Long Range Decision
Support System
- Design Overview
- Institutional Knowledge
-Issues unresolved
Transport Modeling & Software Development
8th April 2005
Bharat Salhotra
Bharat.salhotra@gmail.com
1
Transportation & IR
• Key Facets
– Network Industry
• System wide view is critical
– Diversity of Traffic / Operations
• Capacity Implications
– Dynamic
• Need for databases to be updated
– Large # of Interrelated variables
2
ANSWERING QUESTIONS IN SEQUENTIAL ORDER IS
NOT POSSIBLE DUE TO INTERDEPENDENCIES
Interdependent variables of network planning
•Traffic /Train Mix
•Train speed differential
•Market Demand
•Types of
Locos/Wagons
•Capacity increase of
marshalling yards
•# of stops for
passenger trains
•Capacity of
network
•Deterioration of
infrastructure
•Network
quality
•Introduction
of high-speed
trains
•# of yards/ traction
change points
•Quality Standards for
freight traffic/ passenger
traffic
Solution:
Develop a DSS with three
objectives
1 Model interdependencies
at micro levels
2 Generalize
interdependencies at
Macro Level
3 Assess bundle of
Investments based on
environment
•Throughput
•Complexity in Route
structure of freight
trains
3
System Optimization
vs.
Subsystem Optimization
4
Need for LRDSS
• Provides important desktop information for
planners / decision-makers for:
– Investment planning/project screening
– Market analysis
– Financial impact analysis
– Funds Requirement
5
LRDSS: Salient Features
• Integrative Character:
– Interdisciplinary
– System wide Analysis
– Simultaneous /Sequential Analysis
• Improvements in Technology/ Operating Policy
• Commodity Flows
• Routing Plans
6
LRDSS: Salient Features
• Customized GIS Interface
– Integration of different data by location
– Evaluate alternative routes
– Exhibit pattern of traffic flows
• Strong Decision Support
– Prioritize Investments
– Position Services to optimize market share.
– Analyze Funds required by key year
7
LRDSS : Salient Features
• Strong Decision Support
– Strategic Level Tool
– “What-if” Analysis (“With/Without”)
– “Sensitivity” Analysis
– Information based & Data Driven.
– Iterative Evaluation
– Modular Design
8
LRDSS for Decision Support
PLANNING
PROCESS
Models to
support
different time
horizons
Strategic
Level
Integrated
LRDSS
Five Year
Plans
Annual
Plans
...
BCAM
DAF, FPM
FPM
9
Broad Structure of Model
Supply Analysis
Facility
Performance
Market Analysis
Traffic
Forecasting
Traffic
Assignment
Cost Benefit
Analysis
Financial
Forecasting
10
11
SUPPLY SIDE
Facility Performance
NETWORK
LINKS
+
TERMINALS
12
Rail Performance Model
Determine ability to carry traffic
LINK TYPE
TRAIN TYPE
OPERATING
RULE
TRAFFIC
SPEED & COST
CALCULATOR
Cost
Curve
Speed
Curve
13
14
RAILS Overview
• Two Modules :
– Train Performance Calculator (TPC)
– Train Dispatch Simulator (TDS)
• TPC :
– Single Train running on a section
– Level of interference = 0
– Running determined by track profile and train
15
Train Performance Calculator
– Uses empirical formulae
– Follows TE/Speed Curve, braking curves of
locomotives
– Rolling resistance of coaches
– Train resistance, grade resistance, air
resistance based on Davis Co-efficient
– Train treated as a series of points
16
Train Performance Calculator
TRACK PROFILE
LOCOMOTIVE
COACH
TRAIN PROFILE
TRAIN
PERFORMANCE
CALCULATOR
WAGON
RUN TIMES
Fuel Consumption
Speed Profile
17
TPC Output (Track Profile)
18
Train Performance
19
TRAIN DESPATCH SIMULATOR
Set of Trains
Station File
Track File
Run Times File
Train Types File
Parameter File
Schedule File
Special Events File
Scenario
Simulation results
20
Train Dispatch Simulator
• Simulates actual train operations
– Dispatches trains to resolve conflicts
– Allocates resources dynamically
– Non priority based, route seeking dispatch
– Non Optimizing algorithm
21
22
Train Dispatch Simulator
• Event based
• Useful for analyzing alternative line
configurations
– location of LOOPS, CROSSINGS
• Establishment of Train schedules
– departure/arrival/halts of trains
• Examination of Capacity Issues
– Identification of Conflicts
– Meets and Overtakes
23
Simulation
• Calibration:
• Within 5% of actual situation on field.
• Congestion & Capacity Modeling
– Traffic increased incrementally to obtain
• Congestion Graphs
• Estimated Line Capacity
• Scenario Analysis:
• Impact of failures
• Horsepower to Trailing Load ratios
• Passenger Train Halts
24
Output from FPM
• Simulation results of 17 links
– transit times & congestion curves by train
type & Link Type
– impact of failures (track, signaling, wagons)
– capacity based on simulation (not charting)
• Cost Data
– working expenses by train type
25
Baroda Surat Section
Cost for different types of Passenger Trains
Long Term Line Haul Variable Cost ( Rs.) per
1000 GTKM
330
CONVERSION
INTO COST
FUNCTION
310
290
270
Shatabdi
250
Slow
Passenger
230
Rajdhani
210
Mail
Express
190
170
1
35
44
53
Trains per Day
61
80
26
27
DEMAND SIDE MODELING
Market Analysis
and
Traffic Forecast
28
Mode Share: Key Determinants
(from SURVEY)
• Volumes
• High volumes (>1 lakh TPA) = high rail share if few destinations
• Channel Structure
• Flat distribution channels, bulk buyers favor rail movement.
• Flow rate
• Raw materials
Production line
• Finished goods
Consumption Center.
• Lead length
• Long lead traffic favors Rail
• Business Service Requirements
• JIT ,Reduced Order Quantity, Reliability
• Single to multiple suppliers
29
Key Factors to Success
• Core Factors
– Reliability, Availability, Price and Transit Time
• Desirable Factors
– Connectivity, Product Suitability,
Loss/Damage, Customer Information
– Adaptability, Customer Friendliness,
Negotiability, Access to Decision Makers
– Ease of Payment & Claim Processing Time
30
Market Share Analysis
31
Traffic Forecasting Module
• Objectives:
– Determine Production & Consumption
Functions by Commodity for TAZs
– Forecast Origin Destination Flows by
• Commodity
• Key Years (2006-07 & 2011-12)
– Identify high growth areas
• loading
• unloading terminals
• Origin Destination Routes
32
Traffic Analysis Zones
33
Methodology
• Different models used for different commodities
– GAMS Linear Programming Model
• Assigns traffic by minimizing transportation cost.
– “Furness” Trip Generation Model
• Generates OD flows based on movement pattern in the
base year
– Factoring
• OD flows are projected based on growth rates.
34
# OF TAZs/TERMINALS REPRESENTED BY TYPE
TAZ/TERMINAL TYPE
Fertilizer Plants
Thermal Power Plants
Refineries/Specialized Oil Sidings
Iron ore/Limestone loading points for steel plants
Coal Depots/Sidings
Cement Plants
Steel Plants
Foodgrain Loading Points
Iron Ore Loading points for Export
Non Specialized Terminals/Goodsheds
Total
NUMBER
79
41
357
57
113
134
42
284
15
433
1555
Note: Total number of terminals exceed TAZs (746) due to double
counting w here there is more than one terminal in a TAZ.
Source: LRDSS commodity forecast Analysis
35
DEMAND + SUPPLY
MODELING
Traffic Assignment Module
36
Traffic Assignment Module
• Operation Research based Freight Network
Equilibrium Model.
• Objective function: Minimize total cost of
carrying traffic
– Assign OD flows on paths using least impedance.
• (= Σ congestion cost on links/nodes )
– Each path consists of series of links and nodes.
– Path Cost = aggregated cost of traffic movement
over each link and node.
37
Traffic Assignment Module
• Basic Inputs to the Model:
– I : Demand Side
• Existing and Future Traffic
– Commodity wise flows between pairs of points
– Traffic for 2001’02, 2006-’07, 2011-’12
– II: Supply Side
• Existing and Future Network
– Sections as well as their Cost Characteristics
– Stations as well as their Cost Characteristics
38
Network Databases
• Two distinct database representing IR
network
– Railline Database
– Railnode database
• 1796 links & 1531 nodes for base case
• Nodes Database contains information
like TAZ, Transshipment point, Rail
terminal/yard, Traction change point,
reversal etc.
39
Attributes of a Section
40
Methodology for Assignment
• Base Year: Assignment on Preferred Paths
• Future Years:
– Assignment on both Preferred & Shortest
Paths
– Assignment with committed works
• Sequential/Simultaneous
41
Analysis of TAM Results
• Outputs
– Commodity wise traffic on each link.
– ODs that use a particular link.
– Lowest Cost Route path between pairs of
points.
– Utilization of each Traction Point.
• These reports can be compared for
alternative scenarios.
42
43
44
Variables Handled
• Data Size & Spread
– 900 * 900 * 10 OD matrix elements
– 15,000 Rail Paths
• Each Path has average of 50 links
– Each link has commodity wise congestion function
– 3 sets of data, by key year
45
TAM-Conceptual design
• Replicate Shipper’s Behavior
– What commodity from where to where?
• Replicate Carriers Behavior
– What commodity, what route, what train
type
• Non Linear Programming Optimization
Solver
– MINOS
46
Sub Modules of TAM
• Network Processor
– create a logical multi modal network
• access /egress links,
• transshipment links, traction change points, nodes
– Output consists of Forward star data structure
to be used as input to k path algorithm
• Path generator (K-Short)
– creates shortest paths between two O-D Pairs
– Input to Solver
47
Sub Modules of TAM
• Carrier Input Processor
– Generates MINOS input file
– Represents full specifications of carrier model
with unit costs specified as a ‘real valued’
function of path flows (non linear)
• Post processor
– Interprets MINOS solution file.
– Interfaces with GIS
– Query based GIS interface allows graphical
display of bottleneck links, flows etc.
48
GIS & LRDSS
• Avenue based Path Editor used to check
paths generated by “kshort” algorithm
• transshipments, traction change, reversals
• create new paths via certain given stations
• User Interface to facilitate Data Analysis
through
– Data browser
– Query Builder
– Chart generator
49
Path Editor to display/define routes
50
Path Editor
51
52
Cost Benefit Analysis &
Investment Planning
53
Utility of LRDSS
• LRDSS is a powerful tool for enabling a
pre-feasibility analysis.
• Results are only indicative
– micro modeling to gauge impact of specific
investment/policy initiatives.
– availability of financial resources needs to be
matched with priority of projects.
54
Phase III
• Strengthen existing model with
– Terminal Analysis
– Multi-Modal Traffic Analysis
– Benchmarking Operations
• Improve Information availability on desktop
of decision-makers
• Interconnect
55
Terminal Analysis Module
– Objective
• Develop a better understanding of terminal
operations
• Use Process modeling to estimate detentions
and impact of such detentions
• Evaluate investment options to minimize
detentions and improve efficiency at terminals
– Simulation Model instead of a
Numerical Model
56
Stylized Diagrams
• Terminal representation using six
standard features
– Entry Exit Points
– Support Yards
– Customer Sidings
– Platforms
– Intersections
– Connections
57
Facilities Database
• Associated with a Facility are 3 types of fields
• Time related fields
• Capacity related fields
• Resource related fields
• E.g. a support yard would have fields specifying
•
•
•
•
•
Examination Time (Average, Minimum, Maximum)
Shunting Engine Attach/Detach Time TIME
“Waiting for Train Engine” Time
TIME
Number of Tracks
CAPACITY
Number of examination gangs
RESOURCE
58
Trains Database
– Brain Train
• Trains have an ID, type and commodity
associated
• Arrival Day and Time
• Predefined Route assigned to the train
– Route Descriptions
•
•
•
•
START at ENTRY POINT
Sequence of FACILITIES a train has to USE
Quantum of RESOURCE & TIME required
END at EXIT point
59
60
Tuglakabad Terminal
TKD
SAIL
ICDCON
COR
R&DCO
NCOR
BALLAST
MINERAL
DIESEL
TATA
DCD
DAIRY
ELECTRIC
DNRCV
DNC
LAS
S
UPDEPT
UPC
LAS
S
DNDEPT
UPRCV
NXCR
NXDA
PFINT
BTPPSU
PP
61
BTPP
Simulation
• Terminal Process model simulates
–
–
–
–
–
Movement of trains on routes through Terminal
Delays of trains due to limited resources
Delays due to crew change
Loading/unloading at customer siding
Interaction with passenger trains sharing
facilities with goods trains
– Disruptions at facilities
• User Interface
62
Model Outputs
• Train delays (process times and nonprocess times) by train
• Assignment of delay to
– facility
– resource
• Activity summary of utilization of all of the
facilities and resources, including
• time spent in examination, loading/unloading
• track usage
• Feed investment analysis
63
Multimodal Corridor Analysis Model
Freight Flows
in Corridor
Traffic Growth Rate
by Commodity & OD
Qualitative
Preference data
Target Year
Total Flows
Mode Split Model
Target Year Rail
Container Flows
Cost Benefit
Analysis
Infrastructure
Requirements
Operational
Statistics
64
Mode Split Model
• Parameters
– Price
– Transit Time
– Service Quality Index (Reliability, Availability,
Frequency, Loss & Damage)
– Product Suitability
• Form
– Cobb Douglas
65
Multimodal Corridor Analysis Model
– Estimate Total Traffic
– Estimate Operational Requirements
(Throughput, Trains, Lifts etc.)
– Estimate Capital Requirements
(Infrastructure, Rolling Stock,
Equipment)
– Estimate Cash Flows
– Calculate IRR
66
Resource Requirements
• Terminal Resources
–
–
–
–
–
Land
Rail Lines
Equipment (stackers, trailers, gantry cranes)
Gates
C&W
• Rolling Stock
67
Scenario Analysis
• Assess different Investment alternatives
– Do minimum (only soft changes)
– De-Bottlenecking
– Full Fledged Corridor
• Assess Pricing Strategies
• Assess Impact of Service Levels
68
Interconnect-LRDSS & Other IR
Databases
Planning Data
(Project Data)
MIS
(annual
summaries
e.g. Traffic)
FOIS
(annual
summaries
e.g. op. stats)
Railway Board/
LRDSS
Databases
(Scenario
Data)
Zonal Railway Data
(Line and Section Data)
Engineering
Data
(Improvements
and Unit Costs)
Financial
Data
(e.g. budgets)
69
Current LRDSS Architecture
LRDSS MDV Clients
LRDSS Client
LRDSS Client
CDROM
Reader
RAILS
Simulation
Module
LRDSS Client
CDROM
Reader
CDROM
Reader
CDROM
Reader
LRDSS LAN
LRDSS Client
CDROM Reader
LRDSS Client
LRDSS
Data
Storage
LRDSS Client
Plotter
CDROM Reader
Scanner
Tape Storage
CDWORM
Writer
Intranet
v
IR Web Page
GIS/Trans E-mail and FTP Site
Component
to be Acquired
Component
to be Deleted
v
LRDSS Site
70
Proposed Short Term LRDSS Architecture
LRDSS MDV Clients
LRDSS Client
LRDSS Client
CDROM Reader
RAILS
Simulation
Module
LRDSS Client
CDROM Reader
CDROM Reader
Railway Board LAN
CDROM Reader
LRDSS Client
LRDSS Client
CDROM Reader
LRDSS MDV Client
LRDSS
Data
Storage
(Oracle)
Other
IR
Databases
(FOIS, TMS,
MIS)
Plotter
Scanner
CDWORM
Writer
Internet
RAILS
Simulation
Module
BCAMlite
Pilot LRDSS Client
at Zonal Railway
CDROM Reader
71
Proposed Medium Term Architecture (1 ½ - 3 years)
LRDSS Unit
RAILS
Simulation
Module
LRDSS Client
LRDSS Client
LRDSS Client
LRDSS MDV Clients LRDSS MDV Clients
with CDROM Readers
Railway Board LAN
More
MDV
Clients
LRDSS MDV Client
LRDSS MDV Client
LRDSS
Data
Storage
(Oracle+
SDE)
LRDSS Client
(Expert User)
Other
IR
Databases
(MIS+
FOIS+
Planning)
RAILS
Simulation
Module
CDWORM
Writer
Other
Zonal
Railways
Databases
(incl. GIS)
Internet
or WAN
IR FTP Site
IR Web Page
Other Zonal
Railways
BCAMlite
GIS/Trans E-mail and FTP Site
LRDSS Clients
at Zonal Railways
RAILS
Simulation
Module
RAILS
Simulation
Module
BCAMlite
72
KEY AREAS FOR
COLLABORATION
• LINE CAPACITY SIMULATOR
• TRAFFIC ASSIGNMENT MODEL
– PATH GENERATOR
– OPTIMIZATION ALGORITHM
• PASSENGER TERMINAL DESIGN
73
THANK YOU
74