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

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.