Model-based methodology for validation of traffic flow detectors by minimizing human bias in video data processing

This paper provides a model-based method for analysis and hypothesis testing for paired data where one source of data has to be validated against another source of data that contains subjective and dynamic errors. This study deals with human-observed flow counts collected from traffic videos of freeway cameras. The available videos are mainly used for the purpose of manual observation by transportation personnel in case of emergency. This amounts to a varying inconsistency of the quality of the videos, which presents an additional challenge when analyzing the data. Video processing cannot be performed due to the mentioned issues with regard to the video quality. The processing has to be manually performed by humans who unfortunately have an inherent bias. If the video data have to be used for validating flow detector sensors, then a technique that performs validation with subjective and dynamic erroneous data as a result of the human bias is needed. This paper presents a methodology to deal with this issue. It is based on statistical testing with heteroscedasticity, which is demonstrated through a case study using data from traffic flow detectors and traffic cameras installed on highways in the Southern Nevada Region. A model for the relationship between the video ratings and the distribution of the human errors is developed taking into consideration the human bias. A method for identification of faulty detectors is also demonstrated based on the developed technique