An Energy-Efficient Method with Dynamic GPS Sampling Rate for Transport Mode Detection and Trip Reconstruction

Abstract

This paper presents a novel approach for trip reconstruction and transport mode detection. While traditional methods use a fixed GPS sampling rate, our proposed method uses a dynamic rate to avoid unnecessary sensing and waste of energy. We determine a time for each sampling that gives an interesting trade-off using a particle filter. Our approach uses as input a map, including transit network circuits and schedules, and produces as output the estimated road segments and transport modes used. The effectiveness of our approach is shown empirically using real map and transit network data. Our technique achieves an accuracy of 96.3% for a 15.0% energy consumption reduction (compared to the existing technique that has the closest accuracy) and an accuracy of 85.6% for a 56.0% energy consumption reduction.

Publication
Advances in Artificial Intelligence (Canadian AI 2020)
Jaël Champagne Gareau
Jaël Champagne Gareau
PhD Student of Computer Science

My research interests include AI (both planning and machine learning) and theoretical computer science.