Dynamic Performance Evaluation Method for Transfer in Rail Transit Station Based on Station Simulation and LSTM

doi:10.16182/j.issn1004731x.joss.21-1042

Abstract

Abstract:

Given the boom increasing of rail transit passenger volume, the dynamic performance evaluation method for transfer in rail transit stations based on machine learning are proposed to effectively evaluate the performance of the transfer station in different scenarios. Based on the proposed dynamic performance evaluation indexes of effective transfer number, transfer time and congestion, the influence factors of station dynamic performance are analyzed. The simulation model integrated train operation and pedestrian movement is built to provide the time-series data for the machine learning method. The long short-term memory (LSTM) is implemented to forecast the evaluation indicators, and the station evaluation results can be obtained dynamically under different operational conditions. 22,400 samples generated by the simulation model as the train data are used to train the forecasting model with the Xipu station. The forecasting results demonstrate the accuracy of forecasting model. The impact of ticket purchase ratio on the dynamic performance of the station is quantified. The proposed station dynamic performance evaluation method is proved to be effective and efficient for the operation and organization of the transfer station.

Key words: rail transit, transfer station, dynamic performance, time series, long short-term memory (LSTM)

CLC Number:

TP391.9

Bisheng He, Hongxiang Zhang, Yongjun Zhu, Gongyuan Lu. Dynamic Performance Evaluation Method for Transfer in Rail Transit Station Based on Station Simulation and LSTM[J]. Journal of System Simulation, 2023, 35(3): 544-556.

Figures/Tables 11

Table 1

Fig. 1

Table 2

Fig. 2

Fig. 3

Table 3

Value ranges of influence factors of dynamic performance under different time period types

影响因素	高峰	平峰	低峰
$D 1 t r a$	384~448	320~384	256~320
$R 1 b$	0.25~0.55	0.25~0.55	0.25~0.55
$D 1 A$	560~640	480~560	400~480
$D 1 i n$	6 000	4 800	3 600
$D 2 t r a$	72~90	54~72	36~54
$R 2 b$	0.15~0.4	0.15~0.4	0.15~0.4
$D 2 A$	280~360	200~280	120~200
( $N 1 A G, r a i l$ , $N T V M$ , $N 1 A G, m e t r o$ )	(7, 8, 7), (7, 7, 6)	(6, 7, 6), (6, 6, 5), (5, 6, 5)	(5, 5, 4), (4, 5, 4)
( $N 2 A G, r a i l$ , $N 2 A G, m e t r o$ )	(7, 5), (6, 5)	(5, 5), (5, 4)	(4, 4), (4, 3)
$N I D$	(2/2)	(3/1)	(4/0)
( $H a r i_t r a i n$ , $T c h e c k$ )	(10, 8)	(12, 10), (20, 15)	(30, 25)
$H a r i_m e t r o$	2, 2.5	3, 3.5	4

Table 3

Table 4

Part of the feature values of simulation scene and simulation results

仿真场景序号	仿真场景特征取值						仿真运行结果			结果校核
仿真场景序号	$D 1 t r a$	$R 1 b$	$D 1 A$	$D 1 i n$	( $N 1 A G, r a i l$ , $N T V M$ , $N 1 A G, m e t r o$ )	$H a r i_m e t r o$	$P τ$	$A N t r a, τ$	$T t r a, τ$	$A N t r a, τ$ 相对偏差/%	$T t r a, τ$ 相对偏差/%
1	430	0.35	625	6 000	(7, 8, 7)	2	A	428	4.35	6.92	5.36
2	445	0.40	620	6 000	(7, 7, 6)	2	A	419	4.76	2.57	3.16
3	370	0.35	550	4 800	(5, 6, 5)	3	B	359	4.99	5.33	4.40
4	365	0.40	550	4 800	(6, 6, 5)	3	B	347	5.19	2.98	7.02
5	310	0.40	460	3 600	(5, 5, 4)	4	B	302	5.58	3.72	6.59

Table 4

Table 5

Fig. 4

Fig. 5

Table 6

Evaluation indicators changes of forecasting results for transferring from Chengguan railway to metro

购票比例	50%			40%			30%			25%
评价指标	$P p r e τ$	$A N p r e t r a, τ$	$T p r e t r a, τ$	$P p r e τ$	$A N p r e t r a, τ$	$T p r e t r a, τ$	$P p r e τ$	$A N p r e t r a, τ$	$T p r e t r a, τ$	$P p r e τ$	$A N p r e t r a, τ$	$T p r e t r a, τ$
06:00—07:00	A	265	4.28	A	270	4.15	A	280	3.95	A	279	3.88
07:00—08:00	D	328	7.15	C	377	5.51	B	406	4.13	A	413	4.05
08:00—09:00	D	322	7.15	C	378	5.39	A	411	4.05	A	409	4.01
09:00—10:00	C	320	6.67	B	349	4.70	A	353	4.06	A	359	4.02
10:00—11:00	B	327	5.89	A	352	4.49	A	351	3.88	A	352	3.99
11:00—12:00	B	329	6.01	B	354	4.73	B	351	4.03	A	355	3.94
12:00—13:00	C	323	6.58	B	348	4.68	A	358	3.92	A	360	4.01
13:00—14:00	B	315	5.99	B	358	4.67	A	358	4.03	A	358	3.98
14:00—15:00	B	329	5.95	B	350	4.59	A	354	4.04	A	351	4.04
15:00—16:00	B	325	6.00	B	356	4.68	A	353	4.06	A	351	4.03
16:00—17:00	B	330	6.61	B	350	4.71	A	359	3.99	A	357	4.07
17:00—18:00	D	319	7.08	C	381	5.48	B	410	4.17	B	414	4.11
18:00—19:00	D	322	7.03	C	379	5.53	A	409	4.03	A	415	4.06
19:00—20:00	C	322	6.45	B	355	4.80	A	355	3.96	A	354	3.93
20:00—21:00	B	323	6.02	B	352	4.61	A	352	4.01	A	356	4.02
21:00—22:00	B	293	5.51	A	290	4.19	A	298	3.89	A	299	3.91

Table 6

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拥挤度等级	通行能力/(人l·(m·min)^-1)	行人速度/(m·s^-1)	人均空间/(m²/人)
A	≤16	>1.28	>3.85
B	16~27	1.11~1.28	2.15~3.85
C	27~42	0.91~1.11	1.37~2.15
D	42~68	0.69~0.91	0.86~1.37
E	68~97	0.55~0.69	0.54~0.86
F	波动	<0.55	<0.54

乘客因素	设备设施因素	行车组织因素
进站客流量Dⁱⁿ	流线布设方案F	换乘列车开行间隔H^tra
出站客流量D^out	楼梯宽度W	列车停站时间T^stop
到站客流量D^A	扶梯运行速度S^E	列车满载率(城市轨道交通)R^M
换乘客流量D^tra	安检设备开放数量N^SC	提前检票时长(铁路)T^check
乘客走行速度S^d	自动取/售票机开放数量N^TVM	到达列车开行间隔H^ari
使用单程票的比例R^b	检票闸机开放数量N^AG
	实名制验证开放数量N^ID

预测项	MAPE/%
拥挤度	3.60
平均换乘时间(换乘地铁)	1.90
有效换乘人数(换乘地铁)	4.38
平均换乘时间(换乘铁路)	3.23
有效换乘人数(换乘铁路)	0.70

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