Deep-Learning Traffic Flow Prediction for Forecasting Performance Measurement of Public Transportation Systems

Details

• Creators:
  Shahabi, Cyrus ; Tran, Luan ; Mun, Min ; Chiang, Yao Yi
• Corporate Creators:
  University of Southern California ; Pacific Southwest Region 9 UTC, University of Southern California
• Corporate Contributors:
  California. Dept. of Transportation. Division of Research, Innovation, and System Information ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Bus transit
  • Forecasting
  • Machine learning
  • Neural networks
  • Passenger transportation
  • Public transit
  • Traffic analysis zones
  • Traffic estimation
  • Traffic flow
  • Traffic flow rate
  More +
• Publication/ Report Number:
  PSR-18-10
• Resource Type:
  Tech Report
• Geographical Coverage:
  Los Angeles (California);United States
• Corporate Publisher:
  California. Dept. of Transportation. Division of Research and Innovation
• Abstract:
  In this project, we developed a deep learning approach for traffic flow forecasting and bus arrival time estimation in Los Angeles. First, we developed a novel Graph Convolutional Recurrent Neural Network (GCRNN) to model and forecast traffic flows at different spatial and temporal resolutions. Our GCRNN model considers not only the location of traffic sensors but also their relationships (i.e., topological dependency) in space, which was critical to achieving the best performance for all forecasting horizons compared to the existing methods. Next, we implemented a Geo-Convolution Long Short-Term Memory (Geo-Conv LSTM) framework to model bus Estimated Time of Arrival (ETA) by incorporating the traffic flow predictions of our GCRNN. Using the real-world traffic sensor datasets archived in our data warehouse, we showed that our proposed bus ETA model is more accurate than the existing method, Gradient Boosted Decision Tree (GBDT), by 27% in estimating bus travel time. Lastly, we deployed both models as web applications so that users can access traffic prediction data and check bus arrival times to a destination location from a starting point.
• Format:
  PDF
• Funding:
  69A3551747109 Caltrans Grant 65A0674, Task Order 001
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/54424/dot_54424_DS1.pdf
• File Type:
  [PDF - 2.25 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:0122628d3ca474c820a25661ddc8a9e89806ca3b4687b92ec19dd5402dffef4a2568f471f0fb842e345eb98090220a8ff15d08ade5df27f26a318b0b3f36c3ae