Detection of High-risk Segments of Traffic Incidents on Freeway Networks by Multi-Kernel Density Estimation and Spatial Analysis

Traffic incidents on freeways cause a considerable loss of life and property. Some traffic operation organizations provide freeway safety services to improve the roadway’s safety condition by assisting in the detection and clearance of incidents. To offer assistance in time, the traffic operation centers generally use patrol vehicles to cover the freeway networks. However, the risk of an incident occurring may differ considerably among road segments in the networks. By giving these high-risk segments more resources, the efficiency of the freeway safety service may increase. Therefore, it is essential to recognize the road segments having higher incident risks among the whole freeway network. This research aims at providing a method of detecting the road segments with a higher risk of traffic incidents. The risk will be considered from both spatial and temporal factors by implementing the multi-kernel density estimation method. The statistics of spatial analysis will be applied to evaluate the detection results.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 14p

Subject/Index Terms

Filing Info

  • Accession Number: 01764337
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-04272
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:26AM