Traffic Flow Velocity Prediction Based on Real Data LSTM Model Northeast Forestry University

Format:

Book

Conference/Event

Author/Creator:

Wang, Jiaze, author.

Contributor:

Li, Lin

Conference Name:

Vehicle Electrification and Powertrain Diversification Technology Forum Part I (2021-11-25 : Shanghai, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

In order to improve the energy efficiency of hybrid electric vehicles and to improve the effectiveness of energy management algorithms, it is very important to predict the future changes of traffic parameters based on traffic big data, so as to predict the future vehicle speed change and to reduce the friction brake. Under the framework of deep learning, this paper establishes a Long Short-Term Memory (LSTM) artificial neural network traffic flow parameter prediction model based on time series through keras library to predict the future state of traffic flow. The comparison experiment between Long Short-Term Memory (LSTM) artificial neural network model and Gate Recurrent Unit (GRU) model using US-101 data set shows that LSTM has higher accuracy in predicting traffic flow velocity

Notes:

Vendor supplied data

Publisher Number:

2021-01-7014

Access Restriction:

Restricted for use by site license