Artificial Intelligence

    

A Traffic Prediction Using Machine Learning: Literature Survey

Authors: Ji Yoon Kim

Accurate calculation of the commute cost is crucial for the government to decide whether housing subsidy will be provided to disadvantaged workers, or to create a new method that can reduce the commute cost of the disadvantaged workers by offering mass transit. Many studies have already proven that machine learning can predict traffic and commute times. Although different machine learning algorithms can be used, this study mainly uses Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which are based on the Recurrent Neural Networks (RNNs) architecture.

Comments: 4 Pages.

Download: PDF

Submission history

[v1] 2021-12-02 03:27:08

Unique-IP document downloads: 344 times

Vixra.org is a pre-print repository rather than a journal. Articles hosted may not yet have been verified by peer-review and should be treated as preliminary. In particular, anything that appears to include financial or legal advice or proposed medical treatments should be treated with due caution. Vixra.org will not be responsible for any consequences of actions that result from any form of use of any documents on this website.

Add your own feedback and questions here:
You are equally welcome to be positive or negative about any paper but please be polite. If you are being critical you must mention at least one specific error, otherwise your comment will be deleted as unhelpful.

comments powered by Disqus