An Asynchronous Trajectory Matching Method Based on Piecewise Space-Time Constraints

Liu, Chao; Wang, Jinlei; Liu, Aichao; Cai, Yong-Ning; Ai, Bo

doi:10.1109/access.2020.3044102

Cited by 8 publications

(4 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, vessel trajectory restoration only considers its trajectory data for trajectory restoration. When the starting position of the vessel and the fixed course are known, the trajectory matching technique is indeed a reliable choice to restore the vessel trajectory [16,17]. However, in the real navigation environment, the trajectory of a vessel often deviates from the intended course due to a variety of factors, which requires more general and flexible methods for trajectory restoration.…”

Section: Related Workmentioning

confidence: 99%

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

Ye,

Chen,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

To address the issue of data integrity and reliability caused by sparse vessel trajectory data, this paper proposes a multi-step restoration method for sparse vessel trajectory based on feature correlation. First, we preserved the overall trend of the trajectory by detecting and marking the sparse and abnormal vessel trajectories points and using the cubic spline interpolation method for preliminary restoration. Then, we established a composite indicator of feature correlation for selecting highly correlated trajectory features as inputs to the model, reducing data redundancy while improving the key correlation between trajectory features. Finally, we developed a vessel trajectory restoration model based on the Seq2Seq network for secondary restoration of the trajectory to ensure the accurate restoration of the vessel trajectory. Through comparison and ablation experiments, we demonstrate that the method can efficiently extract highly correlated features from vessel trajectories and combines the advantages of the interpolation method and neural network model to effectively improve the accuracy of trajectory restoration and ensure the integrity and accuracy of trajectory data. The research results could provide crucial technical support for the subsequent mining of vessel behavior patterns and assisted decision-making, which holds significant application prospects and potential value.

show abstract

Section: Related Workmentioning

confidence: 99%

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

Ye,

Chen,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…However, the corresponding time of the model is long, and it is difficult to guarantee the real-time detection. Liu et al [32] proposed a new asynchronous trajectory-matching method (PTSCTM) based on piece-wise spatio-temporal constraints, which is used for AIS trajectory reconstruction and ship abnormal behavior discovery. Based on the spatio-temporal constraints, this method finds the optimal trajectory matching point in the candidate matching point set with time and space distance and uses it to calculate the similarities in trajectory matching.…”

Section: Definition 4 (Od Path)mentioning

confidence: 99%

The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm

et al. 2022

View full text Add to dashboard Cite

As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country.

show abstract

“…A method based on Hausdorff distance is proposed by [14] to compare the similarity of multi-dimensional trajectories and detect the abnormal in them. Reference [15] proposed a method of asynchronous trajectory matching based on piecewise space-time constraints (PTSCTM) to reconstruct and discover the anomalies of ships, in which the Euclidean distance and time distance are used to find similar trajectories points.…”

Section: Introductionmentioning

confidence: 99%

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Guo

Mou

Chen

et al. 2021

JMSE

View full text Add to dashboard Cite

With the enormous amount of information provided by the ship Automatic Identification System (AIS), AIS is now playing a significant role in maritime transport system-related research and development. Many kinds of research and industrial applications are based on the ship trajectory extracted from raw AIS data. However, due to the issues of equipment, the transmission environment, and human factors, the raw AIS data inevitably contain abnormal messages, which have hindered the utilization of such information in practice. Thus, in this paper, an anomaly detection method that focuses on AIS trajectory is proposed, making comprehensive use of the kinematic information of the ship in the AIS data. The method employs three steps to obtain non-error AIS trajectories: (1) data preprocessing, (2) kinematic estimation, and (3) error clustering. It should be noted that steps (2) and (3) are involved in an iterative process to determine all of the abnormal data. A case study is then conducted to test the proposed method on real-world AIS data, followed by a comparison between the proposed method and the rule-based anomaly detection method. As the processed trajectories show fewer abnormal features, the results indicate that the method improves performance and can accurately detect as much abnormal data as possible.

show abstract

An Asynchronous Trajectory Matching Method Based on Piecewise Space-Time Constraints

Cited by 8 publications

References 37 publications

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm

An Anomaly Detection Method for AIS Trajectory Based on Kinematic Interpolation

Contact Info

Product

Resources

About