A generative adversarial network for travel times imputation using trajectory data

Abstract

Knowledge of travel times serves an important role in traffic control and management. As an increasingly popular data source, vehicle trajectories can provide large-scale travel time information. However, real-world travel time information extracted from sparse or low-resolution trajectory data often contains missing data that need to be imputed for further traffic analysis. Thus, this study proposes a travel times imputation generative adversarial network (TTI-GAN) for travel times imputation. Considering the network-wide spatiotemporal correlations, the TTI-GAN can generate travel times for links without sufficient observations by modeling travel time distributions (TTDs) for links with rich data. Then, numerical experiments are carried out with trajectory data from Didi Chuxing. The results show that the TTI-GAN can well estimate link TTDs and performs better than other counterparts in imputing mean travel times under various data missing rates.