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Travelled distance estimation for GPS-based round trips : car-sharing use case

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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.
Keywords
Data quality, travelled distance, CAN-BUS data, GPS data

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Chicago
Lopez Aguirre, Angel, Ivana Semanjski, Dominique Gillis, Daniel Ochoa Donoso, and Sidharta Gautama. 2016. “Travelled Distance Estimation for GPS-based Round Trips : Car-sharing Use Case.” In DATA ANALYTICS 2016 : The Fifth International Conference on Data Analytics, ed. Sandjai Bhulai and Ivana Semanjski, 87–92. IARIA.
APA
Lopez Aguirre, A., Semanjski, I., Gillis, D., Ochoa Donoso, D., & Gautama, S. (2016). Travelled distance estimation for GPS-based round trips : car-sharing use case. In S. Bhulai & I. Semanjski (Eds.), DATA ANALYTICS 2016 : The Fifth International Conference on Data Analytics (pp. 87–92). Presented at the The Fifth International Conference on Data Analytics, IARIA.
Vancouver
1.
Lopez Aguirre A, Semanjski I, Gillis D, Ochoa Donoso D, Gautama S. Travelled distance estimation for GPS-based round trips : car-sharing use case. In: Bhulai S, Semanjski I, editors. DATA ANALYTICS 2016 : The Fifth International Conference on Data Analytics. IARIA; 2016. p. 87–92.
MLA
Lopez Aguirre, Angel, Ivana Semanjski, Dominique Gillis, et al. “Travelled Distance Estimation for GPS-based Round Trips : Car-sharing Use Case.” DATA ANALYTICS 2016 : The Fifth International Conference on Data Analytics. Ed. Sandjai Bhulai & Ivana Semanjski. IARIA, 2016. 87–92. Print.
@inproceedings{8507840,
  abstract     = {Traditional travel survey methods have been widely used for collecting information about urban mobility although, since middle of the 90{\textquoteright}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.},
  author       = {Lopez Aguirre, Angel and Semanjski, Ivana and Gillis, Dominique and Ochoa Donoso, Daniel and Gautama, Sidharta},
  booktitle    = {DATA ANALYTICS 2016 : The Fifth International Conference on Data Analytics},
  editor       = {Bhulai, Sandjai  and Semanjski, Ivana},
  isbn         = {978-1-61208-510-4},
  language     = {eng},
  location     = {Venice},
  pages        = {87--92},
  publisher    = {IARIA},
  title        = {Travelled distance estimation for GPS-based round trips : car-sharing use case},
  year         = {2016},
}