Interpretable Learning for Travel Behaviours in Cyber-Physical-Social-Systems
- Resource Type
- Conference
- Authors
- Qi, Hao; Ye, Peijun
- Source
- 2022 Australian & New Zealand Control Conference (ANZCC) Control Conference (ANZCC), 2022 Australian & New Zealand. :182-187 Nov, 2022
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Knowledge engineering
Deep learning
Scalability
Perturbation methods
Neurons
Transportation
Transforms
Deep Learning
Interpretability
Cyber-Physical-Social-System
Travel Behavior
- Language
- ISSN
- 2767-7257
Interpretable learning is important for understanding human behavioral patterns in Cyber-Physical-Social-Systems (CPSS). It facilitates smart decision-makings of intelligent algorithms so that the management of such human-machine hybrid systems can be efficient and optimal. Unlike the big data driven transportation management, this paper introduces a new interpretable learning method using fuzzy logic to semantically extract travel behaviors. Computational experiments based on actual traffic data indicate that our method is able to generate explicit rules, and these rules can be used to predict traffic patterns very well.