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Improved treatment of special attractors

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Presentation on theme: "Improved treatment of special attractors"— Presentation transcript:

1 Improved treatment of special attractors
Kevin Stefan May 2017

2 Context Some major facilities not represented well in destination choice models: Airport, stadium, hospital, major mall, etc. Unique characteristics Often policy targets

3 Context Traditional approach usually to add to size or add k-factor
Alternative here: change travel disutility structure Consistent with theory – higher order goods have a larger market areas

4 Brisbane model form Aggregate trip-based model Fully nested structure:
Number of trips Destination choice Mode choice Hour of peak Time of day AM Mid PM Off SOV PnR HOV 2 HOV 3 KnR Transit Walk Bike 6-7 7-8 8-9

5 Number of trips (generation)
Destination choice TAZ TAZ TAZ TAZ TAZ TAZ TAZ Mode choice Hour of peak Time of day AM Mid PM Off SOV PnR HOV 2 HOV 3 KnR Transit Walk Bike 6-7 7-8 8-9

6 Adult nonworker age < 55
40 model segments 8 person types Primary student Secondary student Tertiary student White collar worker Blue collar worker Adult nonworker age < 55 Adult nonworker 55-74 Adult nonworker 75+ × 5 purposes each Home to primary* Primary to home Home to other Other to home Non home based * Primary: School for students, work for workers, escort for ANW < 55, shop for ANW 55+

7 Model form: base Logsum of mode choice / time of day choice model
Additional distance function Size term using population, school enrollment, employment (total and by ANZSIC)

8 Model form: Size treatment
Base model plus: Additional size term reflecting total employment in special attractor zones Calibrated to match total trips to SAs (as part of this study)

9 Model form: Distance treatment
Base model plus: Additional parameters modifying the distance function Not calibrated specifically for SAs

10 Distance treatment functions

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14 Airport trips: size treatment

15 Airport trips: distance

16 Size Distance More from further suburbs Too many clustered by airport

17 Conclusions Behavioural basis Attracts trips well
Better respects trip length distribution

18 Thank You! Thanks to my coauthors:
JD Hunt, Paul McMillan and Alan Brownlee, HBA Specto Inc.


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