Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Abstract

Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, effectively and efficiently discovering the specific gathering pattern turns to be a remaining challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a moving object gathering pattern retrieving method that aims to support the retrieving of gathering patterns based on spatio-temporal graph. In this method, firstly the authors use an improved R-tree based density clustering algorithm (RT-DBScan) to index the moving objects and collect clusters. Then, they maintain a spatio-temporal graph rather than storing the spatial coordinates to obtain the spatio-temporal changes in real time. Finally, a gathering retrieving algorithm is developed by searching the maximal complete graphs which meet the spatio-temporal constraints. To the best of their knowledge, effectiveness and efficiency of the proposed methods are outperformed other methods on both real and large trajectory data.

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

“…This approach is more suitable for application to group events such as parades instead of traffic congestion pattern. In order to effectively and efficiently address a high volume of trajectory data, Zhang et al [26] proposed a moving-object gathering pattern retrieval method based on spatio-temporal graphs. However, the performance of the retrieval method greatly depends on the construction of the spatiotemporal graph.…”

Approach to discovering companion patterns based on traffic data stream

“…Zhang et al focused on effectively and efficiently discovering the gathering patterns from the high volume of trajectory data. They utilised an improved R‐tree based density clustering algorithm to index moving objects and clusters and a spatio‐temporal graph to retrieve patterns [22, 23]. Xian et al paid attention to both batch and streaming fashion of gathering patterns parallel discovery [24].…”

Discovering Gathering Pattern Using a Taxicab Service Rate Analysis Method based on Neural Network

“…The recently and mostly related works to our method is gathering pattern discovery such as Crowd-TAD (Zheng et al, 2013) and GR+ (Zhang JM, 2016).In the first experiment, we use three metrics: precision, recall and F-measure. The first experiment is shown in Figure 11, the performance of NNGD is about the same with CROWD-TAD, but a little higher than GR+.…”

“…Specifically, it uses the spatial Gird to index the crowd of moving objects, and then uses the Crowd-TAD method to test each crowd to discover the gathering pattern. To improve the effectiveness and efficiency of the gathering pattern retrieving process, we have also proposed a gathering retrieving method based on spatio-temporal graph that forms by moving object clusters (Zhang JM, 2016). The main part of the method is to find the maximal complete graph that meets the spatio-temporal constraints by indexing the graph.…”