Anomalous trajectory detection from taxi GPS traces using combination of iBAT and DTW

Taniarza, Natya; Akbar, Saiful

doi:10.1109/iceei.2017.8312459

Cited by 6 publications

(5 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these methods can effectively satisfy the needs of trajectory anomaly detection in their own scenarios, they are limited to running in a single node in batch mode [1][2][3][4]. Therefore, their capacities are not enough to meet the requirements of large-scale detection.…”

Section: Gps Trajectory Anomaly Detectionmentioning

confidence: 99%

“…For example, if a bus deviated from the designated route during a trip, the existing solution typically becomes aware about this anomaly within a few minutes. On the other hand, the current solutions [1][2][3][4] fail to handle a large-scale bus transportation system. Currently, many existing works have been conducted on abnormal detection for vehicle GPS trajectories [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the current solutions [1][2][3][4] fail to handle a large-scale bus transportation system. Currently, many existing works have been conducted on abnormal detection for vehicle GPS trajectories [1][2][3][4]. These methods conduct anomaly detection on historical GPS trajectories in the batch mode.…”

Section: Introductionmentioning

confidence: 99%

“…The detecting process is composed of two steps: (1) The first step is to transform the raw GPS trajectory. In this step, it detects the bus arrival event based on the raw GPS trajectory, then converts the GPS trajectory into a stop sequence, and finally employs the state management mechanism to save and update the stop sequence.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink

Zou,

Xiong,

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

Bus transportation system has become the primary mode of traffic for urban residents. Every day, thousands of buses provide services for millions of passengers. Efficiently monitoring bus trajectories is essential for evaluating service quality and ensuring public safety. In this study, we propose a Flink-based solution to detect anomalies for bus trajectories in real time. Specifically, it can identify two types of anomalies. The first type is when a bus deviates from its designated route during a trip. The second type is when a bus arrives at a scheduled stop along its route but fails to stop. This solution employs CEP (Complex Event Processing) to determine bus arrival events and control the detection process. In this process, it utilizes the state management mechanism to save and update a bus’s actual trajectory, which is derived from the raw GPS trajectory and maintained as a stop sequence. Subsequently, it uses LCSS (Longest Common Subsequence) to measure the trajectory similarity between the actual bus trajectory and the scheduled route. We validate the solution using a large-scale real dataset in a Flink cluster with six virtual machines. The experimental results show that (1) each core can handle anomaly detection on 12.5 buses simultaneously and (2) the detection accuracies of the two anomalies are 90.5% and 89.3%, respectively.

show abstract

Section: Gps Trajectory Anomaly Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Real-Time Anomaly Detection Method of Bus Trajectory Based on Flink

Zou,

Xiong,

Wang

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Pang et al [28] proposed a pattern recognition method based on likelihood ratio test statistics, which can detect "persistent" and "new" outliers in trajectory data effectively. Uniform grids have been used to represent the trajectory space and detect abnormal behaviors of taxis in real-time [29,30]. e detection accuracy of gridbased anomaly detection methods is poor for trajectories with nonnormal distribution.…”

Section: Related Workmentioning

confidence: 99%

Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment

Huang

Zhu

Huang

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

Fishing vessel monitoring systems (VMSs) play an important role in ensuring the safety of fishing vessel operations. Traditional VMSs use a cloud centralized computing model, and the storage, processing, and visualization of all fishing vessel data are completed in the monitoring center. Due to the limitation of maritime communications, the data generated by fishing vessels cannot be fully utilized, and communication delays lead to inadequate warnings in cases of fishing vessel abnormalities. In this paper, we present a real-time anomaly detection model (RADM) for fishing vessels based on edge computing. The model runs in the edge layer, making full use of the information of moving edge nodes and nearby nodes, and combines a historical trajectory extraction detection model with an online anomaly detection model to detect anomalies. The detection model of historical trajectory extraction mines frequent patterns in historical trajectories through multifeature clustering and identifies trajectories that are different from the frequent patterns as anomalies. Online anomaly detection algorithms detect anomalous behavior in specific scenarios based on the spatiotemporal neighborhood similarity and reduce the impact of anomaly evolution. Experiments show that RADM was more effective than traditional methods in real-time anomaly detection of fishing vessels, which provides a new method for upgrading the technology of traditional VMS.

show abstract