Completing the Cycle: Incorporating CycleTracks into SF-CHAMP


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This presentation shows how the data we gathered from the smart phone application, CycleTracks, was used to develop a bicycle route choice model which was then integrated into SF-CHAMP, the San Francisco activity-based travel demand model

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  • Here is a comparison of the demographics of the participants whose traces survived the data processing to the subpopulation from the Bay Area Travel Survey that reported a cycling trip in San Francisco. As you can see, the CycleTracks sample is over twice the size of BATS, which contained 50,000 households, illustrating why seeking a representative sample to study cycling is not feasible. But, our sample is biased. While the mean age in the two samples are not significantly different, our study does include a lower proportion of women at 21% compared to BATS’ 36%. While we don’t have a population to compare cycling frequency against, we also suspect that our sample is biased toward frequent cyclists. The bias is a limited problem because we were able to account for it with interaction variables in model estimation.
  • Schuessler, Nadine and Kay W. Axhausen. “Processing Raw Data from Global Positioning Systems Without Additional Information,” Transportation Research Record : Journal of the Transportation Research Board, No 2105. Washington D.C., 2009, pp. 28-35.
  • SharrowsCongestionNight-timeNo bike lanesCapacity of roadwayBike laneBike pathCrimeWeather
  • We use a “Doubly Stochastic Route Search” to find other potential routes in the available choice setBovy, P. & Fiorenzo-Catalano, S. (2007), “Stochastic route choice set generation: behavioral and probabilistic foundations,” Transportmetrica 3, 173-189.
  • Here are the coefficients from the path size multinomial logit route choice model. Obviously, cyclists prefer shorter routes, with fewer turns, and don’t go the wrong way down a one way street unneccessarily. The coeficients on the proportions of the different bicycle facility types are measured on the same scale, and so represent the relative preferences for these treatments. Bike lanes are preferred the most, especially by infrequent cyclists, a preliminary indication that installing bicycle lanes may attract new cyclists. Hill climbing is especially disfavored by women and on commute trips. The path size variable corrects for the correlation between alternatives due to route overlap. The coefficient is not significantly different from the theoretically correct value in a model with a scale parameter of one, another indication of the quality with which our choice sets represent the consideration sets. Traffic volume, vehicle speed, number of lanes, crime, and rain had no effect.
  • The average cyclist will Avoid a turn if it costs no more than one-tenth of a mileAvoid climbing a hill 100 feet tall as long as the detour is less than roughly one mileAvoid traveling the wrong way down a one-way street unless doing so saves more than four times the distance elsewhereAdd a mile on bike lanes in exchange for only half a mile on ordinary roadsBike paths vs Bike Lanes could be due to limited off-street bike path facilities in SF and other factors that make them less attractive (although Krizek also found similar preferences).Mention the other variables that were significant in estimation: females and work-commuters were more hill-averse
  • Talking points/or circle/or flip through
  • Yellow doesn’t stand out enough
  • Not apples to apples: the bike network coded in Harold was much more aggressive. That said, the results are still interesting:Harold has an auto-stick, but no carrot, so the mode switchers switch to whatever is best for them, which happens to be pretty even across bike, walk and transit.Fury has a very strong bike-carrot, and walk modes benefit mildly from the road diets. Biking draws from auto but also transit because of tour distance.
  • Harold again has the auto stick. Walk trips are up because of transit tours being up.Fury has the bike carrot. Walk trips are down (despite the walk tours being up) because of the transit tours being down – many walk trips are part of a transit tour.
  • Our code is open source, and there are a number of agencies who have tried their hand at modifying it to their own needs.
  • Back pocket all four of these?
  • Completing the Cycle: Incorporating CycleTracks into SF-CHAMP

    1. 1. Completing the Cycle: Incorporating CycleTracks into SF-CHAMPUsing technology to understand the needs of cyclistsSAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY Fall 2012
    2. 2. Outline1. Why make CycleTracks?2. What does CycleTracks do?3. Who used CycleTracks and why?4. What data did we get from CycleTracks?5. What did we do with that data?6. Evolution and future of CycleTracks
    3. 3. 1. Why CycleTracks?
    4. 4. Why CycleTracks?Need to prioritize projects, including bike projects.  Estimate a bike choice model that evaluated various bike infrastructure features  Needed bike route choice data on a budget.
    5. 5. 2. What doesCycleTracks do?
    6. 6. Enter personal data (optional)
    7. 7. Enter New Trip
    8. 8. Review Saved Trips
    9. 9. That’s it?Bells and whistles could promote deviation from planned route. Features! Good Data. Flare! Yawn. More users!
    10. 10. 3. Who used CycleTracks and Why? - User Recruitment - Participants
    11. 11. Participants: who gaveus data?
    12. 12. SF Participants: Fall 2009 to Spring 2010 CycleTracks BATS N-366 N=153 z-statAge Mean 34 33 1.1Gender Female 21% 36% -3.5Cycling Frequency Daily 60% Several Times/Week 34% Several Times/Month 7% Less than once a month 0% N/A
    13. 13. 4. What data did we get? - Data Quality - Data Summaries
    14. 14. Data Quality: some good, some bad
    15. 15. Urban Canyon Effect Haight Ashbury vs Downtown
    16. 16. GPS Signal at Beginning of Trip
    17. 17. Not on a Bike
    18. 18. Post Processing Warranted 5,178 traces Gaussian 497 users smoothing Activity & mode detection 3,034 bike Map stages h matching 366 users(Schüssler & Axhausen 2009)
    19. 19. 5. What did wedo with theCycleTracksData?
    20. 20. Matched Route Features to the Chosen Route…
    21. 21. …as well as to a set of routes that were notchosen
    22. 22. What makes us choose one bike route overanother ? Personal Trip Info Features Route Which route Features of was Available Route chosen? Routes Choice Model
    23. 23. Estimation resultsAttribute Coef. SE t-stat. p-val.Length (mi) --1.05 0.09 --11.80 0.00Turns per mile --0.21 0.02 --12.15 0.00Prop. wrong way --13.30 0.67 --19.87 0.00Prop. bike paths 1.89 0.31 6.17 0.00Prop. bike lanes 2.15 0.12 17.69 0.00 Cycling freq. < several per wk. 1.85 0.04 44.94 0.00Prop. bike routes 0.35 0.11 3.14 0.00Avg. up-slope (ft/100ft) --0.50 0.08 --6.35 0.00 Female --0.96 0.22 --4.34 0.00 Commute --0.90 0.11 --8.21 0.00Log(path size) 1.07 0.04 26.38 0.00 2,678 weighted observations, ρ2 = 0.28
    24. 24. Average Marginal Rates of Substitution MRS of Length on Street for Value Units Turns 0.10 mi/turn Total Rise 1.12 mi/100ft Length Wrong Way 4.02 None Length on Bike Paths 0.57 None Length on Bike Lanes 0.49 None Length on Bike Routes 0.92 None SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 28
    25. 25. Updates to SF-CHAMP Synthesized Core, 3 iterations Population Work Location, Land Use Destination Choice, Mode Choice Tour Generation Networks Networks Logsums +Bike Vars! Tour & Trip Mode ChoiceBike Route Choice Set Road & Transit Non-Motorized Bike Generation & Assignment/Skimming (Distances) Logsums Skimming SkimmingInitial Road & Transit Assignment/ Skimming Final Bicycle Assignment SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 29
    26. 26. Bike Accessibility: From 4th and King SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 30
    27. 27. Bike Accessibility: To 4th and King SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 31
    28. 28. Bike Logsums: From 4th and KingEffect of Bike Plan Build SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 32
    29. 29. Bike Logsums: To 4th and KingEffect of Bike Plan Build SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 33
    30. 30. Preliminary Results:Tour Mode Choice Sensitivity Tour Difference Daily Tours v4.1 Harold v4.3 Fury Bike 300 0.1% 1,300 0.9% Walk 300 0.0% 200 0.0% Transit 200 0.0% -900 -0.1% Auto -1,000 -0.0% -600 -0.0% Total -200 -0.0% 0 0.0% SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 34
    31. 31. Preliminary Results:Trip Mode Choice Sensitivity Trip Difference Daily Tours v4.1 Harold v4.3 Fury Bike 500 0.1% 3,000 0.8% Walk 1,100 0.0% -500 -0.0% Transit 850 0.0% -600 -0.0% Auto -2,400 -0.0% -1,300 -0.0% Total 0 0.0% 600 0.0% SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 35
    32. 32. SF-CHAMP Predicted Bike Trips Bikes / hour 0 180 20 360SF-CHAMP v4.1 “Harold”
    33. 33. 6. Evolution andFuture ofCycleTracks
    34. 34. All Open Source • GPL3 License • Code on GitHub • Fork us!
    35. 35. e.g. AggieTrack
    36. 36. CycleTracks Works Everywhere…We already have the database set upAgencies can download “scrubbed” data Austin,TX Monterey Bay, CA …and more!
    37. 37. Title
    38. 38. Questions?
    39. 39. Bike Accessibility: From Inner Sunset SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 43
    40. 40. Bike Accessibility: To Inner Sunset SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 44
    41. 41. Bike Logsums: From Inner SunsetEffect of Bike Plan Build SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 45
    42. 42. Bike Logsums: To Inner SunsetEffect of Bike Plan Build SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY 46