Traffic Flow Velocity Prediction Based on Real Data LSTM Model

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.

• Record URL:
  https://doi.org/10.4271/2021-01-7014
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01487191
• Supplemental Notes:
  • Abstract reprinted with permission of SAE International.
• Authors:
  • Wang, Jiaze
  • Li, Lin
• Conference:
  • Vehicle Electrification and Powertrain Diversification Technology Forum Part I
  • Location: Shanghai , China
  • Date: 2021-11-25 to 2021-11-26
• Publication Date: 2021-12-31

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Serial:
  • SAE Technical Paper
  • Publisher: Society of Automotive Engineers (SAE)
  • ISSN: 0148-7191
  • EISSN: 2688-3627
  • Serial URL: http://papers.sae.org/

Subject/Index Terms

• TRT Terms: Data; Energy conservation; Highway traffic control; Hybrid vehicles; Machine learning; Mathematical models; Neural networks
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01832534
• Record Type: Publication
• Source Agency: SAE International
• Report/Paper Numbers: 2021-01-7014
• Files: TRIS, SAE
• Created Date: Jan 13 2022 2:04PM