Bayesian Path Inference Using Sparse GPS Samples With Spatio-Temporal Constraints
第一作者：
Jun Kang
合写作者：
段沛博,Ke Yan
发表刊物：
IEEE Transactions on Intelligent Transportation Systems
DOI码：
10.1109/TITS.2021.3113710
摘要：
Path inference aims to reveal missing paths given a few number of GPS samples associated with a moving object by exploiting the topology of road network and statistical information of historical GPS trajectories, and plays a vital role in data preprocessing of location based information services. But, in practice path inference severely suffers from the data sparsity as well as the randomness of drivers path selection behaviors. In this paper, we propose a novel Bayesian path inference model subject to spatiotemporal constraints by taking into account the drivers path selection behaviors. To be specific, the problem of path inference is cast as the problem of searching K most probable candidate paths according to the joint posterior selection probabilities of candidate paths. When estimating model parameters, we use the frequency of each road segment in the historical GPS trajectories instead of that of road segment transfers to mitigate the influence of data sparsity. In addition, both spatiotemporal constraints and probability thresholds are introduced to narrow the search space, which significantly improves the time efficiency. The experiments are conducted using practical data and show that the proposed model is significantly superior to three existing popular models. When the GPS sampling interval varies from 1 minute to 5 minutes, the accuracy of the proposed method is 0.94, 0.91, 0.86, 0.80 and 0.74, and the Jaccard similarity 0.89, 0.85, 0.83, 0.80 and 0.75 respectively, the average improvement in accuracy rises from 3.68% to 18.69% and that in the Jaccard similarity from 4.56% to 18.42%
关键字：
Roads;Hidden Markov models;Global Positioning System;Trajectory;Data models;Spatiotemporal phenomena;Predictive models
页面范围：
1 - 13
是否译文：
否
