The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIV-4/W3-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W3-2020, 33–43, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-33-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W3-2020, 33–43, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W3-2020-33-2020

  23 Nov 2020

23 Nov 2020

TOWARD FLEXIBLE DATA COLLECTION OF DRIVING BEHAVIOUR

M. Ameksa1, H. Mousannif1, H. Al Moatassime2, and Z. Elamrani Abou Elassad1 M. Ameksa et al.
  • 1LISI Laboratory, Computer Science Department, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech, Morocco
  • 2OSER Research Team, Computer Science Department, Faculty of Sciences and Technologies, Cadi Ayyad University, Marrakech, Morocco

Keywords: Data Collection, Driving Behavior, DVE’s Data, Driving Dimensions

Abstract. Recently, driving behavior has been the focus of several researchers and scientists, they are attempting to identify and analyze driving behavior using different sources of data. The purpose of this research is to investigate data acquisition methods and tools related to driving behavior, in addition to the type of data acquired. Using a systematic literature review strategy, this study identified tools and techniques used to collect data related to driving behavior among 120 selected studies from 2010 to 2020 in several literature resources. It then measured the percentages of the most commonly used methods, as well as the type of data collected. In-vehicle and IoT sensors was found to play the greatest role in data collection in approximately 67% of the documents selected studies; And concerning the type of data acquired, those relating to the vehicle are the most widely collected. Thus, this study definitively answers the question regarding the different data sources and data types used among researches. However, further studies are needed to give more attention to the driver's data and also to investigate the data from the three dimensions of driving (driver, vehicle, and environment) together as an integrated and interconnected system.