ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-4-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 209–215, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-209-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 209–215, 2020
https://doi.org/10.5194/isprs-annals-V-4-2020-209-2020

  03 Aug 2020

03 Aug 2020

EXPLORATION OF MUNICIPAL MOBILITY USING SMARTPHONE GPS DATA

X. Peng1, C. Wang1, K. Liu1, A. Quinones1, S. Li2, and J. Shan1 X. Peng et al.
  • 1Lyles School of Civil Engineering, Purdue University, USA
  • 2Research Division, Indiana Department of Transportation, USA

Keywords: Smartphone GPS data, Municipal mobility, Flow map, Kernel density function, Traffic volume prediction

Abstract. Due to the advances in location-acquisition techniques, smartphone GPS location data has emerged with opportunity for research, development, innovation, and business. A variety of research has been developed to study human behaviour through exploring patterns from these data. In this paper, we use smartphone GPS location data to investigate municipal mobility. Kernel density estimation and emerging hot spot analysis are used to the GPS dataset to demonstrate GPS user (point) distribution across space and time. Flow maps are capable of tracking clustering behaviours and direction maps drawn upon the orientation of vectors can precisely identify location of the events. Case study with Indianapolis metropolitan area traffic rush hour verifies the effectiveness of these methods. Furthermore, we identify smartphone GPS data of vehicles and develop a concise and effective method for traffic volume estimation for county highway network. It is shown that the developed smartphone GPS data analytics is powerful for predicting reliable annual average daily traffic estimation. The study showcases the capability of GPS location data in identifying municipal mobility patterns for both citizens and vehicles.