Volume XLII-2/W13
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1517–1523, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1517-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1517–1523, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W13-1517-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  05 Jun 2019

05 Jun 2019

FLOATING CAR DATA (FCD) FOR MOBILITY APPLICATIONS

A. Ajmar1, E. Arco2, P. Boccardo2, and F. Perez1 A. Ajmar et al.
  • 1ITHACA, Via Pier Carlo Boggio 61, 10138 Torino, Italy
  • 2Politecnico di Torino – DIST, Torino, Italy

Keywords: Mobility services, Road network, Floating Car Data, Traffic sensors, Traffic paths, Travel behaviour, Vehicle density

Abstract. Floating car data (FCD) is becoming more and more relevant for mobility domain applications, overcoming issues derived by the use of physical sensors (e.g. inductive loops, video observation, infrared and laser vehicle detection etc.), such as limited geographical distribution, measure inhomogeneities, limited or null coverage of minor roads. An increasing number of vehicles are equipped with devices capable of acquiring GPS positions and other data, transmitted in almost real-time to traffic control centres. Based on FCD data, several traffic analysis in support to mobility services can be performed: vehicle density, speed, origin-destination matrices, different patterns in function of vehicle type. If currently the representativeness of FCD can be considered an issue, current growing trend in FCD penetration should naturally overcome this issue. FCD are also higher sensitive to traffic events (e.g. traffic jams) than model-based approaches.