Tablet-Based Traffic Counting Application Designed to Minimize Human Error

Basic traffic counts are among the key elements in transportation planning and forecasting. As emerging data collection technologies proliferate, the availability of traffic count data will expand by orders of magnitude. However, availability of data does not always guarantee data accuracy, and it is essential that observed data are compared with ground truth data. Little research or guidance is available that ensures the quality of ground truth data with which the count results of automated technologies can be compared. To address the issue of ground truth data based on manual counts, a manual traffic counting application was developed for an Android tablet. Unlike other manual count applications, this application allows data collectors to replay and toggle through the video in supervisory mode to review and correct counts made in the first pass. For system verification, the review function of the application was used to count and recount freeway traffic in videos from the Atlanta, Georgia, metropolitan area. Initial counts and reviewed counts were compared, and improvements in count accuracy were assessed. The results indicated the benefit of the review process and suggested that this application could minimize human error and provide more accurate ground truth traffic count data for use in transportation planning applications and for model verification.