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

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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

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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
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