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Classifying Vehicle Activity to Improve Point of Interest Extraction.

Authors :
Van Hinsbergh, James
Griffiths, Nathan
Taylor, Phillip
Xu, Zhou
Mouzakitis, Alex
Source :
Mobile Information Systems; 9/3/2021, p1-20, 20p
Publication Year :
2021

Abstract

Knowledge of drivers' mobility patterns is useful for enabling context-aware intelligent vehicle functionality, such as route suggestions, cabin preconditioning, and power management for electric vehicles. Such patterns are often described in terms of the Points of Interest (PoIs) visited by an individual. However, existing PoI extraction methods are general purpose and typically rely on detecting periods of low mobility, meaning that when they are applied to vehicle data, they often extract a large number of false PoIs (for example, incorrectly extracting PoIs due to stopping in traffic), reducing their usefulness. To reduce the number of false PoIs that are extracted, we propose using features derived from vehicle signals, such as the selected gear and status of doors, to classify candidate PoIs and filter out those that are irrelevant. In this paper, we (i) present Activity-based Vehicle PoI Extraction (AVPE), a wrapper method around existing PoI extraction methods, that utilizes a postclustering classification stage to filter out false PoIs, (ii) evaluate the benefits of AVPE compared to three state-of-the-art general purpose PoI extraction algorithms, and (iii) demonstrate the effectiveness of AVPE when applied to real-world driving data. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
ELECTRIC power management

Details

Language :
English
ISSN :
1574017X
Database :
Complementary Index
Journal :
Mobile Information Systems
Publication Type :
Academic Journal
Accession number :
152247810
Full Text :
https://doi.org/10.1155/2021/9973681