Data Mining to Improve Traffic Safety

Details

• Creators:
  Smith, Randy K. ; Wang, Huanjing
• Corporate Contributors:
  University Transportation Center for Alabama
• Subject/TRT Terms:
  • Traffic safety
  • Data mining
  • Traffic crashes
• Publication/ Report Number:
  UTCA 04107
• Resource Type:
  Research Paper
• Geographical Coverage:
  Alabama
• TRIS Online Accession Number:
  1001007
• OCLC Number:
  60577287
• Edition:
  Final Report: 01/01/2004 - 12/31/2004
• Corporate Publisher:
  University Transportation Center for Alabama
• NTL Classification:
  NTL-SAFETY AND SECURITY-SAFETY AND SECURITY ; NTL-SAFETY AND SECURITY-Highway Safety
• Abstract:
  The ever increasing size of datasets used for data mining and machine learning applications has placed a renewed emphasis on algorithm performance and processing strategies. This research addresses algorithms for ranking variables in a dataset, as well as for ranking values of a specific variable. We propose two new techniques, called Max Gain (MG) and Sum Max Gain Ratio (SMGR), which are well-correlated with existing techniques, yet are much more intuitive. MG and SMGR were developed for the public safety domain using categorical traffic accident data. Unlike the typical abstract statistical techniques for ranking variables and values, the proposed techniques can be motivated as useful intuitive metrics for non-statistician practitioners in a particular domain. Additionally, the developed techniques are generally more efficient than the more traditional statistical approaches.
• Format:
  PDF
• Funding:
  DTSR0023424
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/39367/dot_39367_DS1.pdf
• File Type:
  [PDF - 502.70 KB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:ca6ccaf81a1f722862d6ba982d9129bf1ce4be2a8a5dbb9d936073d962b1da86