Enhancing Active Transportation Sensitivities of an Activity Based Model

Transcription

1 Enhancing Active Transportation Sensitivities of an Activity Based Model Jeff Hood, Hood Transportation Consulting Joe Castiglione, Resource Systems Group Joel Freedman & Chris Frazier, Parsons Brinckerhoff Wu Sun, San Diego Association of Governments 5 th TRB Conference on Innovations in Travel Modeling Baltimore, MD April 28, 2014

2 SANDAG plans $3 billion in grants for pedestrian and cycling improvements to

3 ABM had detailed spatial resolution, but walk & bike not sensitive to network attributes 3

4 Active Transportation Enhancements Car / Transit Networks TransCAD 1. Transit Skimming 8. Transit Assignment TAP TAP Transit Skims ABM 4. Activity & Destin. Choices 5. Tour Mode Choice 6. Intrmd. Stop Choices 7. Trip Mode Choice MGRA MGRA Walk Cost MGRA TAP Walk Cost MGRA MGRA Bike Logsum TAZ TAZ Bike Logsum All Streets Network Active Transport. Java Application 2. Walk Path Skimming 3. Bike Path Logsum Estimation 9. Bike Path Assignment Transit Link Volumes Trip Lists Bike Link Volumes 4

5 New Active Transport Network Legend Traffic signals High elevation chg. 5

6 Cycling Route Choice Utility Parameters Variable Coef. Source Distance on ordinary streets (mi.) Monterey Distance on class I bike paths Portland Distance on class II bike lanes Monterey Distance on class III bike routes Monterey Distance on arterials without bike lanes Monterey Distance on cycle tracks Distance on bike boulevards Portland Distance wrong way San Francisco Elevation gain, cumulative, ignoring declines (ft.) San Francisco Turns, total Portland Traffic signals, excl. rights & thru junctions Portland Un signalized lefts from principal arterial Portland Un signalized lefts from minor arterial Portland Un signalized xing of & left onto principal arterial Portland Un signalized xing of & left onto minor arterial Portland Log of path size Constrained 6

7 How can we estimate consistent multi path impedances? 7

8 What s wrong with single path impedance? Base Build Path 2 Dist.: 2 mi. Utility: 1.72 Path 2 Dist.: 2 mi. Bike Lane: Yes Utility: 1.09 Max. Max. Difference:

9 How about expected utility? Base Build Path 2 Dist.: 2 mi. Utility: 1.72 Path 2 Dist.: 2 mi. Bike Lane: Yes Utility: 1.09 Share: 70% Share: 30% Share: 55% Share: 45% Expected Utility: 1.12 Expected Utility: 0.96 Difference:

10 What if new alternatives appear? Base Build Share: 100% Share: 55% Path 2 Dist.: 2 mi. Bike Lane: Yes Utility: 1.09 Share: 45% Expected Expected Utility: 0.96 Difference:

11 How about the logsum? Base Build Path 2 Dist.: 2 mi. Bike Lane: Yes Utility: 1.09 log e u i i Logsum: 0.86 Logsum: 0.28 Difference:

12 What if routes overlap? Base Build Path 2 Dist.: 1.0 mi. Logsum: 0.86 Logsum: 0.16 Difference:

13 How about path size or cross nested model? Base Build Path Size: 1.0 PS in = a Γ i l a L i 1 M an Path Size: 0.5 Path 2 Dist.: 1.0 mi. Path Size: 0.5 Logsum: 0.86 Logsum: 0.86 Difference:

14 What if paths cannot be enumerated? 14

15 Can we control choice set size? Base Build Path Size: 1.0 Path 2 Dist.: 2 mi. Bike Lane: Yes Path Size: 1.0 Utility: 1.09 Bike Lane: Yes Path Size: 0.5 Utility: 0.55 Not Generated Not Generated Path 2 Bike Lane: Yes Path Size: 0.5 Utility: 0.55 Logsum: 0.27 Logsum: 0.55 Difference:

16 How about with path size link penalty? Base Build Base Build Stochastic Sampling Path Size Link Penalty Nassir et al. (2014), A Choice Set Generation Algorithm Suitable for Measuring Route Choice Accessibility, 93 rd TRB Annual Meeting. 16

17 BootRouting 17

18 Bootstrapping approximates the sampling distribution of a statistic by resampling observations from a given sample set } SE μ =? 18

19 BootRouting approximates sampling probabilities in stochastic path generation by repeatedly sampling overlapping routes As N, the proportion N a /N of paths using link a converges to the probability of sampling a path that uses the link, P a. P a 4 8 = 1 2 The length weighted average l a N a N L i a Γ i approximates the sampling probability of a path P Γ i. 19

20 BootRouting approximates sampling probabilities in stochastic path generation by repeatedly sampling overlapping routes 20

21 Sensitivity Test Results 21

22 Change in logsum: min. N = 8, size = 2 22

23 Change in logsum: min. N = 16, size = 4 23

24 Change in logsum: min. N = 24, size = 6 24

25 Target choice set size stratified by distance, then normalized to one Distance (mi.) 0.0 to to to to to 20.0 Total choice set size Min. sample count not random not random Max. sample count not random not random Insufficient size at max. count for < 15% OD pairs 5k TAZs out to 20 miles 23k MGRAs out to 2 miles All streets network Java, 12 processors 25

26 Contacts JOE CASTIGLIONE SENIOR CONSULTANT JEFF HOOD PRINCIPAL JOEL FREEDMAN PRINCIPAL CONSULTANT CHRIS FRAZIER PROFESSIONAL ASSOCIATE WU SUN SENIOR TRANSPORTATION MODELER