COMBINED METHOD OF LSTM AND RANDOM FOREST FOR TRAFFIC JAM PREDICTION
Traffic jam is a crucial issue in several cities in Indonesia, including cities in West Java province. Traffic jams cause many losses such as lost time, lack of productivity, and increased stress for the driver, and that will have an impact on the next activity of the day. Traffic jams are caused...
Saved in:
Main Author: | Arfina, Ayuni |
---|---|
Format: | Theses |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/85301 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Similar Items
-
MACHINE LEARNING USING A COMBINED ARIMA MODEL AND LSTM FOR TRAFFIC DENSITY PREDICTION
by: Rahmi Maulida, Nabila -
Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)
by: Zhang, Ying, et al.
Published: (2024) -
Predicting metabolic syndrome using the random forest method
by: Apilak Worachartcheewan, et al.
Published: (2018) -
Predicting Metabolic Syndrome Using the Random Forest Method
by: Apilak Worachartcheewan, et al.
Published: (2015) -
Mechanism of traffic jams at speed bottlenecks
by: Quek, Wei Liang, et al.
Published: (2014)