Improving Transportation Information Resilience: Error Estimation for Networked Sensor Data [supporting datasets]

Details

• Alternative Title:
  NCST Caltrans Project on Sensor Data Error Estimation
• Creators:
  Fan, Yueyue ; Yang, Han ; Maheshwari, Saurabh ; Yang, Yudi
• Corporate Contributors:
  University of California, Davis. Institute of Transportation Studies
• Subject/TRT Terms:
  • Data analysis
  • Data quality
  • Loop detectors
  • Remote sensing
  • Traffic data
  • Traffic estimation
  • Traffic models
  • Error analysis Sensor health
  • error estimation
  • information resilience
  More +
• Publication/ Report Number:
  NCST-UCD-RR-20-18
• DOI:
  https://doi.org/10.25338/B8TP5Q
• Resource Type:
  Dataset
• Geographical Coverage:
  United States
• Corporate Publisher:
  California. Department of Transportation
• Abstract:
  The aim of this script is to automate the process of directed network graph formation, i.e., creation of incidence matrix, node adjacency matrix, and map the sensors to appropriate links. The data used for creating the freeway network is obtained from open street maps, whereas, the data for sensors is obtained from PeMS. The results of the algorithm will later be fed to the network sensor error estimation algorithm, that quantifies the erroneous sensors using network statistics.
• Format:
  ZIP
• Funding:
  69A3551747114
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/53918/dot_53918_DS1.pdf
• File Type:
  [PDF - 131.44 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:3bcabf158bec22e2f1f576047fb383148b824f35cd99bfdb57740347412784ed3e2d6653c3b7b950fa9459578e8f03b0f1db76633b0dd86c7a8d44ba785145de