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[1]李颖宏,郝晓青.综合交通枢纽客流状态智能分析[J].智能系统学报,2014,9(6):677-684.[doi:10.3969/j.issn.1673-4785.201308035]
　LI Yinghong,HAO Xiaoqing.Intelligent analysis of passenger flow state in an integrated transport hub[J].CAAI Transactions on Intelligent Systems,2014,9(6):677-684.[doi:10.3969/j.issn.1673-4785.201308035]

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综合交通枢纽客流状态智能分析

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 9 期数: 2014年第6期页码: 677-684 栏目: 学术论文—智能系统出版日期: 2014-12-25

Title:: Intelligent analysis of passenger flow state in an integrated transport hub

作者:: 李颖宏, 郝晓青; 北方工业大学城市道路智能交通控制技术北京市重点实验室, 北京 100144

Author(s):: LI Yinghong, HAO Xiaoqing; NCUT-Beijing Key Laboratory of Urban Intelligent Traffic Control, North China University of Technology, Beijing 100144, China

关键词:: 交通工程; 客流拥挤指数; 模糊评价; 客流运行状态; 综合交通枢纽; 智能分析; 交通流

Keywords:: traffic engineering; passenger flow congestion index; fuzzy evaluation; running state of passenger flow; integrated transport hub; intelligent analysis; traffic flow

分类号:: TP18;U491.1

DOI:: 10.3969/j.issn.1673-4785.201308035

文献标志码:: A

摘要:: 综合交通枢纽每日承载着几十万客流量,为了满足日益发展的综合交通枢纽内的指挥、运营管理及预警预案的需求,对枢纽内的客流运行状态的判别就显得尤其重要。提出了一种新的评判客流运行状态的指标——客流拥挤指数,基于枢纽内行人密度和单位宽度流率等反映枢纽客流状况的特性指标,建立了枢纽内客流状态综合模糊评价模型,并从中获取了客流拥挤指数。通过对实际交通枢纽内的客流状态仿真分析得出,所采用的模糊评价模型所得到的客流拥挤指数较好地反映了枢纽内的真实的客流状态,与实际状况的契合度很高。所提出的枢纽内客流状态评价指标简洁明了,能为枢纽交通管理者和出行者提供有价值的参考。

Abstract:: An integrated transport hub undertakes hundreds of thousands of traffic flow every day. In order to meet the need of directing, operation, management and pre-arranged planning, it is important to figure out the running state of passenger flow inside the hub. In this paper, it is proposed to use a new indicator to judge the running state of passenger flow: passenger flow congestion index. A compressive fuzzy evaluation model of the state of passenger flow inside the hinge is setup based on the characteristics that are able to reflect the condition of passenger flow. This includes density of passenger, passenger flow rate per width. The passenger flow congestion index is also determined from this model. Through the simulation and analysis of the state of passenger flow of an actual transport hub, the proposed passenger flow congestion index used in the fuzzy evaluation model reflected the actual state of passenger flow and corresponded with real conditions. The evaluation criteria of the passenger flow state in the transport hub proposed in this paper is concise and clear, which means that it might also be a valuable reference for the travellers and transport hub managers.

参考文献/References:: [1] 王会会.综合客运交通枢纽内部客流拥堵机理研究[D]. 北京: 北京交通大学, 2011: 23-25.WANG Huihui. Passenger congestion mechanism analysis in comprehensive passenger transportation terminal [D]. Beijing: Beijing Jiaotong University, 2011: 23-25.
[2] 房霄虹,周磊山,周勃.基于可排序DEA的城市综合交通枢纽评价方法研究[J].交通运输系统工程与信息, 2008, 8(2): 108-113.FANG Xiaohong, ZHOU Leishan, ZHOU Bo. Evaluation method of city comprehensive transportation hub based on permutable DEA model[J]. Journal of Transportation Systems Engineering and Information Technoloy, 2008, 8(2): 108-113.
[3] 马莉.城市轨道交通枢纽乘客交通流状态分析与评价[D]. 北京:北京交通大学,2009:15-25.MA Li. Analysis and evaluation of passenger flow operation state in urban railway transit hub[D]. Beijing: Beijing Jiaotong University, 2009: 15-25.
[4] 於毅.城市道路交通状态判别方法研究[D]. 北京:北京交通大学, 2007: 15-17.YU Yi. Research on the identification method of urban road traffic conditions[D]. Beijing: Beijing Jiaotong University, 2007 : 15-17.
[5] 杨兆升,张茂雷.基于模糊综合评判的道路交通状态分析模型[J].公路交通科技, 2010, 27(9): 121-126.YANG Zhaosheng, ZHANG Maolei. A model for road traffic state analys based on fuzzy comprehensive evaluation[J]. Journal of Highway and Transportation Research and Development, 2010, 27 (9): 121-126.
[6] 孙超.城市道路交通状态评价分析研究[D]. 广州:华南理工大学, 2010: 25-31.SUN Chao. Research on urban traffic network state evaluation and analysis[D]. Guangzhou: South China University of Technology, 2010: 25-31.
[7] 陈艳艳,李平普,张广厚,等.综合交通枢纽客流拥挤实时评价方法[J].公路交通科技, 2012, 29: 75-80.CHEN Yanyan, LI Pingpu, ZHANG Guanghou, et al. Real-ime evaluation method of passenger crowding in integrated transport hub[J]. Journal of Highway and Transportation Research and Development, 2012, 29: 75-80.

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备注/Memo

收稿日期:2013-8-24;改回日期:。
基金项目:国家“863”计划资助项目(2012AA112401);国家科技支撑计划资助项目(2011BAH16部-05);学科建设人才培养资助项目(PXM2013_014212_000031).
作者简介:李颖宏,1968年生,教授,博士,主要研究方向为智能交通信号检测及短时交通流及交通状态预测。近3年来作为主要完成人之一,承担了北京奥运交通工程-北京中心区交通信号设备的优化项目和国庆60周年重大项目-长安街交通控制系统改造工程、北京市城市智能交通三年规划项目(2010-2012)项目等10余项。曾获大连市科学技术进步一等奖,发表学术论文50余篇,其中被EI检索24篇,申请国家发明专利12项;郝晓青,1988年生,硕士。主要研究方向为智能交通控制与管理,短时交通流预测,行人预测,发表学术论文2篇。
通讯作者:李颖宏.E-mail:lyh427@ncut.edu.cn.

更新日期/Last Update: 2015-06-16