吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (1): 100-107.doi: 10.13229/j.cnki.jdxbgxb201601015

• • 上一篇    下一篇

基于云模型的地铁换乘枢纽拥挤度辨识方法

周继彪1, 陈红2, 闫彬3, 张文4, 冯微2   

  1. 1.宁波工程学院 交通学院,浙江 宁波 315211;
    2.长安大学 公路学院,西安 710064;
    3.蚌埠汽车士官学校 司训勤务系, 安徽 蚌埠 233011;
    4.广州市城市规划勘测设计研究院 交通规划室,广州 510060
  • 收稿日期:2014-09-24 出版日期:2016-01-30 发布日期:2016-01-30
  • 通讯作者: 陈红(1963-),女,教授,博士生导师.研究方向:综合交通规划.E-mail:hongchen82@126.com
  • 作者简介:周继彪(1986-),男,讲师,博士.研究方向:综合交通规划.E-mail:zhoujb2014@nbut.edu.cn
  • 基金资助:
    国家自然科学基金项目(51308311); 浙江省社会科学基金项目(15NDJC078YB); 宁波市自然科学基金项目(2015A610298)

Identification of pedestrian crowding degree in metro transfer hub based on normal cloud model

ZHOU Ji-biao1, CHEN Hong2, YAN Bin3, ZHANG Wen4, FENG Wei2   

  1. 1.School of Transportation, Ningbo University of Technology, Ningbo 315211, China;
    2.School of Highway, Chang'an University, Xi'an 710064, China;
    3.Department of Driver Training Service, Bengbu Automobile Sergeant's School, Bengbu 233011, China;
    4.Guangzhou Urban Planning & Design Survey Research Institute, Transport Planning & Design Studio, Guangzhou 510060, China
  • Received:2014-09-24 Online:2016-01-30 Published:2016-01-30

RICH HTML

0   

PDF (PC)

406

摘要： 针对地铁换乘枢纽拥挤状态划分中的模糊性和随机性,提出了一种基于云模型的地铁换乘枢纽行人拥挤度辨识方法。首先分析行人拥挤度的内涵和度量标准,根据它们在不同服务水平下边界值计算云的数字特征。其次利用云的合成理论建立不同服务水平对应的模板云模型,同时将采集到的行人交通特性基本参数输入到枢纽内各基础设施的云发生器中,建立待识别云模型。然后根据云相似度计算模板云和待识别云的相似度并引入拥挤度定义,给出拥挤度辨识的具体实现过程。最后以西安市北大街地铁换乘枢纽为例对其进行了试验验证。结果表明:通过实际采集的数据建立待识别云模型,并与模板云作相似度分析,得出通道该时刻的行人拥挤度为100.095,处于拥挤状态;楼梯该时刻的行人拥挤度为100.273,处于拥挤状态。该方法不仅能够较准确地定量辨别枢纽内行人拥挤度状态,而且能够反映行人拥挤的程度和拥挤的变化过程,有较强的实用性。

关键词: 交通运输系统工程, 拥挤度, 云模型, 地铁换乘枢纽, 基础设施

Abstract: In order to solve the problems of fuzziness and randomness in the division of congestion state in metro transfer hub, an identification method of pedestrian crowding degree factor is proposed based on normal cloud model. First, the connotation and the measure standard of the pedestrian crowding degree are analyzed, and according to their boundary value under different service level the digital characteristics of the cloud are calculated. Second, the cloud synthesis theory is applied to establish the template cloud corresponding to different service levels, and the survey data (pedestrian speed, flow etc.) are input into the cloud generator in order to set up the identification cloud model. Third, according the definition of cloud similarity, the similarity between the identified cloud and the template cloud in the infrastructure (e.g. channel, stairs) is calculated. Moreover, the crowding degree is defined independently, which is described in a quantificational level under the state of crowding degree in the metro transfer hub, and the method to identify the pedestrian crowding degree is given. The method is verified by a case study in a metro transfer hub in Xi'an. Results show that the pedestrian crowding degree in the channel 100.095, which is in the crowding state, the pedestrian crowding degree in the stair is 100.273, which is also in the crowded state. This method can not only accurately identify the crowded state quantificationally, but also reflect the change process of pedestrian crowding degree, which has strong practicality.

Key words: engineering of communications and transportation system, crowding degree, cloud model, metro transfer hub, infrastructure

中图分类号: 

  • U491
[1] 周继彪,陈红,闫彬,等.地铁换乘枢纽行人交通特性实证研究[J].武汉理工大学学报,2014,36(4):92-99.
Zhou Ji-biao, Chen Hong, Yan Bin, et al. Pedestrian traffic characteristics analysis in the metro transfer hub[J]. Journal of Wuhan University of Technology, 2014, 36(4): 92-99.
[2] Helbing D, Farkas I J, Molnar P, et al. Simulation of pedestrian crowds in normal and evacuation situations[J]. Pedestrian and Evacuation Dynamics, 2002, 21(1): 21-58.
[3] Miguel A F. The emergence of design in pedestrian dynamics: locomotion, self-organization, walking paths and constructal law[J]. Physics of Life Reviews, 2013, 10(2):168-180.
[4] Ma J, Song W G, Lo S M, et al. New insights into turbulent pedestrian movement pattern in crowd-quakes[J]. Journal of Statistical Mechanics: Theory and Experiment, 2013(2): 28-36.
[5] Zheng X, Zhong T, Liu M. Modeling crowd evacuation of a building based on seven methodological approaches[J]. Building and Environment, 2009, 44(3): 437-445.
[6] Shiwakoti N, Sarvi M. Enhancing the panic escape of crowd through architectural design[J]. Transportation Research Part C: Emerging Technologies, 2013, 37(1): 260-267.
[7] 杨丽丽, 贾洪飞, 隽志才. 轨道交通枢纽换乘系统可靠性及其影响因素[J]. 吉林大学学报:工学版, 2011, 41(4):944-949.
Yang Li-li, Jia Hong-fei, Juan Zhi-cai. Transfer system reliability and its influencing factors of rail transit terminal[J]. Journal of Jilin University (Engineering and Technology Edition), 2011, 41(4):944-949.
[8] Helbing D, Buzna L, Johansson A, et al. Self-organized pedestrian crowd dynamics: experiments, simulations, and design solutions[J]. Transportation Science, 2005, 39 (1): 1-24.
[9] Antonini G, Bierlaire M, Weber M. Discrete choice models of pedestrian walking behavior[J]. Transportation Research Part B: Methodological, 2006, 40(8): 667-687.
[10] Helbing D, Johansson A. Pedestrian, Crowd and Evacuation Dynamics[M]. Berlin: Springer, 2009.
[11] Mawson A R. Mass Panic and Social Attachment: the Dynamics of Human Behavior[M]. Farnham: Ashgate Publishing, Ltd, 2012.
[12] Transportation Research Board. Transit Capacity and Quality of Service Manual[M]. Washington, D C: Transportation Research Board, 2003.
[13] Li D Y, Meng H J,Shi X M. Membership clouds and membership cloud generators[J]. Journal of Computer Research and Development, 1995, 32(6): 15-20.
[14] 李德毅, 刘常昱. 论正态云模型的普适性[J]. 中国工程科学, 2004, 6(8): 28-34.
Li De-yi,Liu Chang-yu. Study on the universality of the normal cloud model[J]. Engineering Science, 2004, 6(8): 28-34.
[15] 孟晖, 王树良, 李德毅. 基于云变换的概念提取及概念层次构建方法[J]. 吉林大学学报:工学版, 2010, 40(3):782-787.
Meng Hui,Wang Shu-liang,Li De-yi.Concept extraction and concept hierarchy constructionbased on cloud transformation[J].Journal of Jilin University (Engineering and Technology Edition),2010,40(3):782-787.
[1] 陈永恒,刘芳宏,曹宁博. 信控交叉口行人与提前右转机动车冲突影响因素[J]. 吉林大学学报(工学版), 2018, 48(6): 1669-1676.
[2] 常山,宋瑞,何世伟,黎浩东,殷玮川. 共享单车故障车辆回收模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1677-1684.
[3] 曲大义,杨晶茹,邴其春,王五林,周警春. 基于干线车流排队特性的相位差优化模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1685-1693.
[4] 宗芳, 齐厚成, 唐明, 吕建宇, 于萍. 基于GPS数据的日出行模式-出行目的识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1374-1379.
[5] 刘翔宇, 杨庆芳, 隗海林. 基于随机游走算法的交通诱导小区划分方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1380-1386.
[6] 钟伟, 隽志才, 孙宝凤. 不完全网络的城乡公交一体化枢纽层级选址模型[J]. 吉林大学学报(工学版), 2018, 48(5): 1387-1397.
[7] 刘兆惠, 王超, 吕文红, 管欣. 基于非线性动力学分析的车辆运行状态参数数据特征辨识[J]. 吉林大学学报(工学版), 2018, 48(5): 1405-1410.
[8] 宗芳, 路峰瑞, 唐明, 吕建宇, 吴挺. 习惯和路况对小汽车出行路径选择的影响[J]. 吉林大学学报(工学版), 2018, 48(4): 1023-1028.
[9] 栾鑫, 邓卫, 程琳, 陈新元. 特大城市居民出行方式选择行为的混合Logit模型[J]. 吉林大学学报(工学版), 2018, 48(4): 1029-1036.
[10] 陈永恒, 刘鑫山, 熊帅, 汪昆维, 谌垚, 杨少辉. 冰雪条件下快速路汇流区可变限速控制[J]. 吉林大学学报(工学版), 2018, 48(3): 677-687.
[11] 王占中, 卢月, 刘晓峰, 赵利英. 基于改进和声搜索算法的越库车辆排序[J]. 吉林大学学报(工学版), 2018, 48(3): 688-693.
[12] 李志慧, 胡永利, 赵永华, 马佳磊, 李海涛, 钟涛, 杨少辉. 基于车载的运动行人区域估计方法[J]. 吉林大学学报(工学版), 2018, 48(3): 694-703.
[13] 陈松, 李显生, 任园园. 公交车钩形转弯交叉口自适应信号控制方法[J]. 吉林大学学报(工学版), 2018, 48(2): 423-429.
[14] 苏书杰, 何露. 步行交通规划交叉路口行人瞬时动态拥塞疏散模型[J]. 吉林大学学报(工学版), 2018, 48(2): 440-447.
[15] 孟品超, 李学源, 贾洪飞, 李延忠. 基于滑动平均法的轨道交通短时客流实时预测[J]. 吉林大学学报(工学版), 2018, 48(2): 448-453.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 《吉林大学学报(工学版)》编辑部
地址：长春市人民大街5988号　电话：+86-431-85095297　传真：+86-431-85094074　E-mail: xbgxb@jlu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发