轨道交通车站出入口客流预测——以北京市为例

doi:10.13229/j.cnki.jdxbgxb.20221387

Abstract

Abstract:

Regarding the accurate prediction of passenger flow at the entrance and exit of rail transit stations， Considering the land use around the station， Rail transit connection conditions， station attributes and attraction， a two-level passenger flow prediction model is constructed in this paper， station attributes and attraction， a two-level passenger flow prediction model is constructed in this paper， including the passenger flow prediction at the traffic district level based on multiple nonlinear regression and the passenger flow prediction at the entrance and exit level based on the CRITIC method. On the basis of the available data， the total passenger flow of all entrances and exits in the traffic district is predicted， and then be allocated to each entrance and exit. Different types of rail transit stations are randomly selected to verify the effectiveness of the model. The results show that the error between the predicted value of the model and the actual value of the daily entry volume is within 30%， and the average error is 20%， which has a high prediction accuracy.

Key words: rail transit, two-level passenger flow prediction model, nonlinear regression, CRITIC method

CLC Number:

U291.69

Jie MA,Zhi-li LIU,Shu-ling WANG,Hao DONG. Passenger flow prediction at entrance and exit of rail transit stations：a case study of Beijing[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2197-2205.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Table 1

Table 2

Table 3

Fig.4

Table 4

Fig.5

Table 5

References 17

1	Santos G, Maoh H, Potoglou D, et al. Factors influencing modal split of commuting journeys in medium-size European cities[J]. Journal of Transport Geography, 2013, 30: 127-137.
2	Wu J Y, Sun H J, Gao Z Y, et al. Risk‐based stochastic equilibrium assignment model in augmented urban railway network[J]. Journal of Advanced Transportation, 2014, 48(4): 332-347.
3	Wang J, Peeta S, He X, et al. Combined multinomial logit modal split and paired combinatorial logit traffic assignment model[J]. Transportmetrica A: Transport Science, 2018, 14(9): 737-760.
4	王淑伟, 孙立山, 郝思源, 等. 基于精细化用地的轨道客流直接估计模型[J]. 交通运输系统工程与信息, 2015, 15(3): 37-43.
	Wang Shu-wei, Sun Li-shan, Hao Si-yuan, et al. Station level transit ridership direct estimation model based on precise land use[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(3): 37-43.
5	岂常禄, 胡昊. 基于混合地理加权回归的城市轨道交通站点客流预测研究[J]. 铁道科学与工程学报, 2021, 18(7): 1903-1909.
	Qi Chang-lu, Hu Hao. Research on ridership forecast of urban rail transit based on mixed geographic weighted regression[J]. Journal of Railway Science and Engineering, 2021, 18(7): 1903-1909.
6	何正强. 城市轨道交通车站分向客流预测方法研究[J]. 交通科技, 2012, 2012 (2): 119-122.
	He Zheng-qiang. Study of prediction methods of passenger flow in different directions of urban rail transit station[J]. Transportation Science & Technology, 2012, 2012(2):119-122.
7	张哲宁, 程苑, 马毅林, 等. 城市轨道交通初步设计阶段接驳客流预测方法研究[J]. 交通工程, 2017, 17(3): 8-13.
	Zhang Zhe-ning, Cheng Yuan, Ma Yi-lin, et al. The forecast method of urban rail transit passenger volume in the preliminary design phase[J]. Journal of Transportation Engineering, 2017, 17(3): 8-13.
8	傅搏峰, 吴娇蓉, 华陈睿. 轨道站出入口客流分布系数估计方法[J]. 同济大学学报: 自然科学版, 2012, 40(11): 1660-1665.
	Fu Bo-feng, Wu Jiao-rong, Hua Chen-rui. Estimation method of distribution coefficient of passenger flow of subway station entrance[J]. Journal of Tongji University(Natural Science), 2012, 40(11): 1660-1665.
9	Cervero R, Kockelman K. Travel demand and the 3Ds: density, diversity, and design[J]. Transportation Research Part D: Transport and Environment, 1997, 2(3): 199-219.
10	李国强, 杨敏, 吴运腾, 等. 基于AFC和POI数据的轨道交通站点客流影响因素挖掘[C]//创新驱动与智慧发展——2018年中国城市交通规划年会论文集, 中国青岛, 2018: 1-11 .
	Li Guo-qiang, Yang Min, Wu Yun-teng, et al. Research on influencing factors of passenger flow in rail transit stations based on AFC and POI data[C]//Innovation-driven and Intelligent Development -- Proceedings of the 2018 Annual Conference on Urban Transportation Planning in China, Qingdao, China, 2018: 1-11.
11	戴维佳. 轨道交通乘客全过程出行行为分析与站点客流吸引范围研究[D]. 北京: 北京交通大学交通运输学院, 2019.
	Dai Wei-jia. Study on passengers´ full-process travel behavior and stations´ passenger flow attraction range for urban rail transit system[D]. Beijing: School of Traffic and Transportation, Beijing Jiaotong University, 2019.
12	王淑伟, 孙立山, 荣建. 北京市轨道站点吸引范围研究[J]. 交通运输系统工程与信息, 2013, 13(3): 183-188.
	Wang Shu-wei, Sun Li-shan, Rong Jian. Catchment area analysis of Beijing transit stations[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(3): 183-188.
13	邓进. 城市轨道交通车站客流特征与周边土地利用互动关系[D]. 北京: 北京交通大学交通运输学院, 2015.
	Deng Jin. Interactive relationship between ridership characteristics at subway station and surrounding land-use[D]. Beijing: School of Traffic and Transportation, Beijing Jiaotong University, 2015.
14	范琪, 王炜, 杨洋, 等. 家庭收入差异对出行方式选择的影响分析[J]. 交通信息与安全, 2019, 37(6): 111-120.
	Fan Qi, Wang Wei, Yang Yang, et al. Effects of household income differences on travel mode choice[J]. Journal of Transport Information and Safety, 2019, 37(6): 111-120.
15	刘李红, 高辰颖, 王文超, 等. 京津冀高质量协同发展:演化历程、动力机理与未来展望[J]. 北京行政学院学报, 2023(5): 61-71.
	Liu Li-hong, Gao Chen-ying, Wang Wen-chao, et al. High-quality coordinated development of the Beijing-Tianjin-Hebei region: evolution course, power mechanism and future prospects[J]. Journal of Beijing Administration Institute, 2023, 2023(5): 61-71.
16	邹江源, 叶霞飞. 城市轨道交通车站出入口布局规划方法[J]. 城市轨道交通研究, 2019, 22(12): 39-42.
	Zou Jiang-yuan, Ye Xia-fei. Layout of station entrance and exit for urban rail transit[J]. Urban Mass Transit, 2019, 22(12): 39-42.
17	薛薇. SPSS统计分析方法及应用[M]. 北京: 电子工业出版社, 2009.

Related Articles 15

[1]	Ying REN,Jian-hua DUO,Rui-xia SONG. Design of rail transit signal joint control system based on variable structure PID control [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2364-2369.
[2]	Bo-song FAN,Chun-fu SHAO. Urban rail transit emergency risk level identification method [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 427-435.
[3]	Jing WANG,Feng WAN,Chun-jiao DONG,Chun-fu SHAO. Modelling on catchment area and attraction intensity of urban rail transit stations [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 439-447.
[4]	Min MA,Da-wei HU,Lan SHU,Zhuang-lin MA. Resilience assessment and recovery strategy on urban rail transit network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 396-404.
[5]	Qing-yong WANG,Wei-qiang QU. Optimization algorithm of urban rail transit operation scheduling based on linear programming [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3446-3451.
[6]	Lin JIANG,Ling ZHOU,Hui ZHAO. Design and stiffness continuously adjustable analysis of hydraulic servo flexible drive mechanism [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(7): 1499-1508.
[7]	Ting-ping ZHANG,Di WAN. Evaluation algorithm of rail transit node importance based on rough set theory [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 2845-2851.
[8]	Feng XUE,Chuan-lei HE,Qian HUANG,Jian LUO. Coordination degree of multimodal rail transit network [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2040-2050.
[9]	Cai-hua ZHU,Xiao-li SUN,Yan LI. Forecast of urban public bicycle traffic demand by station classification [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 531-540.
[10]	ZHAO Xue-yu, YANG Jia-qi, PENG Ya-mei. Competitive and cooperative relationship evolution mechanism between urban rail transit and traditional bus [J]. 吉林大学学报(工学版), 2017, 47(3): 756-764.
[11]	YAO Xiang-ming, ZHAO Peng, YU Dan-dan. Dynamic origin-destination matrix estimation for urban rail transit based on averaging strategy [J]. 吉林大学学报(工学版), 2016, 46(1): 92-99.
[12]	CAO Yang, ZHAO Shu-zhi, TIAN Qing-fei. Urban rail transit subsidy policy based on maximum welfare [J]. , 2012, (03): 618-622.
[13]	ZHAO Shu-zhi, CAO Yang, TIAN Qing-fei. Urban rail transit scheduling decision based on information fusion [J]. 吉林大学学报(工学版), 2011, 41(增刊1): 85-88.
[14]	CHEN Peng\|YAN Xin-ping\|LI Xu-hong\|WU Chao-zhong. Schedule coordination model between rail transit and bus transit [J]. 吉林大学学报(工学版), 2011, 41(4): 950-955.
[15]	XU Wang-tu,HE Shi-wei,SONG Rui. Headway and fare optimization model for feeder bus of rail transit [J]. 吉林大学学报(工学版), 2009, 39(06): 1469-1474.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LI Shoutao, LI Yuanchun. Autonomous Mobile Robot Control Algorithm Based on Hierarchical Fuzzy Behaviors in Unknown Environments[J]. 吉林大学学报(工学版), 2005, 35(04): 391 -397 .
[2]	Liu Qing-min，Wang Long-shan，Chen Xiang-wei，Li Guo-fa. Ball nut detection by machine vision[J]. 吉林大学学报(工学版), 2006, 36(04): 534 -538 .
[3]	Li Hong-ying; Shi Wei-guang;Gan Shu-cai. Electromagnetic properties and microwave absorbing property of Z type hexaferrite Ba_3-xLa_xCo₂Fe₂₄O₄₁[J]. 吉林大学学报(工学版), 2006, 36(06): 856 -0860 .
[4]	Zhang Quan-fa，Li Ming-zhe，Sun Gang，Ge Xin . Comparison between flexible and rigid blank-holding in multi-point forming[J]. 吉林大学学报(工学版), 2007, 37(01): 25 -30 .
[5]	Yang Shu-kai, Song Chuan-xue, An Xiao-juan, Cai Zhang-lin . Analyzing effects of suspension bushing elasticity on vehicle yaw response character with virtual prototype method[J]. 吉林大学学报(工学版), 2007, 37(05): 994 -0999 .
[6]	. [J]. 吉林大学学报(工学版), 2007, 37(06): 1284 -1287 .
[7]	Che Xiang-jiu，Liu Da-you，Wang Zheng-xuan . Construction of joining surface with G¹ continuity for two NURBS surfaces[J]. 吉林大学学报(工学版), 2007, 37(04): 838 -841 .
[8]	Liu Han-bing, Jiao Yu-ling, Liang Chun-yu,Qin Wei-jun . Effect of shape function on computing precision in meshless methods[J]. 吉林大学学报(工学版), 2007, 37(03): 715 -0720 .
[9]	. [J]. 吉林大学学报(工学版), 2007, 37(04): 0 .
[10]	Li Yue-ying，Liu Yong-bing，Chen Hua . Surface hardening and tribological properties of a cam materials[J]. 吉林大学学报(工学版), 2007, 37(05): 1064 -1068 .

影响因素	指标名称	描述	数据来源
土地属性	人口岗位总量	交通小区的人口总量+岗位总量	人口普查+手机信令数据
土地属性	平均房价	交通小区的平均房价	安居客、链家等
交通条件	公交线路条数	交通小区内所有出入口200 m范围内公交站点的公交线路总条数	地图
车站属性	是否为换乘站	交通小区内的车站是否为换乘站。“否”为1，“是”为2	北京地铁官网
车站属性	出入口数量	交通小区内车站出入口数量。同交通小区同线路距离在50 m内的多个出入口按一个计算	北京地铁官网

车站	交通小区	人口岗位总量/个	平均房价/（元·m^-2）	公交线路条数/条	是否为换乘站	出入口数量/个
崇文门	东北	7 383	116 774	1	2	1
崇文门	东南	16 409	118 288	2	2	1
崇文门	西北	13 982	118 048	15	2	3
崇文门	西南	43 988	113 987	12	2	3
东四	东北	27 493	103 563	5	2	1
东四	东南	13 543	133 814	11	2	1
东四	西北	39 506	138 365	6	2	1
东四	西南	12 688	139 538	11	2	2
劲松	东北	38 654	61 363	16	1	1
劲松	东南	46 069	63 917	11	1	1
劲松	西南	23 658	63 880	2	1	1
劲松	西北	14 617	62 481	6	1	1
潘家园	北	59 406	62 399	6	1	1
潘家园	南	46 162	55 766	6	1	2
金台夕照	东北	77 049	74 335	7	1	1
金台夕照	东南	33 788	113 513	9	1	1
金台夕照	西南	66 227	113 663	1	1	1
金台夕照	西北	67 269	113 477	9	1	1
车道沟	西南	29 204	105 199	8	1	1
车道沟	西北	21 291	112 646	2	1	1
车道沟	东北	29 482	106 012	2	1	1
车道沟	东南	20 004	103 370	12	1	1
西二旗	北	57 288	82 207	4	2	1
西二旗	南	81 693	76 835	2	2	1
望京西	东	28 884	87 343	20	2	3
望京西	西	2 840	156 097	1	2	1
朝阳门	东	110 154	98 420	3	2	1
朝阳门	西北	22 752	117 238	6	2	2
朝阳门	南	62 262	118 486	6	2	2

模型	非标准化系数		标准化偏回归系数	t	Sig.
模型	系数	标准误差	标准化偏回归系数	t	Sig.
ln常量	15.841	3.757	—	4.216	0.000
ln出入口数量（ln E）	0.785	0.219	0.431	3.588	0.002
ln人口岗位总量（ln P）	0.530	0.093	0.592	5.704	0.000
ln公交线路条数（ln B）	-0.196	0.091	-0.240	-2.150	0.042
ln是否为换乘站（ln S）	0.981	0.246	0.486	3.995	0.001
ln平均房价（ln H）	-1.072	0.291	-0.435	-3.683	0.001

影响因素	指标名称	描述	数据来源
出入口使用舒适性	出入口是否直连建筑物	非直连建筑的出入口为1，直连建筑的为2	地图+实地调研
出入口使用舒适性	出入口是否有自动扶梯	无自动扶梯为1；单向扶梯为2；双向扶梯为3	实地调研
出入口接驳便利性	与其他交通小区相连的过街设施数量	出入口200 m范围内，距离该出入口最近的，与其他交通小区相连的过街设施数量	地图
出入口接驳便利性	公交站点数量	出入口200 m范围内，距离该出入口最近的公交站点数量	地图
出入口吸引力	楼宇吸引力	出入口200 m范围内总建筑面积	地图+互联网楼宇数据
出入口吸引力	线路吸引力	如式（2）所示	北京地铁官网，2020年北京市交通发展年度报告

车站	出入口名称	日进站量/人次		误差/%
车站	出入口名称	实际值	预测值	误差/%
白石桥南	A	4 132	3 323	-20
	B	4 333	3 187	-26
	C	8 023	6 962	-13
	E	4 690	5 834	24
	G	7 056	7 775	10
珠市口	A	2 887	2 475	-14
	C	2 558	3 314	30
	D	2 503	3 273	31
	E	606	441	-27
	F	764	637	-17
	G	632	431	-32
角门东	A	7 551	6 559	-13
	B	5 894	5 522	-6
	C	7 364	9 217	25
	D	5 688	5 069	-11
呼家楼	B	5 097	6 020	18
	C	6 554	5 582	-15
	C2	4 007	2 941	-27
	D	5 911	6 852	16
	E	5 622	6 656	18
	G	3 846	4 502	17
育新	H	3 171	3 798	20
	A	5 138	5 978	16
	B	6 031	4 997	-17
	C	4 120	5 326	29
	D	7 048	6 032	-14

Passenger flow prediction at entrance and exit of rail transit stations：a case study of Beijing

RICH HTML

PDF (PC)