A GPS-based bicycle route choice model for San Francisco, California

Hood, Jeffrey; Sall, Elizabeth; Charlton, Billy

doi:10.3328/tl.2011.03.01.63-75

Cited by 309 publications

(239 citation statements)

References 8 publications

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“…Several researchers have used GPS to document bicycle travel routes and speeds (Dill and Gliebe 2008;Hood et al 2011). Data from GPS units can be reviewed by survey participants during follow-up interviews to correct preliminary route and mode information (Dill and Gliebe 2008).…”

Section: Considerations and Future Researchmentioning

confidence: 99%

Measuring transportation at a human scale: An intercept survey approach to capture pedestrian activity

Schneider

2013

JTLU

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Pedestrian travel data are critical for measuring and analyzing sustainable transportation systems. However, traditional household travel surveys and analysis methods often ignore secondary modes, such as walking from a street parking space to a store entrance or walking from a bus stop to home. New data collection and analysis techniques are needed, especially in areas where walking is common. This paper describes an intercept survey methodology used to measure retail pharmacy customer travel to, from, and within 20 shopping districts in the San Francisco Bay Area. Of the 1003 respondents, 959 (96 percent) reported all modes of travel used from leaving home until returning home, including secondary modes. Walking was the primary travel mode on 21 percent of respondent tours, but an analysis of secondary modes found that 52 percent of tours included some walking. Pedestrian travel was particularly common within shopping districts, accounting for 65 percent of all trips within 804 meters (0.5 miles) of survey stores. Detailed walking path data from the survey showed that respondents in denser, more mixed-use shopping districts tended to walk along the main commercial street as well as other streets connecting to the core shopping area, while respondent pedestrian movements in automobile-oriented shopping districts tended to be contained within specific shopping complexes. a rjschnei@uwm.edu

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Section: Considerations and Future Researchmentioning

confidence: 99%

Measuring transportation at a human scale: An intercept survey approach to capture pedestrian activity

Schneider

2013

JTLU

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“…The application of GPS data in transportation modeling includes: generation of trip rate correction factors (Wolf et al 2003a;Bricka and Bhat 2006), travel mode detection (Tsui and Shalaby 2006;Reddy et al 2010;Widhalm et al 2012), trip end/activity location detection (Wolf et al 2001;Stopher et al 2005;Ashbrook and Starner 2003), assessment of transportation network conditions (Hackney 2005;Stopher and Swann 2007), and route choice analysis (Jan et al 2000;Li et al 2005;Hood et al 2011;Quddus et al 2003). GPS traces can provide accurate spatial and temporal information of individuals, but generally the attainable sample size and observation period of GPS-assisted surveys are still limited.…”

Section: Introductionmentioning

confidence: 99%

Discovering urban activity patterns in cell phone data

et al. 2015

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Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies.

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“…However, studies on cyclist route choice (revealed preference research) suggest that distance and travel time are the most important factors (Broach et al, 2012;Gommers and Bovy, 1987;Menghini et al, 2010). Moreover, cyclists balance their total journey length and route directness meaning that cyclists aim to reduce the number of turns (Broach et al, 2012;Hood et al, 2011;Raford et al, 2007). Revealed preference studies also report a preference for routes along roads with low motor traffic volumes, standalone bicycle tracks, bicycle lanes and separated bicycle paths, although their contribution to decision making is less important than distance and time (Broach et al, 2012;Gommers and Bovy, 1987;Howard and Burns, 2001;Menghini et al, 2010).…”

Section: Effects Of Bicycle Lanes and Paths On Mobilitymentioning

confidence: 99%