Personalized Destination Prediction Using Transformers in a Contextless Data Setting
- Resource Type
- Conference
- Authors
- Tsiligkaridis, Athanasios; Zhang, Jing; Taguchi, Hiroshi; Nikovski, Daniel
- Source
- 2020 International Joint Conference on Neural Networks (IJCNN) Neural Networks (IJCNN), 2020 International Joint Conference on. :1-7 Jul, 2020
- Subject
- Bioengineering
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Trajectory
Predictive models
Hidden Markov models
Computer architecture
Task analysis
Public transportation
Decoding
destination prediction
transformer
deep learning
personalized prediction
probabilistic prediction
- Language
- ISSN
- 2161-4407
Destination prediction is an important task where the primary goal is to correctly predict a user’s destination given an input movement trajectory. Intelligent machine learning models that learn from observed movement data and can automatically forecast destinations from partial query trajectories are of high interest as they can provide a plethora of benefits to both creators and consumers in various markets. In this work, we present a novel framework for tackling the problem of destination prediction in a contextless data setting where we solely learn from trajectory coordinate information. We propose a Transformer model to predict destinations from partial trajectories and we demonstrate its use on two datasets from different domains, including a simulated indoor dataset and an outdoor taxi trajectory dataset. Our proposed method improves upon the previous state-of-the-art LSTM and BiLSTM deep learning approaches in terms of accuracy and distance from true destinations.