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.

中文翻译：

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利用轨迹数据估算旅行时间的生成对抗网络

出行时间的知识在交通控制和管理中起着重要的作用。作为越来越流行的数据源，车辆轨迹可以提供大规模的旅行时间信息。但是，从稀疏或低分辨率轨迹数据中提取的实际旅行时间信息通常包含缺少的数据，需要进行估算以进行进一步的交通分析。因此，本研究提出了用于旅行时间归因的旅行时间归因生成对抗网络（TTI-GAN）。考虑到整个网络的时空相关性，TTI‐GAN可以通过对具有丰富数据的链接的传播时间分布（TTD）建模，从而在没有足够观察力的情况下生成链接的传播时间。然后，利用滴滴出行的轨迹数据进行了数值实验。