Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes

Details

• Creators:
  Fickas, Stephen
• Corporate Creators:
  University of Oregon
• Corporate Contributors:
  National Institute for Transportation and Communities (NITC) ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Advanced traveler information systems
  • Cyclists
  • Machine learning
  • Mobile applications
  • Signalized intersections
  • Bicyclists
  • Actuated signals
  • Time-series forecasting
  • Precision-recall tradeoff
  • Green wave phone app
• Publication/ Report Number:
  NITC-RR-1299
• DOI:
  https://dx.doi.org/10.15760/trec.264
• Resource Type:
  Tech Report
• Geographical Coverage:
  Oregon ; United States
• Edition:
  Final Report
• Corporate Publisher:
  National Institute for Transportation and Communities (NITC)
• Abstract:
  This project focuses on giving bicyclists a safer and more efficient path through a city's signalized intersections. It builds on a prior NITC project that tested an app for a fixed-time corridor. The goal of this project is to lay the groundwork for extending this earlier app to include actuated signals. Two machine-learning algorithms are introduced that have a good track record with time-series forecasting: LSTM and 1D CNN. The algorithms are tested on data captured from a busy bike corridor on the south end of the University of Oregon campus. A specific actuated intersection is identified on this corridor and real-time data is collected from it. The algorithms are trained on the data and evaluated. The results show that both algorithms can reach 85% accuracy and can predict on a single sample within roughly one second. While these results are encouraging in terms of adding a prediction component to the existing app, a closer look at Precision and Recall is more mixed. A means of computing a Precision-Recall tradeoff is discussed.
• Format:
  PDF
• Funding:
  NITC 1299
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/60041/dot_60041_DS1.pdf
• File Type:
  [PDF - 1.87 MB]
• Collection(s):
  Artificial Intelligence Deployment Center of Excellence (AI COE) ; University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:25a276025db643c50367b3176475412ed02b8bce9b25072d14506766f7c5b12a77952d9e056b90fa0b30e77a19b0748fc6f03126fe31ecd35fbc7ac929f54dd5