Employment of ATMS traffic control device data to assist in identification of crash-prone intersections

Research output: Contribution to journalArticle

Abstract

This paper employs information from the advanced traffic management system (ATMS) of Kaohsiung, Taiwan to help differentiate those crash-prone intersections by discriminant analysis. From the 25,604 records of 2005, 1977 crashes that occurred at 119 intersections with traffic exposure data were compiled to calibrate and validate the model. The road attributes of crash records, traffic control devices and movement exposure are the three types of data used as predicting variables. The correct ratios for model calibration and validation range from 78.33% to 67.80%. If traffic movements are removed, the correct ratios become slightly lowered to 76.67% to 66.10%. Research findings reveal that with or without inclusion of exposure data in identifying high crash-prone intersections for an urban environment does not make a significant difference. In addition, layout and traffic control devices could possibly explain about 66.10-78.33% of the possibility that an intersection will become a high crash intersection. It suggests that the developed approach could be a countermeasure for budget constraints and difficulties in continuation of exposure data collection, and the information of ATMS could help identify crash-prone urban intersections.

Original languageEnglish
Pages (from-to)32-43
Number of pages12
JournalIATSS Research
Volume32
Issue number1
DOIs
Publication statusPublished - 2008 Jan 1

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Advanced traffic management systems
traffic management system
traffic management
Traffic control
traffic control
traffic
discriminant analysis
layout
Taiwan
budget
inclusion
road
Discriminant analysis
calibration
exposure
Calibration

All Science Journal Classification (ASJC) codes

  • Transportation
  • Safety Research
  • Urban Studies
  • Engineering(all)

Cite this

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abstract = "This paper employs information from the advanced traffic management system (ATMS) of Kaohsiung, Taiwan to help differentiate those crash-prone intersections by discriminant analysis. From the 25,604 records of 2005, 1977 crashes that occurred at 119 intersections with traffic exposure data were compiled to calibrate and validate the model. The road attributes of crash records, traffic control devices and movement exposure are the three types of data used as predicting variables. The correct ratios for model calibration and validation range from 78.33{\%} to 67.80{\%}. If traffic movements are removed, the correct ratios become slightly lowered to 76.67{\%} to 66.10{\%}. Research findings reveal that with or without inclusion of exposure data in identifying high crash-prone intersections for an urban environment does not make a significant difference. In addition, layout and traffic control devices could possibly explain about 66.10-78.33{\%} of the possibility that an intersection will become a high crash intersection. It suggests that the developed approach could be a countermeasure for budget constraints and difficulties in continuation of exposure data collection, and the information of ATMS could help identify crash-prone urban intersections.",
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Employment of ATMS traffic control device data to assist in identification of crash-prone intersections. / Hwang, Kevin-P.

In: IATSS Research, Vol. 32, No. 1, 01.01.2008, p. 32-43.

Research output: Contribution to journalArticle

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