Abstract
As part of a 3-year study on cycling infrastructure, a smartphone app was developed to passively collect location information from about 500 participants resulting in 96 million observations. By using WiFi and network location rather than the Global Positioning System (GPS) receiver in the smartphones, the app is able to collect frequent and good quality location data for long periods of time without limiting the use of the smartphones for other purposes. Tests of the data collected using the smartphone app compared to a standalone GPS devices carried by the same participants are presented to provide evidence of the relative differences between the methods presented here with those traditionally used with GPS travel surveys. The data are then used to identify trip stops using a new method that employs a moving average position and incorporates mechanisms that are used to detect relatively short stops including stops involving a transfer of transport mode. In total 86,407 stops are identified with a median time of 1 h and a spatial distribution consistent with the travel diary data collected as part of the same study. The duration of the detected stops are also reasonably consistent with those from the travel diary for stops detected at activity locations such as workplaces. This paper provides evidence of the feasibility of using smartphone WiFi and network location sensors to collect and process data that can be used for analysis of travel behaviour.
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Notes
Figures from the United States
The study was approved by the University of Sydney Human Ethics Committee.
The app is available for other research on request. The ethics approval for the study precludes sharing of the collected data but some example data from the authors’ own testing is available on request.
Darker colours represent GPS data and lighter shades smartphone data.
In the design of the diary, every change of mode is considered to be a new leg. For example, a trip consisting of walking to a bus stop, taking the bus to near the destination and then walking to the final destination would be considered to be three legs.
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Ellison, A.B., Ellison, R.B., Ahmed, A. et al. Spatiotemporal Identification of Trip Stops from Smartphone Data. Appl. Spatial Analysis 12, 27–43 (2019). https://doi.org/10.1007/s12061-016-9188-0
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DOI: https://doi.org/10.1007/s12061-016-9188-0