Attention-based Data Analytical Models for Traffic Flow Prediction

Accurate prediction of traffic flow is important for the successful deployment of smart transportation systems. It can potentially help relieve traffic congestion, make better travel decisions, reduce carbon emission, and improve traffic operation efficiency. Although numerous methods have been developed for traffic flow predictions, most of these methods are not capable of tackling long-time series because they are restricted by their short-term memories and do not have access to the entire time series while predicting the traffic flow. To address this issue, one goal of this project is to develop attention-based methodologies for traffic flow predictions. The proposed attention-based methods employ the attention mechanism that enables neural networks to have access to the entire long-time series while predicting so that gradient vanishing and limited memory problems brought by long-time series can be effectively addressed. Another goal of this project is to develop parallel attention-based traffic flow prediction methodologies. These methods employ the transformer network where identical self-attention layers are stacked for parallel computing. To demonstrate the effectiveness of the proposed model, this research uses the traffic flow prediction dataset from Kaggle that includes hourly traffic data on four different junctions. The proposed predictive models will enable a real-time and accurate traffic flow prediction and will help road users make better decisions to alleviate traffic congestion. The proposed methods can help road users make better travel decisions to avoid traffic congestion areas so that passenger and freight movements can be optimized to improve the mobility of people and goods. Moreover, they can also help reduce carbon emissions and reduce the risks of traffic incidents.

Language

• English

Project

• Status: Active
• Funding: $6361
• Contract Numbers:

  69A3551747127

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Performing Organizations:

  Mineta Consortium for Transportation Mobility

  San Jose State University
  San Jose, CA  United States  95112
• Principal Investigators:

  Wei , Yupeng

• Start Date: 20220101
• Expected Completion Date: 20221231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Data analysis; Forecasting; Neural networks; Traffic flow
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01829817
• Record Type: Research project
• Source Agency: Mineta Consortium for Transportation Mobility
• Contract Numbers: 69A3551747127
• Files: UTC, RIP
• Created Date: Dec 11 2021 8:36AM