Anomaly detection for non-recurring traffic congestions using Long short-term memory networks (LSTMs)

Anomaly detection for non-recurring traffic congestions using Long short-term memory networks (LSTMs)

In this master thesis, we implement a two-step anomaly detection mechanism for non-recurrent traffic congestions with data collected from public transport buses in Stockholm. We investigate the use of machine learning to model time series data with LSTMs and evaluate the results with a baseline pred...

Full description

Bibliographic Details
Main Author: Svanberg, John
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2018
Subjects:
ML
machine learning
neural networks
recurrent neural networks
NN
RNN
LSTM
long short-term memory networks
anomaly detection
public transport
big data
data analysis
Computer Sciences
Datavetenskap (datalogi)
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234465

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234465

Similar Items