Scooter-Share Travel Demand Forecast: A Context-Aware LSTM Recurrent Neural Network Approach

Shared micromobility has been popular in many cities in the U.S. The rise of shared micromobility brings significant operational challenges such as fleet management and demand forecasting. This project develops a Context-Aware Long Short-Term Memory (CALSTM) recurrent neural network to enhance the prediction of daily travel demand for scooter-sharing in Austin, Texas. The CALSTM model boosts prediction accuracy by integrating the impact of nearby points-of-interest (POIs) and daily weather conditions on scooter usage. It processes historical scooter-sharing demand and weather information through separate LSTM modules to extract temporal information. The outputs from these modules are combined through element-wise multiplication to establish temporal dependencies. Additionally, POI information is analyzed using a Multi-Layer Perceptron (MLP) to capture spatial dependencies. These spatial and temporal dependencies are then integrated by another MLP module to produce the forecast outputs. Case study experiments in Austin, TX, demonstrated that the CALSTM model significantly outperformed benchmark models, achieving improvements of 28% in Mean Absolute Error (MAE) and 19% in Root Mean Squared Error (RMSE) over traditional LSTM models. These results offer valuable insights for transportation planning and the enhancement of shared micromobility in urban settings.

Language

• English

Project

• Status: Active
• Funding: $68,300.00
• Contract Numbers:

  69A3551747135

• 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
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  Cooperative Mobility for Competitive Megaregions (CM2)

  University of Texas at Austin
  Austin, TX  United States  78712
• Principal Investigators:

  Jiao, Junfeng

• Start Date: 20200901
• Expected Completion Date: 20210930
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Forecasting; Micromobility; Neural networks; Scooters; Shared mobility; Spatial analysis; Travel demand; Vehicle sharing; Weather conditions
• Geographic Terms: Austin (Texas)
• Subject Areas: Highways; Pedestrians and Bicyclists; Planning and Forecasting;

Filing Info

• Accession Number: 01937932
• Record Type: Research project
• Source Agency: Cooperative Mobility for Competitive Megaregions (CM2)
• Contract Numbers: 69A3551747135
• Files: UTC, RIP
• Created Date: Nov 23 2024 11:10AM