Railway Technology Research Center, National Taiwan...
Transcript of Railway Technology Research Center, National Taiwan...
Development of the Evaluation Process and Models for Metro System Service Stability and Efficiency
Railway Technology Research Center, National Taiwan University
Yung-Cheng (Rex) Lai
14th September, 2012
Presentation at the William W. Hay Railroad Engineering Seminar
• National Taiwan University – B.A., Civil Engineering, 2002
• University of Illinois at Urbana-Champaign – M.S., Civil & Environmental Engineering, 2004
• University of Illinois at Urbana-Champaign – Ph.D., Civil & Environmental Engineering, 2008
Education
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• Rail Transportation System
– Railway Capacity Analysis (Service Performance & Evaluation)
– Railway Operations and Management (Service Design)
– Railway Safety
• Techniques:
– Math Programming
– Heuristic or Decomposition
Methods
– Simulations
Research Interests
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Selected Research Topics on Rail Capacity
• Capacity Evaluation & Computation
– Impact of Key Capacity Factors (heterogeneity, priority, etc.)
– Development of Rail Capacity Models
– Capacity Utilization Efficiency & Stability
• Capacity Management & Investment
– Decision Support Framework for Strategic Capacity Planning
– High Speed Route Improvement Optimizer
– Optimization of Train Network Routing with Heterogeneous Traffic
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Development of the Evaluation Process and Models for Metro System Service Stability and Efficiency
Glass Cup Theory – Tradeoff
between Efficiency and Stability
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• Metro System – Capacity
(Metro Assets) – Used Capacity
(Assets Utilization) – Available Capacity
(Slacks) – System Failure
(Disruption)
• Glass – Size
(Existing Resource) – Water Level
(Resource Usage) – Empty Space
(Buffer) – Vibration
(Disruption)
Higher Assets Utilization May Cause Lower System Stability
Every System has its own “optimal balance”
Evaluation Framework and Models
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Operational Stability and Efficiency
System Reliability and Maintainability
Metro Service Plan
System Characteristics
System Characteristics
Metro Service Plan
Historical Disturbance Data
Operational Efficiency
Operational Stability
Capacity Analysis Module Reliability Module
Downgraded Capacity
Maintainability Distribution
Reliability Distribution
Normal Capacity
Mean and Variance of the Expected Recovery Time
Percentage of the Capacity Usage
Operational Stability and Efficiency Module
Used Capacity
Operational Efficiency - Assets Utilization Efficiency
Percentage of the Capacity Usage
Normal Capacity
Percentage of the Capacity Usage
𝜂 =𝑢
𝐶𝑠𝑐× 100%
𝑤ℎ𝑒𝑟𝑒: 𝜂: 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 % ; 𝑢: 𝑢𝑠𝑒𝑑 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑡𝑟𝑎𝑖𝑛𝑠/ℎ𝑜𝑢𝑟 ; 𝑎𝑛𝑑 𝐶𝑠𝑐: 𝑛𝑜𝑟𝑚𝑎𝑙 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 (𝑡𝑟𝑎𝑖𝑛𝑠/ℎ𝑜𝑢𝑟)
Available Capacity
Used Capacity
Normal Capacity
Used Capacity
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× 𝟏𝟎𝟎%
Operational Stability - Expected Recovery Time
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Disturbed Trains
Traffic Flow (Trains/hour)
T1 T2 T3 T4 T5 Time (Hour)
Normal Capacity
Downgraded Capacity
Available Capacity
Used Capacity
Disturbance
Disturbed Trains
Recovery Time
Service Plan (headway)
Repair Time
Expected Recovery Time = Risk in Capacity Utilization
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Metro Operation Is Not Always Under Disruptions System Instability Is Introduced Through the Concept of
Expected Value in Probability Theory
Expected Recovery Time
Recovery Time
Probability of System Failures
1 tF t e
Failure Rate
t Exposure (e.g. train-hours)
Historical Disturbance Data
Repair Time
P(x)
Repair Time Is Related to the System Maintainability
• Expected recovery time inherits uncertainty from the stochastic properties of maintainability
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Maintainability Distribution
Evaluation Framework and Models
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Operational Stability and Efficiency
System Reliability and Maintainability
Metro Service Plan
System Characteristics
System Characteristics
Metro Service Plan
Historical Disturbance Data
Operational Efficiency
Operational Stability
Capacity Analysis Module Reliability Module
Downgraded Capacity
Maintainability Distribution
Reliability Distribution
Normal Capacity
Mean and Variance of the Expected Recovery Time
Percentage of the Capacity Usage
Operational Stability and Efficiency Module
Used Capacity
A Case Study was Conducted for a Metro System
• Service Plan – Weekday service plan – Weekend service plan
• System Characteristics – 20+ intermediate stations – 2 terminal stations
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• Historical Disturbance Data – Totally around 200 recorded
disturbances were collected for a ten-month period
Operational Stability and Efficiency
Evaluation Model
Inputs • System
Characteristics • Service Plan • Historical
Disturbance Data
Outputs • Mean and Standard
Deviation of Expected Recovery Time
• Percentage of Capacity Usage
System Map (adjusted)
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D1 (Depot)
DR1 (Depot)
B10
B9
B9
B8
B8
B7
B7
B3
B5
B5
B4
B4
B2
B2
BR2
BR2
BR3
BR3
BR4
BR4
BR9
BR9
BR10
BR10
BR11
BR11
BR12
BR12
B6
B6
B1 BR1
BR5
BR5
BR6
BR6
BR7
BR7
BR8
BR8
Determine the Maintainability
• The classification of disturbances can facilitate the improvement of operational performance as it provides information about the sources of instability
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Disturbance Mean Time to Repair
Train Failure Level 1 3.10
Train Failure Level 2 8.62
Loss of Electrical Power
11.01
Line Obstruction 7.89
Signal Failure 4.67
Communication Failure 13.98
Others 3.65
Probability of System Failure
• The operational stability is composed of – Severity of disturbances (Maintainability) – Frequency of disturbances (Reliability)
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Disturbance λ
(failure/train-hour)
Train Failure Level 1 2.00E-04
Train Failure Level 2 2.18E-04
Loss of Electrical Power 3.12E-05
Line Obstructed 4.01E-05 Signal Failure 1.42E-04
Communication Failure 7.26E-05
Others 1.58E-05
1 tF t e
Failure Rate
t Exposure (e.g. train-hours)
Overall Evaluation Results
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Weekday Service Plan Weekend Service Plan
D1-bound DR1-
bound Overall D1-bound
DR1-bound
Overall
Expected Recovery
Time (min)
Mean 0.677 0.679 1.356 0.220 0.219 0.438
Standard Deviation
0.016 0.016 0.023 0.005 0.005 0.006
Average Operational Efficiency (%)
30.37 31.53 30.95 20.81 21.60 21.21
+210%
+46%
Substantial Increase (>200%) in Operational Instability with Relatively Small Increase (46%) in Operational Efficiency
Operational Efficiency 3D-histograms (Weekend)
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Relatively High Operational Efficiency happen at Terminal Sections and Sections near Station BR4
D1-bound DR1-bound
Relatively High Expected Recovery Time is observed at Terminal Sections and Sections near Station BR4
Expected Recovery Time 3D-histograms (Weekend)
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D1-bound DR1-bound
Operational Efficiency 3D-histograms (Weekday)
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High Operational Efficiency happen at Terminal Sections and Sections near Station BR4
D1-bound DR1-bound
Expected Recovery Time 3D-histograms (Weekday)
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Terminal Sections and Sections near Station BR4 have High Expected Recovery Time, particularly in Peak Hours
D1-bound DR1-bound
Operational Stability and Efficiency Section Perspective (Weekday)
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0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
Op
era
tio
nal
Eff
icie
ncy
Exp
ect
ed
Re
cove
ry T
ime
(m
in)
Average Expected Recovery Time 2 Standard Deviaiton from the AverageOperational Efficiency
Highest Instability and Efficiency Occur at Sections near Station BR4; Uncertainty Increases with Expected Recovery Time (Variance Increases)
Operational Stability and Efficiency Time Perspective (Weekday)
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0
0.05
0.1
0.15
0.2
0.25
Op
era
tio
nal
Eff
icie
ncy
Exp
ect
ed
Re
cove
ry T
ime
(m
in) Average Expected Recovery Time 2 Standard Deviation from the Average
Operational Efficiency
Highest Instability and Efficiency Occur During Peak Hours
Means to Improve System Stability
Operational Stability and Efficiency
System Reliability and Maintainability
Metro Service Plan
System Characteristics
Improve Capacity
(Upgrade System)
Adjust
Service Plan
Improve System
Stability &
Maintainability
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Evaluation of Improvements
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0
0.5
1
1.5
2
2.5
3
3.5
0 0.1 0.2 0.3 0.4 0.5 0.6
Dai
ly E
xpe
cte
d R
eco
very
Tim
e (
min
)
Daily Operational Efficiency
1.356
1.083
-20%
0.31
Original
Communication System Upgraded
Conclusions
• With the proposed method, metro operators can examine and monitor the stability and efficiency of their operational plan
• Operational instability usually increases with operational efficiency, and the proposed method can help users establish and understand this relationship between stability and efficiency
• This methodology can also be used to justify whether improvement strategies are cost effective
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Future Work
• An operational database should be established to record and continuously update the disturbance data and system characteristics so as to understand the most up-to-date system reliability status
• Future studies should focus on the determination of the optimal balance in operational stability and efficiency
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Operational Stability and Efficiency
System Reliability and Maintainability
Metro Service Plan
System Characteristics
Thank you & Questions?
Yung-Cheng (Rex) Lai
Assistant Professor
Railway Technology Research Center
Department of Civil Engineering
National Taiwan University
E-mail: [email protected]
Phone: +886-2-3366-4243