Spatiotemporal Identification of Trip Stops from Smartphone Data

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.

Mapping urban greenspace use from mobile phone GPS data

“…In recent years, use of GPS to inform both travel surveys and health research has increased enormously [34][35][36][37]39,41,42]. GPS data has the potential to resolve the Unknown Geographic Context Problem by facilitating a detailed understanding of how people experience their local environment on a day-to-day basis [32][33][34][35].…”

“…This was achieved by identifying time gaps of an appropriate length (for example where there was a gap of greater than x minutes between one GPS recording and the next). Although we were unable to find previous studies that have used time gaps to identify trips, studies that have identified stops within trips have typically used periods of two to five minutes [ 36 , 38 , 45 ]. However, Schuessler and Axhausen [ 49 ] noted that longer periods may be appropriate in the case of poor signal reception, and indeed we found a period of ten minutes to be more appropriate here.…”

“…Data from GPS-enabled mobile devices has revolutionised travel surveys [36][37][38], and its promise in health studies, particularly studies of physical activity and exposure to hazards, has also been recognised [34,[39][40][41]. The use of GPS data is adding to diary-based studies and providing more accurate specifications of both activities and environmental exposures [34,35,37].…”

“…A drawback of GPS data, however, is that the datasets are frequently large and challenging to clean and interpret [ 36 , 38 ]. Two main approaches to cleaning and interpreting GPS data, i.e.…”