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.
Type
Publication
Advances in Artificial Intelligence (Canadian AI 2020)
Authors
Authors
Postdoctoral Researcher in Computer Science
I am currently a postdoctoral researcher in computer science at Université
TÉLUQ, where my research focuses on speeding up the conversion of integer and
floating-point numbers into decimal strings. During my doctoral studies, I
designed algorithms and data structures that leverage modern computer
architectures to solve large instances of Markov decision processes (MDPs). In
my master’s research, I developed routing algorithms for electric vehicles
aimed at determining the optimal path between two points while minimizing
travel time (including driving, charging, and expected waiting time at
charging stations).
Authors