Data Analytics and Pattern Recognition Methods for Work Zone Simulator StudiesAuthor/Presenter: Moradpour, Samareh; Long, Suzanna; Qin, Ruwen; Konur, Dincer; Leu, Ming
This research presents a driving simulator based study to evaluate a driver’s response to alternate work zone sign configurations. This study has compared the Manual on Uniform Traffic Control Devices (MUTCD) configurations against Missouri Department of Transportation (MoDOT) alternate configurations. Study participants within target populations, chosen to represent a range of Missouri drivers, have attempted four work zone scenarios as part of a driving simulator experience. The test scenarios simulated both right and left work zone lane closures with both the CLM and MoDOT alternatives. Drivers’ merging patterns were measured against demographic characteristics of test populations. Statistical data analysis was used to investigate the effectiveness of the alternate configurations employed under different scenarios. The results of this simulation study were compared to the results from a previous MoDOT field study. Pattern recognition and data analytics suggest a correlation between age and gender with the location of merging for the simulated scenarios. Based on results it is observed that the drivers merge earlier with the MoDOT alternative sign than they do with the MUTCD sign. This suggests that MoDOT alternative sign is safer. Also, this observation is in line with the observations of Field study.