Travelled Distance Estimation for GPS-Based Round Trips Car-Sharing Use Case

Abstract

Abstract-Traditional travel survey methods have been widely used for collecting information about urban mobility although, since middle of the 90's Global Position System (GPS) has become an automatic option for collecting more precise data of the households. But how good is the collected data? many studies on mobility patterns have focused on the GPS advantages and leaving aside its issues. However, when it comes to extract the frequency of the trips and travelled distance this technology faces some gaps due to 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, middle and long trips). Results from 170 participants and more than a year of GPS-tracking show that around 9% of the travelled distance is not captured by the GPS and it affects more to 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 on GPS.

Assessment of Smartphone Positioning Data Quality in the Scope of Citizen Science Contributions

“…Finally, our findings show consistency with a previous study [42], in which a driver mode reported comparable figures (9% of missing data). However, that study made use of GNSS loggers installed in a car instead of smartphones, where the GNSS loggers were mainly affected by the cold/warm start because of the long periods of being turned off (e.g., a car being on a parking lot).…”

“…The differences can be in the interaction with the device, since a car driver must be focused on the road rather than manipulating the smartphone, whereas a passenger is free to interact with the smartphone all way long. These outcomes are comparable to a previous study [42], in which data quality was assessed using GNSS loggers instead. The mentioned study reported 9% of missing data in a car mode.…”

Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition

“…Mizzi et al [ 16 ] proposed a method to identify the pedestrian mobility characteristics on a road network to help reconstruct trajectories. Lopez et al [ 17 ] studied the effect of missing GPS observations on the traveled distance estimation and proposed a regression model. Dewhirst et al [ 11 ] proposed a method combining accelerometer-based speed and magnetometer heading estimates (dead reckoning) with low fix rate GPS drift correction to improve the accuracy of path and distance traveled estimates for studies of animal locomotion.…”

Error Sources in the Analysis of Crowdsourced Spatial Tracking Data

“…Positioning errors caused by inherent errors in the GNSS device or by signal reception issues have an influence on the trip lengths. Several authors have identified this phenomenon [4,5] and Lopez has experimentally determined the distribution of the residual distance for several speeds [6,7]. This overreporting has an influence of less than 1 % at 30 km /h, and is therefore relatively minor for motorized traffic.…”