Abstract
Traditional travel survey methods have been widely used for collecting information about urban mobility although Global Position System (GPS) has become an automatic option for collecting more precise data of the households since mid-1990s. Many studies on mobility patterns have focused on the GPS advantages leaving aside its issues such as the quality of the data collected. However, when it comes to extract the frequency of the trips and travelled distance, this technology faces some gaps due to the related issues such as signal reception and time-to-first-fix location that turns out in missing observations and respectively unrecognised or over-segmented trips. In this study, we focus on two aspects of GPS data for a car-mode, (i) measurement of the gaps in the travelled distance and (ii) estimation of the travelled distance and the factors that influence the GPS gaps. To asses that, GPS tracks are compared to a ground truth source. Additionally, the trips are analysed based on the land use (e.g. urban and rural areas) and length (e.g. short, medium and long trips). Results from 170 participants and more than a year of GPStracking show that around 9 % of the travelled distance is not captured by GPS and it affects more short trips than long ones. Moreover, we validate the importance of the time spent on the user activity and the land use as factors that influence the gaps in GPS.
Highlights
Traditional travel survey methods have been widely used in transportation research as a tool for collecting information at an individual or household level (Handy, 1996)
Cambio provided us with a dataset of the reservation details based on Controller Area Network bus (CAN-bus) data. (b) Global Positioning System (GPS) data that are collected through loggers installed on selected cars from the car-sharing company
As one of the interests in this study is to identify the differences in GPS-based distance and the driven kilometres, we use the car odometer-sensor data as the ground truth
Summary
Traditional travel survey methods have been widely used in transportation research as a tool for collecting information at an individual or household level (e.g. description of demographics, travel patterns, trip purpose and mode choice) (Handy, 1996). Later studies (Bolbol et al, 2012; Schuessler et al, 2015) evaluated the use of processed GPS data for both trip tracking and transportation mode detection without the support of questionnaires. Their results showed that trip identification deviates slightly from the census data, whereas. We use tracking data from a fleet of shared cars to (i) assess the gaps present in the GPS-based distance and their possible effect in mobility studies, and (ii) to estimate the travelled distance and the relevant factors that influence the GPS gaps.
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