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Network Assignment and Equilibrium for Disaggregate Models

John Gibb DKS Associates Transportation Solutions. Network Assignment and Equilibrium for Disaggregate Models. Disaggregate traffic assignment. Solves pressing modeling problems Opens modeling opportunities Is practical. Activity-Based Demand Models: Disaggregate Synthesis.

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Network Assignment and Equilibrium for Disaggregate Models

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  1. John Gibb DKS Associates Transportation Solutions Network Assignment and Equilibrium for Disaggregate Models

  2. Disaggregate traffic assignment • Solves pressing modeling problems • Opens modeling opportunities • Is practical

  3. Activity-Based Demand Models: Disaggregate Synthesis • Individual units of demand • Process one at a time • Heterogeneous choice behavior • Single outcome of each choice • Each choice linked to the person & itinerary • Can you do this to assignment?

  4. Assign each individual trip?You gotta be kidding! • Millions of trees, instead of thousands • No “bulk efficiency” • Trees from origin to all destinations • Can’t load whole matrix row at a time

  5. Single trip search space

  6. Is there a better way? • A-star algorithm (1968 - Hart, Nilsson, Raphael) • Vehicle-navigation systems, gaming programs, some dynamic assigners • Very similar to Dijkstra’s • “Informed” search helped by optimistic node-to-destination time estimates • Narrower search space than Dijkstra-to-destination • Exact best path

  7. Network search spaces example Dijkstra Tree A-Star 12% of regional network (except other zone connectors) 2.4% of regional network (except other zone connectors)

  8. Individual Trip Loading • Single-outcome: One path per trip • Save the path • Deduct “old” path when assigning “new” path • Iteration Step Size = fraction of the population assigned between link-delay updates • Several iterations per pass • Complete pass before starting new pass

  9. Experimental Test Assignment • ≈ 4,500,000 trips from an activity-based demand model • Point-specific origin and destination • 6 complete passes through all trips • 900 iterations (link-delay updates) • Gradually-declining step sizes • from 300,000 trips in early iterations, • to 7,100 trips in last iteration • First pass ≈ 20 minutes, all others ≈ 40 minutes

  10. Average Gap of Individual Trips

  11. Vs. Trip-Based Assignment

  12. Direct Comparison: PM Average Gap

  13. Maximum Gap of Individual Trips

  14. Disaggregate Assignment Solves • Heterogeneous path choice • Complex tolls, individual value of time • Centroid aggregation error • Origin, destination points (parcels, addresses…)

  15. Parcels: Elastic zone connectors

  16. Parcels: Elastic zone connectors plus shortcuts

  17. Disaggregate Assignment Creates Opportunities • Warm-starts • Path queries • Full information for dynamic simulation • Activity-based model trip specified to the minute • Any detail scale • Lots of simulation runs, not once after-model • Time-specific skims • Stochastic path choice

  18. Further development • Loading schedule experiments • Full Activity-Based Application • Warm-starts • Dynamic assignment • Fast simulations preferred • Individual skims to the activity-based model • Destination choice samples • Time-specific • Transit

  19. Questions?

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