A Novel Filtering Method of Travel-Time Outliers Extracted from Large-Scale Traffic Checkpoint Data

Abstract

The traffic checkpoint data collected by video detection and license plate recognition technologies contain abundant traffic information such as vehicle travel time and path. However, due to various factors, there are often many outliers in the travel-time data, which may lead to inaccurate research results. How to extract effective travel-time data from the traffic checkpoint data is one of the key questions for urban traffic evolution. Based on the large-scale checkpoint data from a city in Guangdong Province, China, this study proposes a statistics-based filtering algorithm for travel-time outliers and applies it to urban vehicle travel path and origin-destination (OD) identification. The sampling rate and reading rate of checkpoint data are verified. The sampling rate and reading rate are 99.46% and 93.7%, respectively, and the reliability of the data is high. A statistics-based filtering algorithm for travel-time outliers is proposed and compared with three existing clustering algorithms. The results showed that for a load section of 376.54 m, the maximum travel time is reached during the period 18:00–18:15. The average travel time is 5.55 min, and the maximum travel time is 8.08 min, which is consistent with the actual operation of the road section. The travel time obtained by the three clustering methods does not reflect the actual operation of the road section well. This means that the effective travel time extracted by this method is more practical and can reflect the fluctuation of travel time on urban roads. Then, the sensitivity of the proposed method to the time window and road length is tested. The results showed that the time window setting will affect the outlier-filtering effect, and the road length has less effect on the proposed method. Finally, the proposed algorithm is applied to single travel identification of urban vehicles to extract single travel paths and OD information of vehicles, which can provide accurate data for urban traffic research.