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ATTD and ATDS detecting abnormal trajectory detection for urban traffic data

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Xi-Te Wang
Xi-Te Wang
Xi-Te Wang
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Zheng Xu
Zheng Xu
Zheng Xu
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Xiao-Yue Liao
Xiao-Yue Liao
Xiao-Yue Liao
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Mei Bai
Mei Bai
Mei Bai
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Abstract and Figures

Abnormal trajectory detection is pivotal for ensuring safety and optimizing operations in urban traffic management. Despite the progress in this field, current anomaly detection methods, such as the Spatial-Temporal Relationship (STR) algorithm, face limitations including high computational complexity due to simultaneous model calculations, delayed anomaly detection, and an inability to estimate anomalies in the remaining route during online detection. These limitations can lead to inefficiencies and reduced safety in real-world applications. In this paper, we address these limitations by introducing two novel algorithms: Anomaly Trajectory Detection based on Temporal model (ATTD) and Abnormal Trajectory Detection based on Dual Standards (ATDS). The ATTD algorithm simplifies the detection process by integrating a unified spatio-temporal model, which reduces computational complexity and accelerates the detection of anomalies. Furthermore, the ATDS algorithm introduces a proactive approach to anomaly detection that not only identifies anomalies in real-time but also predicts potential deviations in the remaining trajectory, thus providing a more comprehensive and timely detection mechanism. Through extensive experiments on real taxi trajectory datasets, we demonstrate that our algorithms significantly outperform the STR algorithm and other existing methods in terms of detection accuracy and computational efficiency. Our work contributes to the field by providing a more robust and efficient approach to anomaly trajectory detection.
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Applied Intelligence (2025) 55:693
https://doi.org/10.1007/s10489-025-06370-z
ATTD and ATDS detecting abnormal trajectory detection for urban
traffic data
Xi-Te Wang1·Zheng Xu1·Xiao-Yue Liao1·Mei Bai1·Qian Ma1
Accepted: 14 February 2025
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025
Abstract
Abnormal trajectory detection is pivotal for ensuring safety and optimizing operations in urban traffic management. Despite
the progress in this field, current anomaly detection methods, such as the Spatial-Temporal Relationship (STR) algorithm, face
limitations including high computational complexity due to simultaneous model calculations, delayed anomaly detection, and
an inability to estimate anomalies in the remaining route during online detection. These limitations can lead to inefficiencies
and reduced safety in real-world applications. In this paper, we address these limitations by introducing two novel algorithms:
Anomaly Trajectory Detection based on Temporal model (ATTD) and Abnormal Trajectory Detection based on Dual Standards
(ATDS). The ATTD algorithm simplifies the detection process by integrating a unified spatio-temporal model, which reduces
computational complexity and accelerates the detection of anomalies. Furthermore, the ATDS algorithm introduces a proactive
approach to anomaly detection that not only identifies anomalies in real-time but also predicts potential deviations in the
remaining trajectory, thus providing a more comprehensive and timely detection mechanism. Through extensive experiments
on real taxi trajectory datasets, we demonstrate that our algorithms significantly outperform the STR algorithm and other
existing methods in terms of detection accuracy and computational efficiency. Our work contributes to the field by providing
a more robust and efficient approach to anomaly trajectory detection.
Keywords Taxi trajectory ·Abnormal detection ·Spatio-temporal model ·Online detection
1 Introduction
In today’s world, anomaly detection plays a pivotal role
across a multitude of domains. Zero-Shot Anomaly Detec-
tion (ZSAD) algorithm [2] utilizes temporal derivatives and
Grassmann product space projections for detecting anoma-
lies in visual data without the need for labeled training data.
The Stochastic Autoregressive Variational Inference for 3D
BZheng Xu
13583940159@163.com
Xi-Te Wang
wangxite@dlmu.edu.cn
Xiao-Yue Liao
18041563736@163.com
Mei Bai
baimei@dlmu.edu.cn
Qian Ma
maqian@dlmu.edu.cn
1School of information science and technology, Dalian
Maritime University, Dalian 116000, Liaoning, China
Human Motion Generation (SAVI-3D) approach [2] utilizes
a fully differentiable, end-to-end, block-based autoregres-
sive recurrent neural network (RNN) to generate 3D human
motions, incorporating variable auto-conditioning length and
probabilistic variational inference to regulate stochasticity.
With the development of intelligent sensing technology
and the popularity of GPS mobile devices, a large amount
of trajectory data is generated every day. Trajectory data
is spatio-temporal data obtained by sampling the motion
process of moving objects (such as animals, pedestrians,
cars, etc.), including the location information of sampling
points, time labels, movement speed, etc. Trajectory data can
describe the movement behavior of moving objects within a
specific temporal and spatial range, and objectively reflect
the motion laws of moving objects, providing important data
support for related research on urban transportation.
The trajectory data generated by a moving object during
its movement that deviates from the normal mode is called
an abnormal trajectory. Abnormal trajectories often indicate
safety hazards in road traffic, such as fraudulent detours
by passenger drivers. As shown in Fig. 1, the figure shows
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Effective and efficient trajectory outlier detection based on time-dependent popular route
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With the rapid proliferation of GPS-equipped devices, a myriad of trajectory data representing the mobility of various moving objects in two-dimensional space have been generated. This paper aims to detect the anomalous trajectories with the help of the historical trajectory dataset and the popular routes. In this paper, both of spatial and temporal abnormalities are taken into consideration simultaneously to improve the accuracy of the detection. Previous work has developed a novel time-dependent popular routes based algorithm named TPRO. TPRO focuses on finding out all outliers in the historical trajectory dataset. But in most cases, people do not care about which trajectory in the dataset is abnormal. They only yearn for the detection result of a new trajectory that is not included in the dataset. So this paper develops the the upgrade version of TPRO, named TPRRO. TPRRO is a real-time outlier detection algorithm and it contains the off-line preprocess step and the on-line detection step. In the off-line preprocess step, TTI (short for time-dependent transfer index) and hot TTG (short for time-dependent transfer graph) cache are constructed according to the historical trajectory dataset. Then in the on-line detection step, TTI and hot TTG cache are used to speed up the detection progress. The experiment result shows that TPRRO has a better efficiency than TPRO in detecting outliers.
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Anomalous Trajectory Detection Using Recurrent Neural Network: 14th International Conference, ADMA 2018, Nanjing, China, November 16–18, 2018, Proceedings
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Anomalous trajectory detection which plays an important role in taxi fraud detection and trajectory data preprocessing is a crucial task in trajectory mining fields. Traditional anomalous trajectory detection methods which utilize density and isolation approaches mainly focus on the differences of a new trajectory and the historical trajectory dataset. Although these methods can capture the particular characteristics of trajectories, they still suffer from the following two disadvantages. (1) These methods cannot capture the sequential information of the trajectory well. (2) These methods only concentrate on the given source and destination which may lead to data sparsity issues. To overcome above shortcomings, we propose a novel method called Anomalous Trajectory Detection using Recurrent Neural Network (ATD-RNN) which characterizes the trajectory by learning the trajectory embedding. The trajectory embedding can capture the sequential information of the trajectory and depict the internal characteristics between anomalous and normal trajectory. To address the potential data sparsity problem, we enlarge the dataset between a source and a destination by taking the relevant trajectories into consideration. Extend experiments on real-world datasets validate the effectiveness of our method.
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