This work addresses non-stationary traffic flow prediction by implementing an intelligent update scheme to deep neural networks. The intelligent update scheme works by monitoring the frequency domain features extracted from the traffic flow time series. The features at present are compared with the previous ones through a distance function. The resulting similarity is then fed to the exponentially weighted moving average to detect whether a notable change in the traffic flow is present or not. It has been shown in the experiments that the proposed method is able to handle the non-stationarity and produce acceptable traffic flow prediction. Moreover, the proposed method performance is comparable to the fully stochastic gradient descent update scheme while saving computational and time resources up to around 13%.
Non-Stationary Traffic Flow Prediction Using Deep Learning
01.08.2018
583735 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Short-term traffic flow prediction using deep learning
SLUB | 2023
|Distributed Multi-Intersection Traffic Flow Prediction using Deep Learning
DOAJ | 2024
|Deep Learning for Short-Term Traffic Flow Prediction
Online Contents | 2016
|Traffic Flow Prediction Model Based on Deep Learning
British Library Conference Proceedings | 2022
|