Support System- Design Overview - Institutional Knowledge-I.ppt

1、1,Long Range Decision Support System - Design Overview - Institutional Knowledge -Issues unresolved,Transport Modeling & Software Development 8th April 2005Bharat Salhotra B,2,Transportation & IR,Key Facets Network Industry System wide view is critical Diversity of Traffic / Operations Capacity Impl

2、ications Dynamic Need for databases to be updated Large # of Interrelated variables,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 Ge

3、neralize 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 t

4、rains,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,System Optimization vs. Subsystem Optimization,5,Need for LRDSS,Provides important desktop infor

5、mation for planners / decision-makers for: Investment planning/project screening Market analysis Financial impact analysis Funds Requirement,6,LRDSS: Salient Features,Integrative Character: Interdisciplinary System wide Analysis Simultaneous /Sequential Analysis Improvements in Technology/ Operating

6、 Policy Commodity Flows Routing Plans,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 req

7、uired by key year,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,LRDSS for Decision Support,PLANNING PROCESS,Models to support different time hor

8、izons,Five Year Plans,StrategicLevel,Annual Plans,Integrated LRDSS,BCAM DAF, FPM,FPM,10,Broad Structure of Model,Traffic Assignment,Market Analysis,Traffic Forecasting,Cost Benefit Analysis,Supply Analysis,Demand Analysis,Financial Forecasting,Facility Performance,GIS,11,12,Facility Performance,LINK

9、S,TERMINALS,SUPPLY SIDE,+,NETWORK,13,Rail Performance Model Determine ability to carry traffic,LINK TYPE,TRAIN TYPE,Cost Curve,SPEED & COST CALCULATOR,TRAFFIC,OPERATINGRULE,Speed Curve,14,15,RAILS Overview,Two Modules : Train Performance Calculator (TPC) Train Dispatch Simulator (TDS)TPC : Single Tr

10、ain running on a section Level of interference = 0 Running determined by track profile and train,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 Dav

11、is Co-efficient Train treated as a series of points,17,Train Performance Calculator,TRAIN PERFORMANCE CALCULATOR,TRACK PROFILE,TRAIN PROFILE,LOCOMOTIVE,COACH,WAGON,RUN TIMES Fuel Consumption Speed Profile,18,TPC Output (Track Profile),19,Train Performance,20,TRAIN DESPATCH SIMULATOR,Scenario,Train T

12、ypes File,Station File,Run Times File,Schedule File,Special Events File,Track File,Set of Trains,Parameter File,Simulation results,21,Train Dispatch Simulator,Simulates actual train operations Dispatches trains to resolve conflicts Allocates resources dynamically Non priority based, route seeking di

13、spatch Non Optimizing algorithm,22,23,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 Overtak

14、es,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,Output from FPM,S

15、imulation 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,CONVERSION INTO COST FUNCTION,27,Congestion Graphs,28,Market Analysis and Tr

16、affic Forecast,DEMAND SIDE MODELING,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

17、Center. Lead length Long lead traffic favors Rail Business Service Requirements JIT ,Reduced Order Quantity, Reliability Single to multiple suppliers,30,Key Factors to Success,Core Factors Reliability, Availability, Price and Transit Time Desirable Factors Connectivity, Product Suitability, Loss/Dam

18、age, Customer Information Adaptability, Customer Friendliness, Negotiability, Access to Decision Makers Ease of Payment & Claim Processing Time,31,Market Share Analysis,32,Traffic Forecasting Module,Objectives: Determine Production & Consumption Functions by Commodity for TAZs Forecast Origin Destin

19、ation Flows by Commodity Key Years (2006-07 & 2011-12) Identify high growth areas loading unloading terminals Origin Destination Routes,33,Traffic Analysis Zones,34,Methodology,Different models used for different commodities GAMS Linear Programming Model Assigns traffic by minimizing transportation

20、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.,35,36,Traffic Assignment Module,DEMAND + SUPPLY MODELING,37,Traffic Assignment Module,Operation Research based Freight Network Equilibrium Model

21、. Objective function: Minimize total cost of carrying trafficAssign OD flows on paths using least impedance. (= 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,Traffic Assignment Module,Basi

22、c Inputs to the Model: I : Demand Side Existing and Future Traffic Commodity wise flows between pairs of points Traffic for 200102, 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,Network Da

23、tabases,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,Attributes of a Section,41,Methodology for As

24、signment,Base Year: Assignment on Preferred Paths Future Years: Assignment on both Preferred & Shortest Paths Assignment with committed works Sequential/Simultaneous,42,Analysis of TAM Results,Outputs Commodity wise traffic on each link. ODs that use a particular link. Lowest Cost Route path between

25、 pairs of points. Utilization of each Traction Point. These reports can be compared for alternative scenarios.,43,44,45,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 o

26、f data, by key year,46,TAM-Conceptual design,Replicate Shippers Behavior What commodity from where to where? Replicate Carriers Behavior What commodity, what route, what train type Non Linear Programming Optimization Solver MINOS,47,Sub Modules of TAM,Network Processor create a logical multi modal n

27、etwork access /egress links, transshipment links, traction change points, nodes Output consists of Forward star data structure to be used as input to k path algorithmPath generator (K-Short) creates shortest paths between two O-D Pairs Input to Solver,48,Carrier Input Processor Generates MINOS input

28、 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 TA

29、M,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,Path Editor to display

30、/define routes,51,Path Editor,52,53,Cost Benefit Analysis & Investment Planning,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 resour

31、ces needs to be matched with priority of projects.,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,Terminal Analysis Module,Objective Develop a better und

32、erstanding 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,Stylized Diagrams,Terminal representation using six stan

33、dard features Entry Exit Points Support Yards Customer Sidings Platforms Intersections Connections,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

34、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,59,Trains Database,Brain Train Trains have an ID, type and commodity associated Arrival Day and Time Predefined Route assigned

35、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,60,61,TKD,62,Simulation,Terminal Process model simulates Movement of trains on routes through Terminal Delays of trains due to limited resources Delays

36、 due to crew change Loading/unloading at customer siding Interaction with passenger trains sharing facilities with goods trains Disruptions at facilities User Interface,63,Model Outputs,Train delays (process times and non-process times) by train Assignment of delay to facility resource Activity summ

37、ary of utilization of all of the facilities and resources, including time spent in examination, loading/unloading track usage Feed investment analysis,64,Traffic Growth Rate by Commodity & OD,Freight Flows in Corridor,Mode Split Model,Target Year Total Flows,Qualitative Preference data,Target Year R

38、ail Container Flows,Operational Statistics,Infrastructure Requirements,Cost Benefit Analysis,Multimodal Corridor Analysis Model,65,Mode Split Model,Parameters Price Transit Time Service Quality Index (Reliability, Availability, Frequency, Loss & Damage) Product Suitability Form Cobb Douglas,66,Multi

39、modal 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,Resource Requirements,Terminal Resources Land Rail Lines Equipment (st

40、ackers, trailers, gantry cranes) Gates C&W Rolling Stock,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,Interconnect-LRDSS & Other IR Databases,MIS (annua

41、l 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),70,71,72,73,KEY AREAS FOR COLLABORATION,LINE CAPACITY SIMULATOR TRAFFIC ASSIGNMENT MODEL PATH GENERATOR OPTIMIZATION ALGORITHMPASSENGER TERMINAL DESIGN,74,THANK YOU,