Road network functional hierarchy classifies individual roads into several levels, for efficient traffic management and road network generalization purposes. Automatic and semi-automatic road network extraction methods exist, but the generated products normally lack information on its functional hierarchy. This paper presents a methodology for automatically retrieve functional hierarchy for an OpenStreetMap derived road network from Floating Car Data, obtaining evenly distributed (e.g. for generalization purposes) or dynamic (e.g. to take into account differences in traffic volumes in different moments of the day) classifications. Road network elements are classified in function of vehicle speed values: the class distribution generated with the proposed methodology follows a linear distribution that can be better exploited for generalization purposes. Furthermore, the methodology allows to clearly distinguish different distributions in different moments of the day and days of the week, supporting traffic management activities.

DEFINITION OF A METHODOLOGY TO DERIVE ROAD NETWORK FUNCTIONAL HIERARCHY CLASSES USING CAR TRACKING DATA / Ajmar, A.; Arco, E.; Boccardo, P.. - In: INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES. - ISSN 2194-9034. - ELETTRONICO. - XLIII-B4-2020:(2020), pp. 307-312. [10.5194/isprs-archives-XLIII-B4-2020-307-2020]

DEFINITION OF A METHODOLOGY TO DERIVE ROAD NETWORK FUNCTIONAL HIERARCHY CLASSES USING CAR TRACKING DATA

Ajmar, A.;Arco, E.;Boccardo, P.
2020

Abstract

Road network functional hierarchy classifies individual roads into several levels, for efficient traffic management and road network generalization purposes. Automatic and semi-automatic road network extraction methods exist, but the generated products normally lack information on its functional hierarchy. This paper presents a methodology for automatically retrieve functional hierarchy for an OpenStreetMap derived road network from Floating Car Data, obtaining evenly distributed (e.g. for generalization purposes) or dynamic (e.g. to take into account differences in traffic volumes in different moments of the day) classifications. Road network elements are classified in function of vehicle speed values: the class distribution generated with the proposed methodology follows a linear distribution that can be better exploited for generalization purposes. Furthermore, the methodology allows to clearly distinguish different distributions in different moments of the day and days of the week, supporting traffic management activities.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2843292