[J].IEEE Transactions on Pattern A-nalysis and Machine Intelligence,2003,25(12):1505-1518.
[11]Wu S,Moore B E,Shah M.Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes[C]//2011 IEEECopmuter Society Conference on Computer Vision and Pattern Recognition.San Francisco,USA:IEEE,2010:2054-2060.
[12]Mehran R,Moore B E,Shah M.A streakline representation of flow in crowded scenes[J].European Conference on Computer Vision,2010,3:439-452.
[13]Adam A,Rivlin E,Shimshoni I,et al.Robust real-time unusual event detection using multiple fixed-location monitors[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2007,30(3):555-560.
[14]Reynolds C W.Flocks herds and schools:A distributed behavioral model[J].Computer Graphics,1987,21(4):25-34.
[15]刘晓芳,吝涛,郑千惠,等.城市街巷型公共空间人群聚集安全风险量化评估[J].中国安全科学学报,2016,26(12):134-139.