Tour-Based Model for a Small Area

1. Tour-Based Model for a Small Area Presented at 11th Transportation Planning Applications Conference Reno, NV May 2011 William G. Allen, Jr., PE Consultant Windsor, SC

2. The Modern Modeller’s Muddle Where we are Where we need to be Trips Tours Further than you think

3. Glynn County, Georgia • Southeast Georgia coast, between Savannah and Jacksonville • County seat: Brunswick • 2006: 67,600 people, 36,600 jobs • Home to St. Simons Island, Jekyll Island, Federal Law Enforcement Training Center • Bisected by I-95

4. Glynn County Location

5. Why a New Travel Model? • Georgia DOT already has a travel model -- the official MPO model • County was growing rapidly • GDOT not always able to respond quickly • County wanted more detail, more focus on local roads and small areas • County wanted to control the process

6. Where to Start? • Buy the software (Cube Voyager, the GDOT standard) • Software is a platform, not a model • Using a model is easy, creating one requires specialized expertise • County hired a consultant to develop the model and train staff

7. Observed Travel Data • Home interview survey is best • Expensive ($200/household) • Difficult, time-consuming • Add-on to NHTS (next one: 2017) • 2000 Census has some data on Work travel • GDOT has traffic counts • Validation year: 2006 (before gas price spike, recession)

8. Glynn County Approach • Very limited budget and schedule • Subdivide the GDOT TAZes and add network detail • GDOT: 397 zones, County: 676 • Transfer model from other cities, adjust to reflect local conditions & counts • Copy some information from GDOT model

9. Conventional Travel Modelling • “Four step process”: generation, distribution, mode choice, assignment • Travel is zone-to-zone aggregate totals • Trips are independent of each other • Used for over 50 years

10. The New Way • Model every individual trip • Measure travel in round-trip tours • More realistic representation of travel • Faster computers make calculations feasible • More accuracy and flexibility possible • Favored by academics and researchers • More theoretically correct • Slowly becoming adopted

11. What Is a “Tour”? Stop k (Half)-Tour Origin i Destination j Trip

12. Challenges • Most tour models have been data intensive, costly, and time-consuming • A moving target: research is on-going • Typical development: 2 - 3 years, $ millions • Often custom-written software (black box) • Model run times measured in days • New York, Columbus, Sacramento, Atlanta

13. Simplified Version • County staff expressed no preference • Limited resources • 6 months, $60K, no survey data • Not a research project, need real results • Not a true activity-based model • No transit • Doesn’t model personal interactions, household relationships, or trip sequencing

14. Some Things Are the Same • Still must represent the basic choices: • How many trips? • Where? • By what mode? • At what time? • By what route? • Sequence of steps is not much different • Most components are familiar

15. Some Things Are Different • Travel represented as round-trip tours • Model discrete travel by HHs, not zonal averages • Use Monte Carlo simulation to model individual travel choices • Added simple time of day model (4 periods) • New intermediate stop model • How many stops? • Where?

16. Model Synthesis • Use Baltimore 2001 NHTS add-on survey • Port, manufacturing, tourism, I-95 – it’s Brunswick on a larger scale • Provided many parameters, relationships • Adjust for geographic scale • Borrowed some parameters from GDOT model • Validated to 2000 JTW & local counts

17. Generation • Starts conventionally • Purposes: HBW, SCH, HBS, HBO, COM, TRK, ATW, VIS, 4 I/E’s, 4 E/I’s • Prods: look-up table by size & income • Attrs: regression by zone • Rates from GDOT and Baltimore models • Non-motorized share removed • Output a record for each RT tour

18. Distribution • Allocate productions to zone of tour attraction • HBW, SCH: work or school • Other: where you spent the most time • Discrete destination choice • Probabilities calculated by gravity function • F’s based on 2000 JTW; non-work by analogy • Process iterated to match attractions

19. Intermediate Stops • Each journey is a round-trip tour • Main tour purposes: work, school, shop, other, at-work, visitor • Stops are made on the way from home and on the way back home • 30% of tours involve at least 1 stop • Stops are for shopping, personal business • No Non-Home-Based trip purpose!

20. Two Sub-Models • Two multinomial logit models • Model 1: How many stops? (separately by P-A and A-P) • Based on tour purpose, HH size, income, area types, retail emp, P-A travel time • A-P stops also based on number of P-A stops • Model 2: Where are the stops? • Logit destination choice • Detour time, area type, employment

21. Other Models • Time of Day: fixed percents by purpose used to allocate half-tours to 4 time periods • Mode Choice: standard logit auto occupancy model: 1, 2, 3, 4+ per auto • Trip Accumulator: splits RT tours into individual O/D trips by SOV / HOV / TRK and period • Conventional assignment by period, veh type • One speed feedback loop

22. Uses of New Model • Evaluate growth proposals • Support impact fees • Long-range plan analysis • Provide data to site traffic studies • Corridor studies

23. So What? • Runs in 1 hour on any Windows computer • No black box software; all in Cube Voyager • Easy to run; requires few inputs • Accuracy was improved (10% RMSE) • Incorporated the key features of tour-based models • Proof that new approach can be applied to a smaller area, on a budget (6 months, $60 K)

24. Questions? Presentation is available at www.williamgallen.com