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MODULE 4

MODULE 4. Volume III – Guidelines for Applying Microsimulation Modeling Software. VOLUME III. Recommend a project management process applicable to all traffic studies regardless of tool used. Provide overview of Project Management Process using a Microsimulation Example

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MODULE 4

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  1. MODULE 4 Volume III – Guidelines for Applying Microsimulation Modeling Software

  2. VOLUME III • Recommend a project management process applicable to all traffic studies regardless of tool used. • Provide overview of Project Management Process using a Microsimulation Example • Discuss the importance of model calibration • Share examples and experiences

  3. GOALS OF A GOOD MODELING PROCESS • Based on a clearly defined purpose and objective • Develop consensus • Elected officials, transportation officials and citizens • Identify trade-offs - better decision making • Measure performance of alternatives • Do they meet general goals of the project. • Improve design and evaluation time and costs

  4. PROJECT MANAGEMENT PROCESS • Scope Project (Purpose, scope and approach) • Data Collection • Base Model Development • Error Checking • Calibration (Compare model MOE’s to field data) • Alternative Analysis • Final Report and Technical Documentation

  5. Handout

  6. Courtesy of MNDOT

  7. TYPICAL QUESTIONS TO CONSIDER • How should the project scope and physical limits be established? • What are the project objectives? • Which traffic analysis tool is best? • How do I pick a traffic analysis tool? • What are the available resources? • Is sufficient expertise available to develop the model?

  8. SCOPE PROJECT • Identify Project Purpose and Need • High Level Planning • Detailed Design • Operational Improvements

  9. SCOPE PROJECT • Identify Limits of Project • Physical Construction • Operational Area of Influence • Model Limits? • Estimate Data Collection • Available Data Sources • Additional Data Needs • Analysis Year(s) and Time Period(s) • Estimate Level of Effort • Commensurate to Purpose and Need, Investment, and Safety or Failure Risk Figure Courtesy of MNDOT

  10. PROJECT SCOPETHE REVIEWING AGENCY’S POSITION • Have needs and expectations been met? • Have appropriate project tasks and deliverables been identified? • Is there a focus on documentation, including the model development process and calibration process? • Have the needs of the target audience been identified?

  11. QUESTIONS ON PROJECT SCOPE AND MODEL SELECTION • Discuss what was considered in the selection of the microsimulation tool. • Does the scope of work reflect these considerations? • Will the scope of work generate a report that is soundly supported by model results that we can base engineering decisions on? • Any additional questions?

  12. DATA COLLECTION • The Quality of the Data Will Influence Analysis • Use Data Which is Measurable in the Field

  13. DATA COLLECTION • Base Mapping • Institutionally Acceptable • Eases any Referenced to other Activities • Field Review • Identify Hot Spots • Confirm Operations / Timing Sheets • Confirm Geometry / “Ad-hock” Lanes

  14. DATA COLLECTION • Traffic Volumes • Peak Periods • Could be > 1 hr • Saturated Conditions – consider periods before / after peak • Turning Movements • 15 minute intervals, (or available interval) • Data should be no more than 2 years olds • Balance Counts – Influences the Outcome

  15. GEOMETRIC DATA • Lane widths • Speeds • Length of accel/decel lanes • Length of turn bays • Exclusive turn movements Source: Introduction to Corsim Training Course Workshop 3

  16. CONTROL DATA • Actuation/Pre-timed • Phase Operations • Green times • Vehicle extensions Source: Introduction to Corsim Training Course Workshop 5

  17. DATA FOR CALIBRATION • Depending on the Scope of the Project, Two or More of the Following Are Needed : • Mainline Volumes (Every X Feet) • Mainline Speeds (Every X Feet) • Travel Times (Link or Between Occupancy/Density Pairs) • Bottleneck Capacity (Measured, Not Theoretical) • Entrance Ramp Queues • Intersection Queues and Queue Discharge Rates Source: Chapter 9 Model Calibration John Hourdakis, Center for Transportation Studies University of Minnesota

  18. FIELD OBSERVATIONS • Verify Geometry • Has road been widened? • Have lanes been added? • Have movements been restricted? • Verify Signal Phasing/Timing • Do signals operate as documented? • Verify Speed Limits • Posted speeds • Prevailing speeds

  19. DATA COLLECTION • Sources of Data / Studies • Instrumented Systems (TMC / TOC/Temp “RTMS”) • Planning / Permanent Count Stations • Automatic Vehicle Location / Identification Systems • Crash Databases • Manual Counts • Speed Studies • Queue Observations • O-D Studies • Truck/Freight Data

  20. QUESTIONS ON DATA COLLECTION • Is the necessary data available? • Does additional data need to be collected? • What resources are available for data collection in a timely manner? • Have any data assumptions been made and have these assumptions been documented?

  21. BASE MODEL DEVELOPMENT • Use Structured / Consistent Process • Information Management – Establish QA/QC • Potential Volume • Means to Trace Input, Output and Errors • Spreadsheets • Schematics and Images • Text Files • Automated Programs • Node Numbering Convention • Assumptions

  22. BASE MODEL DEVELOPMENT • Each Tool Is Different • Follow Recommendations of Developer • Capitalize on the Capabilities of the Tool • Multiple Parameters Include • Driver Behavior • Vehicle Characteristics • Consider Limitations of the Tool

  23. BASE MODEL DEVELOPMENT • Develop Link-Node Diagram and Lane Schematic Figure Courtesy of MNDOT

  24. Direction of travel Lane 2 Lane 1 1500’ 1500’ BASE MODEL DEVELOPMENT • Placement of Nodes Will Influence Output (Measure of Effectiveness) Please Be Aware

  25. BASE MODEL DEVELOPMENT • Code Network – Inputs Vary by Model • Link – Node Diagram • Traffic Volume • Origin – Destination • Turn Volume / Percentage • Signal Timing / Operational Characteristics Variations During Analysis Period?

  26. BASE MODEL DEVELOPMENT • Code Network – Inputs Vary by Model • Link – Node Diagram • Traffic Volume • Origin – Destination • Turn Volume / Percentage • Signal Timing / Operational Characteristics May Warrant Multiple Time Intervals

  27. BASE MODEL DEVELOPMENT • Develop Input for for Multiple Time Intervals • 15 Min Intervals

  28. BASE MODEL DEVELOPMENT • Run Model with Multiple Time Interval Data • What About Multiple Runs?

  29. BASE MODEL DEVELOPMENT Why Multiple Runs? CORSIM is a stochastic model, which means that random numbers are assigned to driver and vehicle characteristics and to decision making processes. The MOEs that are obtained from a simulation are the result of a specific set of random number seeds. For example, one set of random number seeds may result in three very conservative drivers driving side by side on a three-lane roadway blocking more aggressive drivers behind them. The resulting MOE would reflect a lower average speed then has been observed in the real world. Relying on the MOE generated from a single run of CORSIM may be misleading. To gain a better understanding of network performance the network should be simulated several times using different sets of random number seeds. The resulting distribution of MOEs should then be an accurate representation of the network performance • TSIS Users Guide

  30. BASE MODEL DEVELOPMENT Why Multiple Runs? For serious simulation applications, it is recommended that multiple runs be performed with different random number seeds… The MOEs can then be averaged with some clever spreadsheet manipulations. • SimTraffic Users Guide

  31. QUESTIONS ON BASE MODEL DEVELOPMENT • How does the coding of the network affect the output? • Do we need to make any assumptions or do we need additional data? • Does the existing/base year model reflect the geometric and operational characteristics of those seen in the field? • Are the limits of the simulated network broad enough to capture the effect of the alternatives in question?

  32. ERROR CHECKING • Review Input Data • Check basic network connectivity for consistency • Review Vehicle Characteristics and Performance Data • Review Turning Movements • Review Traffic Signal Operations

  33. ERROR CHECKING • Run Model at Low Volume / Short Time Period • Review Animation to Confirm Geometry • Review Animation to Confirm Operations • Single vehicle • Low volume • Full volume • Review Model Warnings and Errors

  34. YOU HAVE A WORKING MODEL ERROR CHECKING Upon Completion of Base Model Development, Including Assessment with Multiple Runs

  35. QUESTIONS ON ERROR CHECKING • What process should be used to identify and correct errors during base model development? • What type of internal QA/QC process was used to assure all errors were identified?

  36. BREAK

  37. WHY CALIBRATE? • Computers Cannot Magically Replicate Reality! • Simulation Models Are Designed to be General • Driver Behavior and Road Characteristics Depend on • Location i.e. Minnesota vs California • Vehicle Characteristics (Horsepower, Size, etc.) • Weather Conditions (Dry, Wet, Ice, etc.) • Microscopic Simulators Can Adapt and Replicate Almost Any Condition if the Model Parameters Are Properly Adjusted • What is Realistic and What is Not? Source: Chapter 9 Model Calibration John Hourdakis, Center for Transportation Studies University of Minnesota

  38. OBJECTIVE OF CALIBRATION To improve the ability of the model to accurately reproduce local traffic conditions.

  39. COMPARE MODEL TO FIELD DATA • Calibration of Existing Models • Sometimes Referred to as Existing Condition • Base Model is Calibrated When: • Volume, Density and other Operational Observations are Satisfactorily Replicated • Statistical Tests Support Such a Determination

  40. ADJUST PARAMETERS • Calibration Involves: • Modification of Default Values (Parameters) • Consideration of the Sensitivity of Parameters • Reliance on a Sound Base Model • Representative Field Data

  41. ADJUST PARAMETERS • Suggestions • Limit the Modification of Multiple Parameters per Iteration and document changes between runs • Modify Known Global and Link Level Parameters • Consider Impact of Global Parameters on Individual Links • Consider Impact of Link Parameters on Upstream and Downstream Locations • Use Caution When Modifying Unknown Parameters • Carefully Note all Parameter Modifications

  42. ADJUST PARAMETERS • Sample Global Parameters • Vehicle Entry Headway • Fleet Composition • Driver Behavior • Sample Link Parameters • Free Flow Speed • Warning Sign Locations • Mean Start Up Delay

  43. COMPARE MODEL TO FIELD DATA • What is Satisfactory? • A statistical test is suggested • Volumes Match • Density/Occupancy Match • Speeds Match • Field Observations Occur in Model • “False” bottlenecks are cleared • Post Processor • Spreadsheets

  44. CALIBRATION ISSUES • Very Important For Model Accuracy and Robustness • Accuracy Depends on Measurement Granularity • Averages Over Several Days is a Bad Choice • Might Need Additional Information to be Collected in Turbulent Sections (Bottlenecks, Weaving Areas, etc.) • Simulation Objective Affects Calibration • When Adaptive Control Strategies Are Simulated, Stricter Calibration is Needed • Modeling of an Isolated Interchange in Rural Minnesota Will be Restrictive Source: Chapter 9 Model Calibration John Hourdakis, Center for Transportation Studies University of Minnesota

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