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Anomalous Trajectory Detection Using Recurrent Neural Network

  • Li Song
  • Ruijia Wang
  • Ding Xiao
  • Xiaotian Han
  • Yanan Cai
  • Chuan ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11323)

Abstract

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.

Keywords

Anomalous trajectory detection Trajectory embedding Recurrent neural network 

Notes

Acknowledgement

This work is supported in part by the National Natural Science Foundation of China (No. 61772082, 61702296, 61375058), and the Beijing Municipal Natural Science Foundation (4182043).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Li Song
    • 1
  • Ruijia Wang
    • 1
  • Ding Xiao
    • 1
  • Xiaotian Han
    • 1
  • Yanan Cai
    • 2
  • Chuan Shi
    • 1
    Email author
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Big Data CenterPICC Property and Casualty Company LimitedBeijingChina

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