1. 1 Long Range Decision Support System - Design Overview - Institutional Knowledge -Issues unresolved Transport Modeling & Software Development 8 th April 2005 Bharat Salhotra Bharat.salhotra@gmail.com

2. 2 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

3. 3 ANSWERING QUESTIONS IN SEQUENTIAL ORDER IS NOT POSSIBLE DUE TO INTERDEPENDENCIES Interdependent variables of network planning Solution: Develop a DSS with three objectives Model interdependencies at micro levels Generalize interdependencies at Macro Level Assess bundle of Investments based on environment 1 2 3 Traffic /Train Mix Train speed differential Capacity of network Introduction of high-speed trains # of yards/ traction change points Network quality Throughput Complexity in Route structure of freight trains Quality Standards for freight traffic/ passenger traffic Capacity increase of marshalling yards # of stops for passenger trains Deterioration of infrastructure Types of Locos/Wagons Market Demand

4. 4 System Optimization vs. Subsystem Optimization

5. 5 Need for LRDSS Provides important desktop information for planners / decision-makers for: –Investment planning/project screening –Market analysis –Financial impact analysis –Funds Requirement

6. 6 LRDSS: Salient Features Integrative Character : –Interdisciplinary –System wide Analysis –Simultaneous /Sequential Analysis Improvements in Technology/ Operating Policy Commodity Flows Routing Plans

7. 7 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

8. 8 LRDSS : Salient Features Strong Decision Support –Strategic Level Tool –“What-if” Analysis (“With/Without”) –“Sensitivity” Analysis –Information based & Data Driven. –Iterative Evaluation –Modular Design

9. 9 LRDSS for Decision Support PLANNING PROCESS Models to support different time horizons Five Year Plans Strategic Level Annual Plans Integrated LRDSS... BCAM DAF, FPM FPM

10. 10 Broad Structure of Model Traffic Assignment Market Analysis Traffic Forecasting Cost Benefit Analysis Supply Analysis Demand Analysis Financial Forecasting Facility Performance

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12. 12 Facility Performance LINKS TERMINALS SUPPLY SIDE + NETWORK

13. 13 Rail Performance Model Determine ability to carry traffic LINK TYPE TRAIN TYPE Cost Curve SPEED & COST CALCULATOR TRAFFIC OPERATING RULE Speed Curve

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15. 15 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

16. 16 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

17. 17 Train Performance Calculator TRAIN PERFORMANCE CALCULATOR TRACK PROFILE TRAIN PROFILE LOCOMOTIVE COACH WAGON RUN TIMES Fuel Consumption Speed Profile

18. 18 TPC Output (Track Profile)

19. 19 Train Performance

20. 20 TRAIN DESPATCH SIMULATOR Scenario Train Types File Station File Run Times File Schedule File Special Events File Track File Set of Trains Parameter File Simulation results

21. 21 Train Dispatch Simulator Simulates actual train operations –Dispatches trains to resolve conflicts –Allocates resources dynamically –Non priority based, route seeking dispatch –Non Optimizing algorithm

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23. 23 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

24. 24 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

25. 25 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

26. 26 CONVERSION INTO COST FUNCTION

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28. 28 Market Analysis and Traffic Forecast DEMAND SIDE MODELING

29. 29 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

30. 30 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

31. 31 Market Share Analysis

32. 32 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

33. 33 Traffic Analysis Zones

34. 34 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.

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36. 36 Traffic Assignment Module DEMAND + SUPPLY MODELING

37. 37 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.

38. 38 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

39. 39 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.

40. 40 Attributes of a Section

41. 41 Methodology for Assignment Base Year: Assignment on Preferred Paths Future Years: –Assignment on both Preferred & Shortest Paths –Assignment with committed works Sequential/Simultaneous

42. 42 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.

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45. 45 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

46. 46 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

47. 47 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

48. 48 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. Sub Modules of TAM

49. 49 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

50. 50 Path Editor to display/define routes

51. 51 Path Editor

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53. 53 Cost Benefit Analysis & Investment Planning

54. 54 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.

55. 55 Phase III Strengthen existing model with –Terminal Analysis –Multi-Modal Traffic Analysis –Benchmarking Operations Improve Information availability on desktop of decision-makers Interconnect

56. 56 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

57. 57 Stylized Diagrams Terminal representation using six standard features –Entry Exit Points –Support Yards –Customer Sidings –Platforms –Intersections –Connections

58. 58 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 TimeTIME “Waiting for Train Engine” TimeTIME Number of TracksCAPACITY Number of examination gangs RESOURCE

59. 59 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

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61. 61TKD

62. 62 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

63. 63 Model Outputs Train delays (process times and non- process 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

64. 64 Traffic Growth Rate by Commodity & OD Freight Flows in Corridor Mode Split Model Target Year Total Flows Qualitative Preference data Target Year Rail Container Flows Operational Statistics Infrastructure Requirements Cost Benefit Analysis Multimodal Corridor Analysis Model

65. 65 Mode Split Model Parameters –Price –Transit Time –Service Quality Index (Reliability, Availability, Frequency, Loss & Damage) –Product Suitability Form –Cobb Douglas

66. 66 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

67. 67 Resource Requirements Terminal Resources –Land –Rail Lines –Equipment (stackers, trailers, gantry cranes) –Gates –C&W Rolling Stock

68. 68 Scenario Analysis Assess different Investment alternatives –Do minimum (only soft changes) –De-Bottlenecking –Full Fledged Corridor Assess Pricing Strategies Assess Impact of Service Levels

69. 69 Interconnect-LRDSS & Other IR Databases MIS (annual summaries e.g. Traffic) FOIS (annual summaries e.g. op. stats) Planning Data (Project Data) Engineering Data (Improvements and Unit Costs) Railway Board/ LRDSS Databases (Scenario Data) Financial Data (e.g. budgets) Zonal Railway Data (Line and Section Data)

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73. 73 KEY AREAS FOR COLLABORATION LINE CAPACITY SIMULATOR TRAFFIC ASSIGNMENT MODEL –PATH GENERATOR –OPTIMIZATION ALGORITHM PASSENGER TERMINAL DESIGN

74. 74 THANK YOU