Traffic Mining Applied to Police Activities - Proceedings of the 1st Italian Conference for the Traffic Police (TRAP- 2017) (Paperback, 1st ed. 2018)

This book presents high-quality original contributions on the development of automatic traffic analysis systems that are able to not only anticipate traffic scenarios, but also understand the behavior of road users (vehicles, bikes, trucks, etc.) in order to provide better traffic management, prevent accidents and, potentially, identify criminal behaviors. Topics also include traffic surveillance and vehicle accident analysis using formal concept analysis, convolutional and recurrent neural networks, unsupervised learning and process mining. The content is based on papers presented at the 1st Italian Conference for the Traffic Police (TRAP), which was held in Rome in October 2017. This conference represents a targeted response to the challenges facing the police in connection with managing massive traffic data, finding patterns from historical datasets, and analyzing complex traffic phenomena in order to anticipate potential criminal behaviors. The book will appeal to researchers, practitioners and decision makers interested in traffic monitoring and analysis, traffic modeling and simulation, mobility and social data mining, as well as members of the police.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Advances in Intelligent Systems and Computing, 728
Release date:	March 2018
First published:	2018
Editors:	Fabio Leuzzi • Stefano Ferilli
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	155
Edition:	1st ed. 2018
ISBN-13:	978-3-319-75607-3
Categories:	Books > Professional & Technical > Civil engineering, surveying & building > Highway & traffic engineering Books > Computing & IT > Internet > Network computers Books > Business & Economics > Business & management > Management & management techniques > Operational research Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	3-319-75607-9
Barcode:	9783319756073

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!