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Development of an Advanced Tour-Based Model by the Pikes Peak Area Council of Governments

16 th TRB Planning Applications Conference May 13 – May 18, 2017, Raleigh, NC Session: Path to Enlightenment Tuesday, May 16, 2017, 1:30 pm – 3:00 pm. A Bridge to Activity Based Modeling. Development of an Advanced Tour-Based Model by the Pikes Peak Area Council of Governments.

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Development of an Advanced Tour-Based Model by the Pikes Peak Area Council of Governments

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  1. 16th TRB Planning Applications Conference May 13 – May 18, 2017, Raleigh, NC Session: Path to Enlightenment Tuesday, May 16, 2017, 1:30 pm – 3:00 pm A Bridge to Activity Based Modeling Development of an Advanced Tour-Based Model by the Pikes Peak Area Council of Governments Presented by: Maureen Paz de Araujo – Wilson & CompanyChetan Joshi – PTVMary Lupa – WSPKen Prather – Pikes Peak Area COG (PPACG)

  2. PRESENTATION ORGANIZATION 01 - Why a Tour-Based Model? 02 - Building Support for New Model 03 - Model Development Approach 04 - Tour-Based Model Structure 05 - PPACG Beta Tour-Based Model

  3. 01 – Why A Tour-Based Model? PPACG’s current model lacks detail and fidelity to support planning roles and responsibilities. The current trips-based model: Does not account for trip interdependencies – models only discrete one-way trips Lacks detail needed to evaluate travel behavior of individuals – individuals within households are modeled as groups with homogeneous travel behavior Is relatively insensitive to time-of-day – only eight time-of-day slices are modeled Understates the roleof accessibility – doesn’t capture travel decisions made based on accessibility 3

  4. Planning TO BE Supported By Model: Long Range Planning: evaluation of transportation improvement project scenarios Mobility Planning: testing of land use scenarios and evaluation of their impact on regional mobility Equity Analysis: evaluation of how benefits (or dis-benefits) accrue to different populations Scenario Planning: evaluation of the impacts of accessibility on trip-making characteristics Evaluation of Complex Investment Strategies and Polices: evaluation of investment strategies and policies that vary by time-of-day, e.g. pricing scenarios 4

  5. WHY NOT A FULL ABM? Migration to a full ABM was a “hard sell” and not a “good fit” for PPACG in the near term. This was primarily due to resource constraints. Factors weighing against near term implementation of a full ABM included: Initial Development Cost – typical model costs $1M to $1.4M Technical Issues– run times, greater data requirements, inconsistency of outputs, model accessibility for external users Staffing and Training – modeling staff of one, potential for staff turn-over, staff knowledge base Institutional Issues – aversion to changing the status quo, general aversion to models (distrust/discontent) with results 5

  6. IF Not an ABM, What THEN? • Ahybrid, tour-based modelprovided an incremental step toward ABM functionality that was developed quickly and cost-effectively, as extension of the current model, by: • Leveraging resources – funding and agency staff resources • Using a blended team – to staff and expedite model development, while building agency staff capability/knowledge base • Preserving model compatibility/continuity – update networks and zonal data concurrently for the two models • Developing comprehensive documentation– technical methodology and user guide 6

  7. 02 – Building Support for New Model Targeted outreach to garner support for update of the model stressed: Limitations of the current model – inability to support evaluation transportation equity, congestion pricing and non-motorized modes Value of compatibility peers and state DOT – a first step, advanced model would support data exchange and preserve a “place at the table” Benefits of incremental implementation – would preserve planning program continuity and keep model current Tour-based model as a cost effective, extendable solution – $200K for consultant development contract, $10K for software 7

  8. 03 – Model Development Approach Rather than upfront development or “transfer and refine” implementation, PPACG chose to use an adapted incremental implementation approach: Tour-based model was conceived as a first step to in path to advanced model– future upgrade elements, sequence and timing of next steps were identified upfront. Tour-based model was built on current trips-based model structure and software platform with selected common model elements maintained to facilitate transition to new model. Used a parallel development approach was used – the trips-based model was maintained as the “official model” during development and will be maintained for extended period to support transition and model applications by external users. 8

  9. 04 – Tour-based Model Structure The PPACG tour-based model: Retains current software – implements a tour-based add-on module (VISEM); only synthetic population generation (PopGen) is an external data/process. A combination of python scripts and built-in procedures are used, all within VISUM. Focuses on essential upgrade – addition of tour generation model. Retains trips-based model components – distribution, mode choice and trip assignment were retained and adapted. Uses matrix operations to convert retained components to tour-based format. Implements enhanced time-of day resolution–hourly time-of-day. 9

  10. Overview of Methodology • Disaggregate simulation-based tour generation is combined with aggregate tour-based destination and mode choice Disaggregate Person Level Tour Frequency Choice Synthetic Population (PopGen) Number of work-based sub-tours constrained to work destinations Tour type / stop frequency choice at zone level by Person type Aggregate destination/mode choice by Person/tour type External Data/Process VISUM Work based sub-tour using work destination as control total

  11. Tour generation • Tour generation is carried out in two steps: Simulation of Tour Frequency Choice at Person Level (logit model) (number of work/school/non-work tours) Work School Non-Work 1 3 2 1 2 0 1 3 2 0 3 0 Tour inflation (Stop Frequency) Inflates skeletal tours into tours with explicit stops (logit model or fractional splits of half-tours is based on estimation results) HWH Takes care of NHB trips HOWH HWH HWOH HOOWH Disaggregate Aggregate H…W…H

  12. Tour frequency choice model • Standard estimated choice model attributes listed below were considered. Many variables were found to be significant; a constrained local data set was ultimately used Source: Activity-Based Travel Demand Models, A Primer, Transportation Research Board, 2014.

  13. Tour-based Mode choiceImplemented as a logit/nested logit model applied at tour/destination activity level… Transformed Utility of mode m between an origin i and destination j Choice Probability of a mode m between an origin i and destination j

  14. Tour-based distribution Maintains current model user form (combined) and uses rubber-banding (minimizing out-of-way travel) [operating on half-tours] for tours with primary work purpose and without rubber-banding [operating on closed full tours] for other tours. Probability of destination choice Trips between an OD pair

  15. Tour-based distribution – without rubber-bandingSuccessive destination choice and matrix transpose operations till the tour closes…Tour with 2 stops HOOH computed as: HOOH

  16. Tour-based distribution – with rubber-bandingSplit full tour into half-tour and compute trip to successive legs by using composite utility function to minimize out of way travelHOWH computed as: HW first, then HOW and WH Primary destination choice H->W

  17. Tour-based distribution Stop location attraction potential The locations for stops on tours may be different based on tour type… hence, two different stop destination attraction size variables are used for the tour calculation based on the tour type: • Work tour (HOWH … ) <= more oriented towards school/drop off type attractions • Non-work tour (HOOH) <= more oriented towards commercial attractions

  18. Time of day calculation • Time of day calculation can adopt two forms: • OPTION 1: Time of day choice model with choice between various outbound and inbound time-of-day combinations • Demand strata created for each tour time-of-day combination: HWH_>AM < MD HWH_>AM < PM HWH_>AM < EV HWH_>… • OPTION 2: Peaking factors applied to activity pairs by person type • Calculate tour leg matrices for entire day • Multiply tour leg matrix by time-of-day factors for activity pair Option 2 was implemented using travel survey-based peaking factors.

  19. 05 – PPACG Beta Tour-Based Model • The beta tour-based model was developed from current model base year data. Calculations were coded as a mix of python scripts and built-in procedures: 27

  20. Model FACTS and dimensions • How the model measures up: • Hardware – Standard laptop computer (workstation specifications desirable) • Software – PTV VISUM with tour-based add-on module • Run time – shorter than trips-based model, < 3 hours • VISUM version (scenario) size on disk – < 1GB • Development timeframe – beta version three months • Development cost – $210K 28

  21. Beta (2010) Tour-Based Model Assignment Matching Validated Trips-Based Model Assignment 29

  22. Extra Slides for Q&A

  23. Tour inflation • STEP 1: Process the travel survey database and extract Outbound and Inbound stops on work and school tours + total stops on non-work tours Output extract…

  24. Tour inflation • STEP 2: Develop fractions from the processed tour stop frequency to obtain stop frequency breakdown matrix:

  25. Tour inflation • STEP 3: Apply the fractions developed in step 2 to total work/school tours in TAZ to obtain total number of expanded tours per TAZ Work/School Tours per TAZ

  26. Tour based data model structureTour-Based Model Organized as Objects • Person groups (analyzed demographic group) • Structural properties (land use/employment data/ attraction size variable) • Activities • Activity Chains • Demand Strata • Activity Pairs • Demand Time Series (activity peaking profile)

  27. Tour based data model structure • Person groups (analyzed demographic group)

  28. Tour based data model structure Structural properties (land use/employment data, used as a measure of attractiveness of an activity location – related to attraction equations)

  29. Tour based data model structure Activities (main reason for trip making, trip purpose in the 4-step context) Anchor Activity Primary Destination Choice

  30. Tour based data model structure • Activity Chains - Describe a sequence of activities for example: Home-Work-Dwell-Home (HWDH) includes activity pairs: HW, WD, DH

  31. Tour based data model structure • Demand Strata - Link Activity Chains and Person Groups

  32. Tour based data model structure Activity Pairs • Describe origin to destination trips made within a tour – HWOH  HW, WO, OH | trip leg sequence • Also link activity pairs to time of day profiles

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