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Traffic Mining Applied to Police Activities

Overview of attention for book
Cover of 'Traffic Mining Applied to Police Activities'

Table of Contents

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    Book Overview
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    Chapter 1 Data and Analytics Framework. How Public Sector Can Profit from Its Immense Asset, Data
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    Chapter 2 Advancements in Mobility Data Analysis
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    Chapter 3 Towards a Pervasive and Predictive Traffic Police
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    Chapter 4 A Process Mining Approach to the Identification of Normal and Suspect Traffic Behavior
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    Chapter 5 Detecting Criminal Behaviour Patterns in Spain and Italy Using Formal Concept Analysis
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    Chapter 6 Efficient and Accurate Traffic Flow Prediction via Fast Dynamic Tensor Completion
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    Chapter 7 Reducing the Risk of Accidents with Not Insured British Vehicles in Southern Spain
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    Chapter 8 Unsupervised Classification of Routes and Plates from the Trap-2017 Dataset
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    Chapter 9 Vehicle Classification Based on Convolutional Networks Applied to FMCW Radar Signals
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    Chapter 10 Traffic Data: Exploratory Data Analysis with Apache Accumulo
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    Chapter 11 Exploiting Recurrent Neural Networks for Gate Traffic Prediction
Attention for Chapter 6: Efficient and Accurate Traffic Flow Prediction via Fast Dynamic Tensor Completion
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Chapter title
Efficient and Accurate Traffic Flow Prediction via Fast Dynamic Tensor Completion
Chapter number 6
Book title
Traffic Mining Applied to Police Activities
Published by
Springer, Cham, October 2017
DOI 10.1007/978-3-319-75608-0_6
Book ISBNs
978-3-31-975607-3, 978-3-31-975608-0
Authors

Jinzhi Liao, Xiang Zhao, Jiuyang Tang, Chong Zhang, Mingke He, Liao, Jinzhi, Zhao, Xiang, Tang, Jiuyang, Zhang, Chong, He, Mingke

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 50%
Professor > Associate Professor 1 50%
Readers by discipline Count As %
Computer Science 2 100%

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