Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A comparative study of the application of the standard kernel density estimation and network kernel density estimation in crash hotspot identification
University of Massachusetts Amherst.
University of Massachusetts Amherst.
University of Massachusetts Amherst.
2013 (English)In: Proceedings of the 16th International Conference Road Safety on Four Continents: Beijing, China. 15-17 May 2013, Linköping: Statens väg- och transportforskningsinstitut, 2013Conference paper, Published paper (Other academic)
Abstract [en]

Despite a growing number of studies have claimed the network Kernel Density Estimation (network KDE) a more advanced method for crash hotspot identification than the planar Kernel Density Estimation (planar KDE), few conducted comprehensive study to examine their accuracy and practicality on a large-scale basis (i.e. municipal and county). This research attempted to fill the gap by conducting a comparative study of planar KDE and network KDE using the crash data of Hampden County, Massachusetts from 2009 to 2011. A two-tier planar KDE and a network KDE were implemented using the Kernel Density tool in ESRI ArcGIS 10 and SANET 4.1 developed at University of Tokyo respectively. Results showed that (1) Planar KDE is computationally inexpensive and easily accessed. (2) Both methods yielded virtually similar hotspot patterns but with different rankings of the high crash locations. (3) In identifying specific hotspot locations, network KDE could achieve more accurate results and was more timesaving, although multiple runs of planar KDE identified specific locations as well. Accordingly, several suggestions were made for crash hotspot analysis: (1) Since KDE takes the interrelationship among crashes into consideration, it is a more statistically sound approach than traditional methods in crash hotspot identification and can be widely adopted by state and local agencies for initiating safety improvement projects. (2) Planar KDE is recommended to identify general hotspot patterns on large-scale basis for its practicality and efficiency. (3) Network KDE is recommended to identify specific intersections and roadway segments for accuracy.

Place, publisher, year, edition, pages
Linköping: Statens väg- och transportforskningsinstitut, 2013.
Keyword [en]
Accident black spot, Method, Mathematical model, Road network, Analysis (math)
National Category
Infrastructure Engineering
Research subject
X RSXC; 80 Road: Traffic safety and accidents, 812 Road: Collation of accident statistics; 80 Road: Traffic safety and accidents, 82 Road: Geometric design and traffic safety
Identifiers
URN: urn:nbn:se:vti:diva-7401OAI: oai:DiVA.org:vti-7401DiVA: diva2:761250
Conference
16th International Conference Road Safety on Four Continents. Beijing, China (RS4C 2013). 15-17 May 2013
Available from: 2014-11-06 Created: 2014-11-06 Last updated: 2014-11-06Bibliographically approved

Open Access in DiVA

fulltext(904 kB)425 downloads
File information
File name FULLTEXT01.pdfFile size 904 kBChecksum SHA-512
adfe5327cb440f07299e4a55f821632ab2f67743d03ae12b4c4f3a4aaad9f2dc16e1dce19c96a5b05c852ee72aeb43e3548a29ac7cbe5329ae8f9823ab877507
Type fulltextMimetype application/pdf

Infrastructure Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 425 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 179 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf