prof. haiying li state key laboratory of rail traffic control and … · g n e i i) b b e n 0 2 4 6...
TRANSCRIPT
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Metro Passenger Flow Management in a Megacity:
Challenges and Experiences in Beijing
International Workshop on High-Speed Rail Planning and OperationsWashington D.C., Oct 30, 2015
Prof. Haiying Li
State Key Laboratory of Rail Traffic Control and Safety
Beijing Jiaotong University
Beijing, China
E-mail: [email protected]
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OutlineChallenge
• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Calculation and evaluation of station capacity
• Metro operation simulation
• Passenger flow control
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Metro map of Beijing in 2015
(18 lines,660km)
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4
Metro planning of Beijing in 2020(30 lines,1177km)
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In 2020, the network of Beijing subway will consist of 30 lines, and the
operation mileage will be 1177km.
1969 2000 2008 2011 2012 2013 2014 2015
Number of lines
1 2 8 15 16 17 18 20
Number of stations
16 41 123 215 261 276 325 350
Operationmileage
(km)23 54 200 372 442 465 527 660
Daily passenger
volume(million)
— 1.20 3.33 6.01 6.72 8.65 9.56 10.46
Operator Beijing subway Beijing subway, Beijing MTR
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6
Metro map of Shanghai in 2015(14lines,548km)
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7
Metro planning of Shanghai in 2020(18 lines,800km)
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8
Metro map of Guangzhou in 2015(9 lines, 260km)
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Metro planning of Guangzhou in 2020(17 lines,677km)
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Outline
Challenge• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Calculation and evaluation of station capacity
• Metro operation simulation
• Passenger flow control
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Load factor
>120%
100%~120%
80%~100%
0%~80%
Passenger Status of Beijing Subway during morning peak hours
(8:15-8:30, Wednesday, May 20, 2015)
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Outline
Challenge• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Calculation and evaluation of station capacity
• Metro operation simulation
• Passenger flow control
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20082007
2009
2003Before 2003
2010
Isolated Lines → Network
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Outline
Challenge• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Calculation and evaluation of station capacity
• Metro operation simulation
• Passenger flow control
*
Beijing MTR Corporation
Beijing Subway
16lines
2lines
Line 4, Daxing Line, Line 14
2 Operators since 2009
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Outline
Challenge• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Calculation and evaluation of station capacity
• Metro operation simulation
• Passenger flow control
*
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• Project
• “Transport organization key technologies and system development
on urban rail transit network”
• Participant Organization
• State Key Laboratory of Rail Traffic Control and Safety(RCS) of
Beijing Jiaotong University
• Beijing Metro Network Control Center(TCC)
Experience in Beijing
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Objective of the Project
• Analysis of passenger demand
• Deployment of transportation capacity
• Coordination among stations, lines and
network
• Corresponding evaluation method and
standard for operators
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Experience in Beijing• During 2011-2014, we established the “Networked-operation
Decision Center”(NDC)
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Experience in Beijing
• NDC has been deployed in the information system of
Beijing Metro Network Control Center.
• It can analyze the impact of new lines to existing lines, and
evaluate the coordination of capacity and demand.
• It also can be used as a reference to the design of stations
and lines in urban rail transit.
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Outline
Challenge• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Calculation and evaluation of station capacity
• Metro operation simulation
• Passenger flow control
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Page 23
Passenger flow forecast of new lines
The passenger volume impact of new lines to transfer stations
The passenger volume forecast of new lines to an existing line
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24
Multi-resolution modeling and simulation:
Station Simulation
Station simulation
of Pinganli Station
Line 6
Line 4
Space occupancy of
Pinganli Station
A multi-agent-based microscopic pedestrian simulation model in the
station is proposed.
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25
Multi-resolution modeling and simulation:
Facility capacity calculationBased on the concept of the stress test, the facility capacity can
be calculated by the station simulation system.
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Identification and Relief of Bottlenecks
According to the bottleneck transmission characteristics, we
proposed a bottleneck identification method and the sensitivity
analysis-based bottleneck relief strategy.
Bottleneck
Non-Bottleneck
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1B*
2B
1v
2v3v
4v5v
6v10v
7v9v
8v
11v15a
45a
32a
57a
65a53a
78a
86a108a
89a 911a
1110a
Entra-
nce
A
Stair1 Walkway1 In-gate A
Escalator1 Walkway2 Out-gate A
In-gate D
Stair2
Escalator3
Walkway3
Entra-
nce
D
Entr-
ance
B
Stair4Walkway5
Escalator2
Walkway4Out-gate B
Walkway5
Walkway3
Walkway6
Entr-
ance
C
Stair3
West
hall
Eest
hallIsland
Platform
Up
Down
Stair5
Stair6
Stair7
Stair8
Out-
gate C
In-gate B
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Evaluation of Station Capacity
Indexes of station efficiency Capacity utilization rate of facilities
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Multi-resolution modeling and simulation:
Network Simulation
•Network topology model building
•Simulation experimental project
management
•Simulation experiment process
control
•Passenger distribution
•Simulation data acquisition and
processing
•Visual display of simulation process
•Calculation and display of evaluation
indexes
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Outline
Challenge• Fast development of urban rail network
• Passenger volume increases rapidly
• Isolated operation→ Network operation
• Single operator→ Multi-operator
Experience• Passenger flow forecast
• Metro operation simulation
• Calculation and evaluation of station capacity
• Passenger flow control
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Metro Passenger flow control:
Boarding-limiting method65 constant boarding-limiting stations in Beijing
58 stations at morning peak hours
19 stations at evening peak hours
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Inbound passenger flow control
Control the number of inbound passengers per unit time,
to reduce the load of facilities in the station.
Fencing in the entrances
Adjust the number and the location of the ticket gates
Change the speed of ticket sale
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Transfer passenger flow control
Control the transfer passenger volume per unit time from
one line to another, to influence the number of boarding
passengers of another line and passenger route choice.
Adjust the width of transfer walkway
Change the transfer streamline
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Multi-station Collaborative Boarding-Limiting
(MCB) •During rush hours, intensive passenger flow usually appears not
only at a single station but also at several adjacent stations on
the urban rail transit network.
•The organization scheme of an upstream station will influence the
passenger boarding at several downstream stations
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Caofang-Changying
Changying-Huangqu
Huangqu-Dalianpo
Dalianpo-Qingnianlu
Qingnianlu-Shilibao
Shilibao-Jintailu
Jintailu-Hujialou
Hujialou-Dongdaqiao
Secti
on
lo
ad
fa
cto
r
Section
load factor>1.0
load factor<1.0
0
1000
2000
3000
4000
5000
6000
7000
8000
Caofang Changying Huangqu dalianpo Qingnianlu Shilibao Jintailu Hujialou
Inb
ou
nd
Pa
ssen
ger V
olu
me(
pa
ssen
ger p
er h
ou
r)
Station
Inbound passenger volume>5000
Inbound passenger volume<5000
Section load factor of line 6
during morning peak hours
Inbound passenger volume of line 6
during morning peak hours
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Station A Station B Station C Station D
Station A Station B Station C Station D
Station E
Line 1
Line 2 Station A Station B Station C Station D
Station E
Line 1
Line 2
Boarding-LimitTransfer
The influence of the MCB method
on the station congestion
The same line
Before MCB
Before MCB
After MCB
After MCB
The adjacent line
Station A Station B Station C Station D
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The influence of the MCB method
on the route choice
Before MCB After MCB
Station A Station B
Station C Station D
Line 2Line 1
Line3
Line4
Route2
Route1
Station A Station B
Station C
Station D
Line3
Line4
Line 2Line 1 TransferBoarding-Limit
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MCB Model
Assumption
(1) The OD demand of the network is fixed.
(2) The inbound passengers enter the station at a constant speed.
(3) The headway of each line, running time in the each section and
dwelling time at each station are fixed.
Decision variable
Number of inbound passengers taking line r at station n per unit time
Number of transfer passengers taking line r at station n per unit time
Control the number of passengers boarding at each station
to allocate the train capacity reasonably.
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MCB Model: Object function
(1) Maximize the number of boarding passengers
It can be calculated by inbound and transfer passenger volume
per unit time.
(2) Reduce the congestion in the key stations.
The key stations means the stations that suffer huge passenger
volume. More passengers should be allowed to board the trains
than other stations.
This objective can be expressed by the variable coefficient of the
ratio of boarding passenger volume to passenger demand.
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MCB Model: Constraints
(1) Passenger demand constraints
(2) Dwell time constraints
(3) Train capacity constraints
The boarding passenger volume should be less than
passenger demand per unit time.
The passengers on the platform should be able to board
during dwell time.
The number of passengers on the platform should be less
than the residual train capacity.
(4) Transfer walkway capacity constraint
The number of boarding-limiting transfer passengers
should be less than the transfer walkway capacity.
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MSA Algorithm
Object function(1)Maximum boarding passengers
(2)Reduce the congestion in the key
stations
The MCB Scheme
GA algorithm
Update route choice
Multi-objective
Optimization
Decision Variable(1) Inbound passenger volume per unit time(2) Transfer passenger volume per unit time
Passenger assignment
with multi-path and
varying impedance
MCB Model: Algorithm framework
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Xi’erqi
Shangdi
Wudaokou
Zhichunlu Xitucheng JiandemenMudanyuan Beitucheng
Olympic Green
Olympic
Sports Center
Lincuiqiao
Yongtaizhuang
Xixiaokou
Huoying
Yuxin
Huilongguandongdajie
PingxifuYuzhiluZhuxinzhuang
Gonghuacheng
Life Science Park
Line 08
Line 10
Line 13
Line 15
Changping
Line
HuilongguanLongze
Qinghuadongluxikou
Liudaokou
Beishatan
Shahe
Non-Boarding-limiting
Station
Boarding-limiting
Station
Shahe University Park
Nanshao
South Gate of
Forest Park
Adjacent boarding-limiting station
Case Study
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(b) Changping Line
(a)Line 13
The numbers of boarding passengers before and after MCB
(Line 13 and Changping Line)
0
50
100
150
200
250
300
350
400
Huoying Huilongguan longze Xierqi Shangdi
Pa
ssen
ger
Vo
lum
e (p
ass
eng
er p
er h
ou
r) Before MCB
After MCB
Arriving Rate
Station
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Huoying-
Huilongguan
Huilongguan-
Lingze
Longze-
Xierqi
Xierqi-
Shangdi
Shangdi-
Wudaokou
Sec
tio
n L
oa
d F
act
or
Before MCB
After MCB
Section
0
20
40
60
80
100
120
Nanshao Shahe
UniversityPark
Shahe Gonghuacheng Zhuxinzhuang Life
SciencePark
Pa
ssen
ger
Vo
lum
e (p
ass
eng
er p
er h
ou
r) Before MCB
After MCB
Arriving Rate
Station
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Nanshao-
Shahe
University
Park
Shahe
University
Park-
Shahe
Shahe-
Gonghuacheng
Gonghuacheng-
Zhuxinzhuang
Zhuxinzhuang-
Life
Science
Park
Life
Science
Park-
Xierqi
Sec
tio
n L
oa
d F
act
or
Before MCB
After MCB
Section
*The loading factor before and after MCB
(Line 13 and Changping Line)
(b) Line 10(a)Line 13
0
0.2
0.4
0.6
0.8
1
1.2
Beitucheng-Jiandemen Jiandemen-Mudanyuan Mudanyuan-Xitucheng Xitucheng-Zhichunlu
Secti
on
Lo
ad
fa
cto
r
Before MCB
After MCB
Section
0.00
0.20
0.40
0.60
0.80
1.00
1.20
Huilongguandongdajie-Huoying
Huoying-Yuxin
Yuxin-Xixiaokou
Xixiaokou-Yongtaizhuang
Yongtaizhuang-Lincuiqiao
Lincuiqiao-South Gate of Forest Park
South Gate of Forest Park-
Olympic Green
Olympic Green-Olympic Sports
Center
Olympic SportsCenter-
Beitucheng
Sectio
n L
oa
d f
acto
r
Before MCB
After MCB
Section
Xi’erqi
Shangdi
Wudaokou
Zhichunlu Xitucheng JiandemenMudanyuan Beitucheng
Olympic Green
Olympic
Sports Center
Lincuiqiao
Yongtaizhuang
Xixiaokou
Huoying
Yuxin
Huilongguandongdajie
PingxifuYuzhiluZhuxinzhuang
Gonghuacheng
Life Science Park
Line 08
Line 10
Line 13
Line 15
Changping
Line
HuilongguanLongze
Qinghuadongluxikou
Liudaokou
Beishatan
Shahe
Non-Boarding-limiting
Station
Boarding-limiting
Station
Shahe University Park
Nanshao
South Gate of
Forest Park
•The section load factors of Line 8
and Line 10 rise slightly. This
passenger flow is transferred from
heavily jammed Line 13 to less
crowded Line 8 and Line 10.
•Taking line 13 and Changping Line
into consideration, the total number
of boarding passengers increases by
600 passengers per hour after MCB.
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Thank you for your attention!
Prof. Haiying Li
State Key Laboratory of Rail Traffic Control and Safety
Beijing Jiaotong University
Beijing, China
E-mail: [email protected]
International Workshop on High-Speed Rail Planning and OperationsWashington D.C., Oct 30, 2015