TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks

TL;DR

TrajectoryNet introduces a neural network architecture that effectively classifies human transportation modes from GPS traces by embedding features into a richer space, achieving high accuracy without extra sensors.

Contribution

The paper presents a novel GPS trajectory embedding method combined with RNNs, significantly improving transportation mode classification accuracy over existing models.

Findings

Achieved over 98% accuracy in classifying four transportation modes

Developed a new trajectory embedding technique for better feature representation

Outperformed existing models without additional sensory data

Abstract

Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.