Completing the Cycle: Incorporating CycleTracks into SF-CHAMP

Editor's Notes

• #17: 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.
• #23: 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. http://trb.metapress.com/content/tv306m812140p330/
• #25: SharrowsCongestionNight-timeNo bike lanesCapacity of roadwayBike laneBike pathCrimeWeather
• #26: 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.
• #28: 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.
• #29: 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
• #31: Talking points/or circle/or flip through
• #33: Yellow doesn’t stand out enough
• #35: 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.
• #36: 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.
• #39: Our code is open source, and there are a number of agencies who have tried their hand at modifying it to their own needs.
• #44: Back pocket all four of these?