Metro Passenger Flow Management in a Megacity: Challenges and Experiences in Beijing

Transcription

1 Metro Passenger Flow Management in a Megacity: Challenges and Experiences in Beijing Prof. Haiying Li State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing, China hyli@bjtu.edu.cn International Workshop on High-Speed Rail Planning and Operations Washington D.C., Oct 30, 2015

2 Challenge Outline 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

3 Metro map of Beijing in 2015 (18 lines,660km)

4 Metro planning of Beijing in 2020 (30 lines,1177km) 4

5 In 2020, the network of Beijing subway will consist of 30 lines, and the operation mileage will be 1177km Number of lines Number of stations Operation mileage (km) Daily passenger volume (million) Operator Beijing subway Beijing subway, Beijing MTR

6 Metro map of Shanghai in 2015 (14lines,548km) 6

7 Metro planning of Shanghai in 2020 (18 lines,800km) 7

8 Metro map of Guangzhou in 2015 (9 lines, 260km) 8

9 Metro planning of Guangzhou in 2020 (17 lines,677km) 9

10 Challenge Outline 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 10

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12 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)

13 Challenge Outline 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 13

14 Isolated Lines Network Before

15 Challenge Outline 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

16 2 Operators since lines Beijing Subway Beijing MTR Corporation 2lines Line 4, Daxing Line, Line 14

17 Challenge Outline 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

18 Project Experience in Beijing 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) 18

19 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 19

20 Experience in Beijing During , we established the Networked-operation Decision Center (NDC) 20

21 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.

22 Challenge Outline 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

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 Page 23

24 Multi-resolution modeling and simulation: Station Simulation A multi-agent-based microscopic pedestrian simulation model in the station is proposed. Line 4 Line 6 Station simulation of Pinganli Station Space occupancy of Pinganli Station 24 24

25 Multi-resolution modeling and simulation: Facility capacity calculation Based on the concept of the stress test, the facility capacity can be calculated by the station simulation system. 25

26 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. Entrance A Stair1 Walkway1 In-gate A Escalator1 Walkway2 Out-gate A West hall Stair5 Up Island Platform Stair7 Eest hall Out-gate B In-gate B Walkway4 Walkway5 Stair3 Escalator2 Stair4 Entrance B Entrance D In-gate D Stair2 Escalator3 Walkway3 Stair6 Down Stair8 Walkway6 Walkway5 Walkway3 Outgate C Entrance C v 2 B 2 v 3 v 1 a 15 a 53 a 32 v a 45 4 v 5 a 57 a 65 v7 v9 a 78 B 1 a a v 86 8 a108 v6 v10 a a 1110 v 11 Bottleneck Non-Bottleneck

27 Evaluation of Station Capacity Indexes of station efficiency Capacity utilization rate of facilities

28 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

29 Challenge Outline 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 29

30 Metro Passenger flow control: Boarding-limiting method 65 constant boarding-limiting stations in Beijing 58 stations at morning peak hours 19 stations at evening peak hours

31 31 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

32 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

33 Section load factor Inbound Passenger Volume(passenger per hour) 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 load factor>1.0 load factor< Inbound passenger volume>5000 Inbound passenger volume< Caofang- Changying Changying- Huangqu Huangqu- Dalianpo Dalianpo- Qingnianlu Section Qingnianlu- Shilibao Shilibao- Jintailu Jintailu- Hujialou Section load factor of line 6 during morning peak hours Hujialou- Dongdaqiao 0 Caofang Changying Huangqu dalianpo Qingnianlu Shilibao Jintailu Hujialou Station Inbound passenger volume of line 6 during morning peak hours 33

34 The influence of the MCB method on the station congestion Station A Station B Station C Station D The same line Station A Station B Station C Station D Before MCB After MCB Line 2 Station A Station B Station C Station D Line 1 The adjacent line Line 2 Station A Station B Station C Station D Transfer Boarding-Limit Line 1 Station E Before MCB Station E After MCB

35 The influence of the MCB method on the route choice Line 1 Line 2 Line 1 Line 2 Transfer Boarding-Limit Station A Route1 Station B Line3 Station A Station B Line3 Station C Route2 Station D Line4 Station C Station D Line4 Before MCB After MCB

36 MCB Model Control the number of passengers boarding at each station to allocate the train capacity reasonably. 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

37 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. 37

38 MCB Model: Constraints (1) Passenger demand constraints The boarding passenger volume should be less than passenger demand per unit time. (2) Dwell time constraints The passengers on the platform should be able to board during dwell time. (3) Train capacity constraints 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

39 MCB Model: Algorithm framework Decision Variable (1) Inbound passenger volume per unit time (2) Transfer passenger volume per unit time MSA Algorithm Update route choice Passenger assignment with multi-path and varying impedance Object function (1)Maximum boarding passengers (2)Reduce the congestion in the key stations Multi-objective Optimization GA algorithm The MCB Scheme

40 Case Study Nanshao Shahe University Park Shahe Gonghuacheng Zhuxinzhuang Yuzhilu Pingxifu Adjacent boarding-limiting station Life Science Park Longze Huilongguandongdajie Huilongguan Huoying Xi erqi Yuxin Xixiaokou Shangdi Wudaokou Yongtaizhuang Lincuiqiao Qinghuadongluxikou Beishatan Liudaokou South Gate of Forest Park Olympic Green Olympic Sports Center Boarding-limiting Station Non-Boarding-limiting Station Line 08 Line 10 Line 13 Line 15 Zhichunlu Xitucheng Mudanyuan Jiandemen Beitucheng Changping Line

41 Passenger Volume (passenger per hour) Section Load Factor Section Load Factor Passenger Volume (passenger per hour) Before MCB After MCB Arriving Rate Huoying Huilongguan longze Xierqi Shangdi Station (a)line Huoying- Huilongguan Huilongguan- Lingze Longze- Xierqi Section Xierqi- Shangdi Before MCB After MCB Shangdi- Wudaokou Before MCB After MCB Arriving Rate Before MCB After MCB 0 Nanshao Shahe University Park Shahe Gonghuacheng Zhuxinzhuang Life Science Station Park (b) Changping Line 0 Nanshao- Shahe University Park Shahe University Park- Shahe Shahe- Gonghuacheng Section Gonghuacheng- Zhuxinzhuang Zhuxinzhuang- Life Science Park Life Science Park- Xierqi The numbers of boarding passengers before and after MCB (Line 13 and Changping Line)

42 Section Load factor Section Load factor Nanshao Shahe University Park Life Science Park Xi erqi Shangdi Wudaokou Shahe Gonghuacheng Zhuxinzhuang Longze Yuzhilu Pingxifu Huilongguandongdajie Huilongguan Yongtaizhuang Lincuiqiao Qinghuadongluxikou Beishatan Liudaokou Yuxin Xixiaokou Zhichunlu Xitucheng Mudanyuan Jiandemen Huoying South Gate of Forest Park Olympic Green Olympic Sports Center Beitucheng Boarding-limiting Station Non-Boarding-limiting Station Line 08 Line 10 Line 13 Line 15 Changping Line 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 Before MCB After MCB Before MCB After MCB 0.00 Huilongguandongdajie- Huoying Huoying- Yuxin Yuxin- Xixiaokou Xixiaokou- Yongtaizhuang Yongtaizhuang- Lincuiqiao Section Lincuiqiao- South Gate of Forest Park (a)line 13 South Gate of Forest Park- Olympic Green Olympic Green- Olympic Sports Center Olympic Sports Center- Beitucheng 0 Beitucheng-Jiandemen Jiandemen-Mudanyuan Mudanyuan-Xitucheng Xitucheng-Zhichunlu Section (b) Line 10 The loading factor before and after MCB (Line 13 and Changping Line)

43 Thank you for your attention! Prof. Haiying Li State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing, China International Workshop on High-Speed Rail Planning and Operations Washington D.C., Oct 30, 2015