A Novel Machine Learning-Based Approach to City Crime Sensor Placement Prediction
Author:
Affiliation:
1. School of IT, York University,Toronto,Canada
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10539362/10539375/10539414.pdf?arnumber=10539414
Reference17 articles.
1. Crime and Risky Behavior in Traffic: An Example of Cross-Situational Consistency
2. Targeting Fatal Traffic Collision Risk from Prior Non-Fatal Collisions in Toronto;Bavcevic;Camb J Evid Based Polic,2020
3. Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
4. Using GPS-based AVL data to calculate and predict traffic network performance metrics: A systematic review
5. Short time traffic speed prediction using data from a number of different sensor locations
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1. Cloud-Based Perspective for Intelligent Transportation and Crime Prevention: A Web Deployment Solution;2024 International Conference on Computer, Information and Telecommunication Systems (CITS);2024-07-17
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