O’Hare Modernization Program Surface Transportation Travel Projections: Overview and Methodology - PowerPoint PPT Presentation

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O’Hare Modernization Program Surface Transportation Travel Projections: Overview and Methodology

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  1. November 5, 2002 O’Hare Modernization ProgramSurface TransportationTravel Projections:Overview and Methodology PRELIMINARY DRAFT (11/5/02) (for discussion purposes only)

  2. Outline of Presentation • Introduction • Kimley-Horn Scope • Agency Coordination to Date • Base Year Data and Assumptions • Travel Projection Methodology • Travel Projection Output

  3. Purpose of Presentation • Explain travel projection procedures and inputs • Provide an opportunity for questions and feedback

  4. Kimley-Horn Scope of Services • Comprehensive Surface Transportation Inventory • Traffic Counts • Parking Counts • Curbfront Operations • Passenger Surveys

  5. Kimley-Horn Scope of Services • Develop Travel Demand Model • Expanded Study Area • Evaluate Future Year Alternatives • Assist With Agency Coordination • Develop Roadway Concepts

  6. Agency Coordination to Date • Illinois Department of Transportation • West O’Hare Bypass and Elgin O’Hare Expressway • Chicago Transit Authority • Blue Line Extension • Chicago Area Transportation Study • Travel Model Methodology

  7. Travel Projection Objectives • Evaluate OMP surface transportation alternatives • Evaluate traffic operations impacts of surface transportation system • Provide surface transportation input to air quality models

  8. Base Year Data and Assumptions

  9. Existing Conditions • Base year data • Collected by a variety of sources • Review of existing data • Collection of new data in a post 9/11 environment • Pull data from other reports • Other reports and surveys • CATS data

  10. Surface Transportation Travel Projection Study Area

  11. 2002 OMP Surveys • Surface transportation existing base conditions • Post 9/11 operation conditions

  12. Other O’Hare Transportation Surveys • 1997, 1992, and 1989 surveys • Trends, reasonability checks

  13. CATS Travel Demand Information • CATS provided database • KHA created macros • Used for non-Airport trips • CATS data used for latest transportation plan update and conformity analysis

  14. Air Passenger Activity Data • Base and future year air passenger activity data produced by Ricondo and Associates • Hourly origin-destination passenger activity by terminal; domestic/international split

  15. Employee Data • Directions of approach, shifts, parking locations • Occupancy of larger employee lots

  16. Employee Characteristics • Directional distributions based on zip codes • Airport destinations based on parking areas • Time of day at Airport based on shift schedules

  17. Pre/Post 9/11 Pattern Changes • Curbfront dwell times • Parking distribution • Traffic volumes

  18. Comparison of Traffic Volumes Around the Terminal Area

  19. Level of Service for Selected Intersections in 2002 2002 LOS D or Better 2002 LOS E or Worse

  20. Level of Service for Selected Intersections in 2002 Peak Hour: 4:30 PM to 5:30 PM

  21. Based on Air Passenger Activity 2002 Terminal 1………..36% Terminal 2………..18% Terminal 3………..37% Terminal 5………..9% Percentage of Origin-Destination Passengers by Terminal

  22. Auto - Direct to Parking………………. 19.6% Auto -Drop off at Curb………………… 31.0% Auto - “Kiss 'n' fly” curb at Lot E…… 0.9% Limo………………………………………. 8.1% Rail (CTA/Metra)………………………… 5.9% Taxi……………………………………….. 13.4% Hotel/Motel Shuttle…………………….. 4.5% On-Airport Rental Car Shuttle……….. 9.9% Off-Airport Rental Car Shuttle……….. 1.1% Off-Airport Parking Shuttle…………... 0.6% Bus (all classes)………………………… 2.7% Airport Express (Continental)……….. 2.2% Passenger Mode of Arrival

  23. Passengers I-90 E……………….. 36.8% I-90 W………………. 16.3% I-294 S……………… 25.4% I-294 N……………... 16.7% Mannheim Rd N….. 2.2% Mannheim Rd S….. 1.8% Direction of Travel to Airport from Survey

  24. % Occupancy of Public Parking Lots

  25. Parking Characteristics • Public parking • Typical stay in Core and Lot D = 1 day • Typical stay in Lot E and Lot G = 3 days • Visitor parking • Hourly parkers are 17% of the Core area parkers • Typical stay = 1.5 hours

  26. Taxi/Limo Characteristics • After dropping off a fare, approximately 80% go to the CVHA and 20% go out of the network • Directional distribution

  27. Hotel/Motel Shuttle Characteristics • Drop off - Upper levels of the Core and Terminal 5 • Pick up - Bus Shuttle Center and Lower Level of Terminal 5 • Directional Distribution • 65% from I-190 • 25% from Mannheim North • 10% from Mannheim South

  28. CTA Characteristics • CTA • Approximately 9,000 boardings a day • Approximately two-thirds are employees

  29. Bus Characteristics • Drop off and pick up at Bus Shuttle Center and Terminal 5 Lower Level • Directional Distribution • 98% from I-190 • 1% from Mannheim North • 1% from Mannheim South

  30. Methodology

  31. Traffic Projections Process Base Year Counts and Surveys for Study Area CATS Travel Data Flight Schedules CATS data for Background Trips (EMME/2 Software) Airport Trips Model (ALPS) Employee Data Background (Non-Airport) Trips TRAFFIX Travel Estimate/ Projection Base Year/Design Year Traffic Data Evaluation and Presentation of Results

  32. Application of Methodology • Forecasting and analysis of landside trips built on several models and forecasts • Air travel demand • O/D air passenger access behavior • Commercial vehicle (buses, taxis, etc.) • Parking behavior • Curbfront behavior • Rental car behavior • Visitor behavior • Employee behavior • Non-airport trips

  33. Application of Methodology (cont.) • Four software platforms used to codify process • ALPS • CATS/EMME2 • TRAFFIX • SYNCHRO

  34. Application of Methodology (cont.) • Overall approach is the same • Why four platforms? • Use applications to support methodology • Focus applications to their scale and analytical strengths • Provide means of reporting at all levels of detail required for this study • Provide means of adopting methodology for additional analysis

  35. Why This Approach? (cont.) • Overall • Allows incorporation of airport-focused and regionally-focused trips into consistent network down to operations levels • Data remains consistent across platforms • Minimal effort to post-process system-wide measures of effectiveness • Provides consistent inputs for the off-model curbfront and parking analysis • Can be adopted for use in other operations and analysis packages, such as CORSIM, as required

  36. Airport-Related Trips

  37. Airport-Related Trips • Air passenger activity based • Modeled using ALPS (Airside Landside Planning Software) • ALPS models trips to and from the airport on the roadways • Entering and exiting person trips from the Airport for all 24 hours were generated

  38. Base Year ALPS Network Boundaries

  39. Flight-Related Trips • Passenger trips are modeled using: • Passenger flight characteristic curves • Visitor trips (meeter-greeters and well wishers) are modeled using: • Visitor percentages based on the origin-destination passengers • Visitor characteristic curves

  40. Early Arrival Curve • Determine when passengers enter the terminal area in relation to their scheduled flight departure time • From the 2002 OMP Surface Transportation Passenger Survey * These data points are based on the midpoints of the time period they represent

  41. Late Departure Curve • Determine when passengers start to exit the terminal after their flight has arrived • From studies at similar airports, the same curve was used in WGP analysis

  42. Visitor Characteristics • Of Origin-Destination Passengers • 5% are domestic well wishers • 10% are domestic meeter greeters • 10% are international well wishers • 10% are international meeter greeters

  43. Visitor Curves

  44. Visitor Characteristics • Parking Location • 90% domestic visitors park in the EPS, 8% in Lot C, and 2% in Lot B • 95% of international visitors park in Lot D, 5% in the EPS • 5% of visitors drop off passengers before parking • 1% of visitors pick up passengers after exiting parking

  45. ALPS Vehicle Routes • Hand-coded rather than minimum impedance path • Based on directional distribution • Allows recirculation routes • Routes developed for each travel class/mode

  46. Background(Non-Airport Generated) Trips

  47. Trip Generation, Distribution, and Assignment • Background trips generation/distribution using CATS data • Assignment using TRAFFIX model

  48. Application of the CATS Data • Regional travel model • Create boundary around Airport and remove Airport trips • Established methodology • Determine all trips entering or exiting the study area

  49. Background Trip Procedure Partial Background Trip Matrix

  50. Background Trip Procedure (cont.) • Reviewed 2002 CATS model network • Macros were developed to create: • Time-of-day trip tables • Volume delay functions • Assignment routines