Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

Abstract

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.

Review and classification of trajectory summarisation algorithms: From compression to segmentation

“…The trivial algorithm of Tobler 130,131 can be seen as a window of fixed size. The same is true for the Pulshashi et al 54 with graphs, which generates k vertices for each point, where k is the size of the window.…”

“…Coresets, 34 AACAT, 30 SimpleTrack, 31 SGTCR-CS 27 Probabilistic IMM, [35][36][37] APSOS, 38 SAS, 32 SAOTS, 33 SGTCR-CS 27 Graph Distance Bellman, 39,40 DOTS, 41 DOTS-CASCADE, 41 Iri-Imai, 42,43 MRPA, 44 Daescu, 45,46 OGPC and OSPC, 47 MMTC-offline, 48 MMTC-online, 48 SPPA, 49 GRTSOpt, 50 Latecki, 51 Trajic, 52 Representativeness, 53 KAA and StreamKAA, 54 OLTS and OPTTS, 55 DOTS*, 56 OSC and OSTC, 28 CLEAN 57 Angle VTracer, 58 DPTS + , 59 Latecki, 51 61 GRPPA, 62 TSHL, 63 AMS, 16 CFF, 64 BOPW and NOPW, 4 OHTA, OnlineOHTA and SATA, 65 CDR, CDRm, GRTSOpt and GRTSSec, 50 TraClus, 66 OPERB and A-OPERB, 67 BQS, 68 ABQS, FBQS and PBQS, 69 LO-OPW-TR, 70 OPW-TR, 3 SMoT, 71 Pan, 72 Patroumpas,…”

“…Another more specific type of solution is proposed by Pulshashi et al, 54 this one is not so much focused on compression, but on generating segments by eliminating atypical trajectory points due to noise. To do this, the DAG it generates introduces, for each trajectory point k new vertices, to the next k nodes.…”

Spatial Data Science

“…Dealing with trajectory data continues to be a challenge; there are still many problems to tackle in order to be able to extract relevant and accurate knowledge from trajectory data. Pulshashi et al [21] propose an application to simplify trajectory data, for both batch and streaming environments, in their paper "Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments." The application seeks to reduce noise, and especially outlying point-locations that can mislead the analysis and alter the statistical properties of trajectories.…”