title: Intelligent geocoding system to locate traffic crashes.
abstract: State agencies continue to face many challenges associated with new federal crash safety and highway performance monitoring requirements that use data from multiple and disparate systems across different platforms and locations. On a national level, the federal government has a long-term vision for State Departments of Transportation (DOTs) to report state route and off-state route crash data in a single network. In general, crashes occurring on state-owned or state maintained highways are a priority at the Federal and State level; therefore, state-route crashes are being geocoded by state DOTs. On the other hand, crashes occurring on off-state highway system do not always get geocoded due to limited resources and techniques. Creating and maintaining a statewide crash geographic information systems (GIS) map with state route and non-state route crashes is a complicated and expensive task. This study introduces an automatic crash mapping process, Crash-Mapping Automation Tool (C-MAT), where an algorithm translates location information from a police report crash record to a geospatial map and creates a pinpoint map for all crashes. The algorithm has approximate 83 percent mapping rate. An important application of this work is the ability to associate the mapped crash records to underlying business data, such as roadway inventory and traffic volumes. The integrated crash map is the foundation for effective and efficient crash analyzes to prevent highway crashes.
authors.affiliation: Department of Civil and Environmental Engineering, South Dakota State University, Brookings, SD 57007, United States. Xiao.Qin@sdstate.edu
authors.forename: Xiao
authors.initials: X
authors.lastname: Qin
authors.forename: Steven
authors.initials: S
authors.lastname: Parker
authors.forename: Yi
authors.initials: Y
authors.lastname: Liu
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Graettinger
authors.forename: Susie
authors.initials: S
authors.lastname: Forde
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000465:Algorithms; D001334:Automobile Driving; D062305:Geographic Mapping; D006801:Humans; D011786:Quality Control; D018570:Risk Assessment; D014922:Wisconsin