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Rail Capacity Definition Stringline Diagrams Parametric Models Train Performance Calculators Train Dispatching Simulation
Modeling Rail Capacity
AASHTO SCORT Sept 22, 2010 David Hunt of Oliver Wyman
The maximum number of trains that can be moved between two
locations in a day without exceeding a predefined level of
service.
Suggested Definition of Rail Capacity
Line Capacity Yard Capacity
Crew Capacity Equipment
Capacity
Line Capacity: number of tracks; type and spacing of control system; number, spacing, and length of sidings; mix of train types; operating and maintenance plans
Yard Capacity: total acreage; number of tracks; container storage slots
Crew Capacity: available crew starts; yard crews; maintenance crews
Equipment Capacity: locomotives; railcars; containers/trailers
This presentation will focus on line capacity
Elements in Determining Rail Capacity
Physical Track Layout Number of tracks Type of signals Number and spacing of sidings
Operating Plan Schedules Type of service
Train makeup Number and horsepower of locomotives Train length and weight
Geography Mountains Tunnels Bridges
Some of the Factors Determining Line Capacity
Transportation firms can never utilize a facility 100% of the time
Maintenance Weather Peaking of traffic volumes Disruptions and recoverability Normal variability in operational conditions
Industry practices call for standards to maintain fluidity of operations and avoid major issues at chokepoints Useable (effective) capacity is 70% to 80% of the
maximum (theoretical) capacity Utilizing the capacity buffer between effective and
maximum capacity results in deferred maintenance, reduced ability to react to variability with increasing recovery time, significant reduction in reliability
Maximum Versus Effective Capacity
Graphical depiction of a timetable Provides a visual representation of trains
scheduled to operate on a corridor Typically show stations (space) along the
y-axis and time along the x-axis Also referred to as time-space and time-
distance diagrams Have many uses:
Identification of schedule conflicts (meets/passes)
Identification of slots for new service Scheduling track maintenance Resource planning (crews, locomotives)
Stringlines
Example StringlineTransnet Freight Railroad: Witbank-Komatipoort
Stringlines are a Primary Analytical Tool Used WorldwideExample from Kazakhstan Temir Zholy
Green & Blue = Passenger, Red = Freight, Brown = Locals
Software Products Allow:• Selecting timeframes,
corridors, and trains• Building or adjusting
schedules by adding and dragging strings
SimulationPlanning ModelsInitial Estimation
Uses a commercial rail simulation product (RTC, RAILS, FastTrack)
Requires precise network layout (tracks, sidings, interlockings, signals, etc.)
Requires knowledge of operating plan: trains that will be run and schedules
Initial setup is expensive
Variety of methods, requires more data than back of the envelope but less than a full simulation
Parametric (statistical based) models stem from 1975 FRA work and the CN model (Krueger, 1999)
“Paper” simulations (now evolved to spreadsheets) are also used for capacity estimation
“Back of the envelope” methods
Expert with basic knowledge of number of tracks, type of signals, and special conditions (e.g. mountainous terrain)
Useful for quick assessment of a single corridor or facility
Levels of Effort in Modeling Rail Capacity
Requested by the National Surface Transportation Policy and Revenue Study Commission
Commissioned by the AAR Prepared by Cambridge
Systematics, Inc. Purpose was to estimate
the rail freight infrastructure improvements and investments needed to meet the U.S. DOT’s projected demand for rail freight transportation in 2035
Used STB Waybill data, empty car estimates, and ORNL network attributes
The AAR Approach to Modeling Rail CapacityNational Rail Freight Infrastructure Capacity and Investment Study
> 1.00Unstable flows; service break down conditionsAbove CapacityF
0.8 to 1.0
Very heavy train flow with very limited capacity to accommodate maintenance and recover from incidents
At CapacityE
0.7 to 0.8
Heavy train flow with moderate capacity to accommodate maintenance and recover from incidents
Near CapacityD
0.4 to 0.7C
0.2 to 0.4B
0.0 to 0.2Low to moderate train flows with capacity to accommodate maintenance and recover from incidents
Below Capacity
A
Volume/Capacity RatioDescriptionLOS Grade
> 1.00Unstable flows; service break down conditionsAbove CapacityF
0.8 to 1.0
Very heavy train flow with very limited capacity to accommodate maintenance and recover from incidents
At CapacityE
0.7 to 0.8
Heavy train flow with moderate capacity to accommodate maintenance and recover from incidents
Near CapacityD
0.4 to 0.7C
0.2 to 0.4B
0.0 to 0.2Low to moderate train flows with capacity to accommodate maintenance and recover from incidents
Below Capacity
A
Volume/Capacity RatioDescriptionLOS Grade
AAR Study Recommended Levels of Service for Rail
Source: AAR “National Rail Freight Infrastructure Capacity and Investment Study”, September 2007.
Forecasted Growth of Freight Trains Per Day2035 – Based on US DOT Freight Analysis Framework
Source: AAR “National Rail Freight Infrastructure Capacity and Investment Study”, September 2007.
Type of Control SystemCTC=Blue, ABS=Green, Manual=Red
Number of TracksTwo or More Tracks=Blue, Single Track=Tan
Track Attributes Were used to Determine Capacity
Source: Oak Ridge National Labs rail network. Raw data not verified for accuracy.
Tracks & Control
Practical Max
w/Multiple Trains Types
Practical Max
w/Single Train Type
1 No signal 16 201 ABS 18 252 No signal 28 351 CTC 30 482 ABS 53 802 CTC 75 100
3 CTC 133 163
Capacity Tables Used for AAR Study
Source: AAR “National Rail Freight Infrastructure Capacity and Investment Study”, September 2007.
Future Volumes Compared to Current Capacity45% of Network at Level of Service E or F
Source: AAR “National Rail Freight Infrastructure Capacity and Investment Study”, September 2007.
One form of a parametric model:
Where: C = maximum capacity in trains/day N = number of tracks L = type of control system (categorical variable) S = average spacing between sidings M = mix of train types αi, βj = coefficients … other parameters may be considered
Calibrate model using: Capacity information for selected lines obtained from
the simulation studies
C = β0 x (1 + β1 N)α1 x (1 + β2 L)α2 x (1 + β3 S)α3 x (1 + β4 M)α4 x …
Parametric Modeling
Establish the level of service for each rail line:
Where: R = volume to capacity ratio, from which the
level of service (LOS) is determined V = volume in trains/day C = maximum capacity in trains/day
Identify potential chokepoints: Lines with a LOS of “D”, “E”, or “F”, using the
AAR capacity scale Other special considerations (e.g. tunnels,
bridges)
R = V / C
Reporting Parametric Model Results Using AAR V/C
The Canadian National parametric model is the best known example It was designed for single track corridors Does not handle complex track configurations
FRA has sponsored research into improving parametric models for more complex track layouts
Parametric models are best used for high-level national or regional modeling to identify potential problem areas
Detailed capacity analysis is done with simulation
Limitations of Parametric Models
• Set of simplifying mathematical assumptions that attempt to duplicate actual train operations
• The simulation should allow a comparison of the current actual train operation with alternative assumptions about:
Changes in the number of trains Changes in the types of trains Changes in the schedules of trains Changes to the physical plant
What is Computer Simulation?
Objectives Determine Minimum Run Time (unobstructed)
on a specific route for specific train characteristics
Develop run time information for use as input to dispatching simulation models
Estimate fuel consumption Uses
Provides key component analysis for schedule preparation
Determines effect of line speed changes on run time
Determines effect of train consist changes Locomotive (tractive effort characteristics, etc.) Cars (weight/braking characteristics, etc.)
Train Performance Calculator (TPC)The Physics of Railroading
Inputs Track profiles: Grades and curves Speed limits Typical train and locomotive consists
Outputs Unhindered speeds and times Fuel consumption
Train Performance Calculator
Speed Limit
Actual Speed
Train Performance CalculatorGraphical Output
TPC’s only provide the expected operational characteristics for a free-running train without regard to: Meets and passes with other trains Capacity for schedule adherence
Train priorities Train density and characteristic mix Physical plant Main track work
Why do we use Train Dispatching Simulation Tools?
Main objective: a mathematical approximation of operational results for a given set of variables based on a reasonable dispatching algorithm
Logic attempts to realistically replicate decisions by a “good” dispatcher (not an optimal one)
Other objectives: Reliable, repeatable results Ease of use
Minimize or automate input data User-friendly input procedures Comprehensive user interface Externally usable results
Train Dispatching Simulation Software
Dispatcher logic Follows rules - preferences - dynamic
priorities Might lower the priority for high priority train
running late Might raise the priority for low priority train if
crew close to exceeding hours Variable - limited outlook
Anti-lock-up logic All moves tested before implementation When move is rejected, next most preferable
move is selected
Event-Based Simulation Dispatching Attributes
Across each train’s look-ahead, conflicts are identified
If there are any, all “reasonable” resolution options are identified
Each option for resolution is “costed” using user defined delay costs. These costs dynamical change as the simulation runs.
Penalties are added for less preferred moves such as changing tracks, entering siding, etc.
Options are sorted by “cost” Each option is submitted to the anti-lock-up
logic until one is accepted The necessary moves of that option are
implemented in the simulation
Dispatching Logic
Infrastructure Plant Signals
Traffic Operating characteristics
Which trains are running Train priorities Train size and power
Route (including reverse moves if applicable)
Time of operation Schedule based and non-schedule based
Train Dispatching SimulationTypes of Inputs
Replicate track maintenance Remove track/control point from service
Apply/remove temporary speed restrictions Replicate train work at a location
Train delay(s) Train characteristics
Train connections Replicate passenger operation
Schedule train(s) departure times Specify a route or track
Special Routing Events
Stringline (time-distance plot) User configurable reports TPC profiles Track occupancy charts Animation of simulation
Train Dispatching SimulationTypes of Outputs
Individual train ”logs" Scheduled & unscheduled delays
In total By train type By location
Statistical analyses Distribution of trip times Locomotive-miles Distribution of delays
Used to determine if plant is balanced Operating costs Timetables
Types of Reports
Source: www.berkeleysimulation.com
Animation of Results
Animation of Results – Yard with Industrial Leads
Source: US DOT Rail Capacity Workshop, 2002.
Track Occupancy Chart
Source: www.berkeleysimulation.com
Develop “base case” traffic Track configuration, signals & other physical
attributes Operating plan
Develop alternative scenarios (changes to physical plant or operating plan)
Compare alternative scenario to base, or to other alternative
The Iterative Planning ProcessUsing Computer Modeling
Inject track maintenance and operating failures at critical points, and determine if plant still works. If not, refine plant some more and re-run
First without perturbations Then with perturbations
Select alternatives that meet operating objectives If none, refine alternatives & re-run Balanced plant is achieved when delays are
evenly distributed
The Iterative Planning ProcessUsing Computer Modeling - 2
Data and time intensive Must validate to actual Yard operations are modeled
separately (hump operations, intermodal lifts, etc.)
Resource constraints (crews, locomotives, etc..) are largely ignored
Models do not look beyond study area to the rest of the network
Even detailed simulation requires simplifying assumptions
Limitations of Simulation Models
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