Incomplete Trajectory Recovery for Supporting Urban Link Travel Time Distribution Considering Spatial-Temporal Correlation

Travel time distribution (TTD) estimation on urban arterial links with sparse trajectory data is a practically important while substantially challenging subject. To this end, this paper makes full use of ubiquitous incomplete trajectories that only traverse part of the link and incorporates them with complete trajectories to alleviate the data sparsity problem. The focus of this study is to develop a framework of estimating link TTDs based on incomplete and complete trajectories. Three major steps are involved: first, we consider a simple trajectory imputation method for missing GPS points in order to improve the input data quality and serve as a basis for measuring the similarity between incomplete and complete trajectories; then, spatiotemporal K-nearest neighbor (KNN) algorithm is proposed to estimate virtual link travel times of incomplete trajectories for the purposes of increasing travel time sample size; finally, Bayesian estimation method via Gibbs sampling is introduced to approximate the posterior distribution of link travel times by incorporating historical trends and enhanced data. A case study was conducted on a major arterial in Nanjing, China. The results indicate that the proposed approach with the augmented data can achieve promising performance, compared to the baseline method in terms of effectiveness and adaptiveness.