, Assistant Professor, Civil, Environmental and Geo-Engineering
John Hourdos, Former Research Associate Professor, Civil, Environmental and Geo-Engineering
This study evaluated the first and second implementations of the MN-QWARN queue warning algorithm developed by Hourdos et al. This algorithm was developed to detect specific crash-prone conditions created by traffic oscillations (shockwaves) on freeway systems. The MN-QWARN system was specifically calibrated for the freeway studied in Hourdos et al. and was moved to a new location with minimal calibration. This evaluation found that the right-side model had a detection rate of 25 percent and a false alarm rate of 36 percent. The left-side model had a detection rate of 64 percent and a false alarm rate of 23 percent. Researchers also noted high over-warning rates on both lanes. Based on these findings, the research team recommends recalibrating the MN-QWARN algorithm at this location to examine improvements in performance.
- Project number: 2019027
- Start date: 09/2018
- Project status: Completed
- Research area: Transportation Safety and Traffic Flow
Congestion, Data and modeling, Safety