Verification method to improve the efficiency of traffic survey

Abstract

Road traffic volume survey is a survey to determine the number and type of vehicles passing at a specific point for a certain period of time. Previously, a method of classifying the number of vehicles and vehicle types has been used while a person sees an image photographed using a camera with the naked eye, but this has a disadvantage in that a lot of manpower and cost are incurred. Recently, a method of applying an automated algorithm has been widely attempted, but has a disadvantage in that the accuracy is inferior to the existing method performed by manpower. To address these problems, we propose a method to automate road traffic volume surveys and a new method to verify the results. The proposed method extracts the number of vehicles and vehicle types from an image using deep learning, analyzes the results, and automatically informs the user of candidates with a high probability of error, so that highly reliable traffic volume survey information can be efficiently generated. The performance of the proposed method is tested using a data set collected by an actual road traffic survey company. The experiment proved that it is possible to verify the vehicle classification and route simply and quickly using the proposed method. The proposed method can not only reduce the investigation process and cost, but also increase the reliability due to more accurate results.