﻿ 考虑时间及路径约束的地铁断面客流构成推定方法
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 同济大学学报(自然科学版)  2018, Vol. 46 Issue (7): 920-925.  DOI: 10.11908/j.issn.0253-374x.2018.07.009 0

### 引用本文

HUANG Zhiyuan, XU Ruihua, ZHOU Feng, XU Tianjie. Estimation Method of Section Passenger Flow Composition for Metro Network with Constraints of Time and Route[J]. Journal of Tongji University (Natural Science), 2018, 46(7): 920-925. DOI: 10.11908/j.issn.0253-374x.2018.07.009

### 文章历史

Estimation Method of Section Passenger Flow Composition for Metro Network with Constraints of Time and Route
HUANG Zhiyuan , XU Ruihua , ZHOU Feng , XU Tianjie
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China
Abstract: For relieving the peak hour congestion in metro network and inducing part of passengers to take a less crowded route effectively to optimize the distribution of network passenger flow, capacitated sections in the network were identified firstly by calculating their load factor. Then an estimation model was constructed considering constraints of time and route and an iterative algorithm was designed to calculate the passenger flow composition of capacitated section in the metro network. The passenger flow composition was classified according to the characters of orgin and destination(OD). Capacitated section Caoyang Rd.-Longde Rd. in Shanghai metro system was selected as a research object. The passenger flow composition resulted in the congestion of Caoyang Rd.-Longde Rd. during the morning peak period 8:30~9:00 is obtained. The results have verified the feasibility of the model and algorithm proposed in this paper.
Key words: metro network    capacitated section    estimation model    section passenger flow composition    iterative algorithm

1 问题分析与假设

2 断面客流构成反推模型 2.1 模型建立

 $V = \{ {V_1}, {V_2}, \cdots , {V_i}, \cdots , {V_I}\}$ (1)

 ${V_S} = \sum\limits_{i = 1}^I {{V_i}, i = 1, 2, \cdots , I}$ (2)
 $\forall {Q_i}, {V_i} = \sum\limits_{{u_n} \in {U_{{Q_i}}}} {{P_{{u_n}}}}$ (3)
 ${P_{{u_n}}} = \sum\limits_{k = 1}^{{K_{{Q_i}}}} {{P_k} \cdot {\theta _k}({u_n}), k = 1, 2, \cdots , {K_{{Q_i}}}}$ (4)
 ${\theta _k}({u_n}) = \left\{ \begin{array}{l} 1\;\;{T_{\rm{a}}} \le {T_{{u_n}}} \le {T_{\rm{b}}}且S \in {R_k}\\ 0\;\;其他 \end{array} \right.$ (5)
 ${T_{{u_n}}} = T_{{u_n}}^{\rm{O}} + t_k^{{\rm{train}}} + t_k^{{\rm{wait}}} + t_k^{{\rm{transfer}}}$ (6)

2.2 迭代算法设计

(1) 客流数据：限制区段S的断面客流总量VS

(2) 路径信息：清分比例表中任一OD之间的有效路径条数KQiQi之间的第k条路径Rk的乘客出行选择概率Pkk=1, 2, …, KQi

(3) 时间数据：设定求解高峰时段[Ta, Tb]、[Ta, Tb]时段的列车运行时刻表、由AFC获取的乘客un在O站的进站时刻TunO和由调查得到的网络换乘站各换乘方向的换乘时间.

 图 1 迭代算法流程图 Fig.1 Flow chart of the iterative algorithm
2.3 归类排序

 图 2 相同特征的OD归类示意 Fig.2 An overview of classification of OD with same characters
3 案例分析

3.1 限制区段识别

 图 3 上海地铁局部网络示意图 Fig.3 Part of simplified Shanghai metro network

3.2 断面客流构成反推与归类

 图 4 OD虚拟站点分布示意图 Fig.4 Distribution of virtual stations of ODs

(1) 早高峰8:30~9:00时段经过限制区段的客流，其中的84.25%由11号线曹杨路以北车站出发，因此将此类O站归类为虚拟站点O1；

(2) O1D0的客流为本线客流，即无需换乘即可到达目的地，因此无替代路径，故无法对此类OD实施路径诱导；

(3) 具有替代路径的O1D1，O1D2，O1D3，O1D4所涉及客流量占总量的48.3%，进一步研究此部分客流量，基于替代路径满载率，利用能力富余路径分担其中部分客流量，从而可为制定有效的客流路径诱导方案主动控制网络客流分布奠定基础.

4 结语

(1) 针对目前地铁网络高峰时段客流分布不均衡，部分区段满载率过高，供需矛盾突出的现状，基于断面满载率识别网络能力限制区段，并构建断面客流构成反推模型和设计迭代算法求解导致限制区段拥挤的断面客流构成，以上海地铁网络为例，选择限制区段曹杨路隆德路为研究对象验证了模型和算法的可行性，求解得到在早高峰8:30~9:00时段导致该区段拥挤的客流构成，并分析了该区段断面客流构成的主要特征.本文的研究成果可为运营管理部门针对性地制定有效的客流路径诱导方案提供理论依据，从而使客流组织更具人性化和主动性，提出的断面客流构成推定方法同样适用于求解非限制区段的断面客流构成.

(2) 地铁网络客流拥挤作为网络化运营面临的突出问题，需要更多的理论研究和多种客流组织手段的协调运用，下一步的研究重点将是替代路径的搜索和可供实际操作的客流诱导方案的制定.

 [1] 焦朋朋, 陆化普. 全路网改进动态O-D反推模型[J]. 清华大学学报(自然科学版), 2006, 46(9): 1505 JIAO Pengpeng, LU Huapu. Estimates of dynamic origin-destination flows for general road networks[J]. Journal of Tsinghua University (Science & Technology), 2006, 46(9): 1505 [2] 陈振起, 王书强. 利用道路交通流量进行OD矩阵反推的模型与方法综述[J]. 物流技术, 2009, 28(8): 89 CHEN Zhenqi, WANG Shuqiang. Summary on models and methods of OD matrix estimation based on road traffic flow[J]. Logistics Technology, 2009, 28(8): 89 [3] 马广英, 李平, 闻育, 等. 基于极大熵模型的交通出行矩阵解法研究[J]. 浙江大学学报(工学版), 2006, 40(10): 1778 MA Guangying, LI Ping, WEN Yu, et al. Study of solving origin-destination matrix based on maximum entropy model[J]. Journal of Zhejiang University (Engineering Science), 2006, 40(10): 1778 DOI:10.3785/j.issn.1008-973X.2006.10.024 [4] 郝媛, 徐天东, 杜豫川, 等. 局域路网OD矩阵反推模型研究[J]. 中南公路工程, 2006, 31(5): 107 HAO Yuan, XU Tiandong, DU Yuchuan, et al. Research on OD matrix estimation model of local network[J]. Central South Highway Engineering, 2006, 31(5): 107 [5] 杜长海, 黄席樾, 杨祖元, 等. 改进的粒子群算法在动态OD矩阵反推中的应用[J]. 计算机工程与应用, 2008, 44(34): 234 DU Changhai, HUANG Xiyue, YANG Zuyuan, et al. Application of improved particle swarm algorithm in dynamic OD matrix estimation[J]. Computer Engineering and Applications, 2008, 44(34): 234 DOI:10.3778/j.issn.1002-8331.2008.34.071 [6] 吕娜娜. 基于多中心的公交客流OD反推法研究[D]. 重庆: 重庆交通大学, 2009. LV Nana. Study on bus passenger flow OD matrix estimation methods of multi-center cities [D]. Chongqing: Chongqing Jiaotong University, 2009. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y1525049 [7] 孙捷萍. 城市轨道交通路网客流拥堵控制方法与模型[D]. 北京: 北京交通大学, 2016. SUN Jieping. Methods and models for traffic congestion control in an urban rail transit network [D]. Beijing: Beijing Jiaotong University, 2016. http://www.wanfangdata.com.cn/details/detail.do?_type=degree&id=Y3124836 [8] 周玮腾. 拥塞条件下的城市轨道交通网络流量分配演化建模及疏导策略研究[D]. 北京: 北京交通大学, 2016. ZHOU Weiteng. Modeling passenger flow assignment and evolution in urban rail transit network with dispersion strategy research under congestion conditions [D]. Beijing: Beijing Jiaotong University, 2016. http://cdmd.cnki.com.cn/Article/CDMD-10004-1016059131.htm [9] ROBERTO C, JOSE C. Common-lines and passenger assignment in congested transit networks[J]. Transportation Science, 2001, 35(3): 250 DOI:10.1287/trsc.35.3.250.10154 [10] SI B F, ZHONG M, YANG X B, et al. Urban transit assignment model based on augmented network with in-vehicle congestion and transfer congestion[J]. Journal of Systems Science and Systems Engineering, 2011, 20(2): 155 DOI:10.1007/s11518-011-5159-9 [11] 徐瑞华, 罗钦, 高鹏. 基于多路径的城市轨道交通网络客流分布模型及算法研究[J]. 铁道学报, 2009, 31(2): 110 XU Ruihua, LUO Qin, GAO Peng. Passenger flow distribution model and algorithm for urban rail transit network based on multi-route choice[J]. Journal of the China Railway Society, 2009, 31(2): 110 [12] ZHOU F, XU R H. Model of passenger flow assignment for urban rail transit based on entry and exit time constraints[J]. Transportation Research Record: Journal of the Transportation Research Board, 2012(2284): 57 [13] ZHU W, HU H, XU R H, et al. Modified stochastic user-equilibrium assignment algorithm for urban rail transit under network operation[J]. Journal of Central South University (English Edition), 2013(10): 2897 [14] ZHOU F, SHI J G, XU R H. Estimation method of path-selecting proportion for urban rail transit based on AFC data [J]. Mathematical Problems in Engineering, 2015. DOI: http://dx.doi.org/10.1155/2015/350397. [15] 徐瑞华, 李伟, 朱炜. 考虑时空约束的地铁乘客出行路径集生成算法[J]. 同济大学学报(自然科学版), 2015, 43(7): 1025 XU Ruihua, LI Wei, ZHU Wei. Path set generation algorithm for schedule based rail transit with constraints of time and space[J]. Journal of Tongji University(Natural Science), 2015, 43(7): 1025 DOI:10.11908/j.issn.0253-374x.2015.07.010