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{{Event 
|Acronym=TRAP 2017 
|Title=TRAP  2017 : First European Conference on Traffic Mining Applied to Police Activities 
|Type=Conference 
|Field=data mining
|Start date=2017/10/25
|End date=2017/10/26 
|City=Rome 
|Country=Italy
|Homepage=https://trap2017.poliziadistato.it/
|Submission deadline=2017/08/21
}}
With the increasing amount of traffic information collected through automatic
number plate reading systems (NPRS), it is highly desirable for police
activities and investigations to find meaningful traffic patterns from the
accumulated massive historical dataset in order to identify potential criminal
behaviors. Nowadays, NPRS sensors are widely spread on Italian highways making
the collection of traffic data more accessible to Italian police, Polizia di
Stato.

However, analyzing traffic data in order to find potential criminal behaviors is
challenging due to the huge size of the dataset and the complexity and dynamics
of traffic phenomena.

The focus of this conference is to develop and employ automatic traffic analysis
systems that can detect, track and in general understand the behavior of
road-users in order to identify criminal behaviors. For example, most of the
criminal activities on Italian highways are perpetrated by criminal drivers in
rest stop or service areas. Those drivers often exhibit a recurring
highway-dart-in-and-out behavior aimed at moving from one service area to
another in order to find a suitable target as part of their victim selection
process.

The scientist, or research group (RG), is totally free to present contributions
with or without the use of the datasets that are provided below.
In case of studies carried out with our datasets, the scientist (or RG) is again
totally free to try to satisfy the shared-task goal, or to face any other open
research problem.

This conference aims to gather not only data mining researchers interested in
these topics, but also traffic researchers and decision makers. Original
research papers related to the following topics, as well as papers regarding
other topics that could be relevant in this area, are welcome:
detection and tracking of road users (vehicles, bikes, trucks, etc.);
behavior understanding of road users;
automatic understanding of the environment in traffic scenarios;
applications related to traffic surveillance;
vehicle accident analysis.

Using:
outliers detection and understanding;
clustering and conceptual clustering;
process mining;
inductive logic programming;
deep learning and classification.

Aiming at focusing the efforts on a common goal, a dataset is provided. It
contains transits recorded using several gates. Records are referred to the
transits of a limited area of Italy, in which gates are homogeneously
distributed. The plates are coherently anonymized (a plate is always referred
using the same ID). The dataset can be downloaded filling a little form about
the affiliation, and other preliminary non-binding indications.

With the intent to unify the way experimental results are evaluated and to push
research forwards the development of a real working system supporting police
activity, a shared task is proposed.

The main practical goal is to identify itineraries that could imply a criminal
intent. The scientist (or RG) is free to define the concept of itinerary,
formalizing it functionally to the proposing approach. Other open data can be
integrated in the itinerary features or not, up to the scientist (or RG).
Criminal intents could be described, for instance, as follows:

the sequential visit of service areas each facing the other;
the sequential visit of service areas in the same direction;
transits inconsistent under the space-time point of view, that are the proof of
cloned plates;
combinations of the previous cases involving several plates and possibly the
same criminal organization;
and so on.
There are no restrictions about methods or techniques. There are only two
requirements to participate in this shared task, that are:

the use of a virtual machine (VM) to send us the system, such a strategy will
make it easier to validate each participant's results;
the representation formalism of the output (details can be found below), in this
way our systems will be able to build an overall report about results, in order
to better evaluate the final outcome.

Program Committee:

Prof. Dino Pedreschi, University of Pisa
Dr. Fosca Giannotti, Information Science and Technology Institute of the
National Research Council at Pisa
Prof. Stefano Ferilli, University of Bari
Prof. Federico Chesani, University of Bologna
Prof. Claudia Diamantini, Marche Polytechnic University
Dr. Marco Montali, University of Bolzano
Dr. Fabiana Lanotte, Digital Transformation Team
Dr. Giorgia Lodi, AgID
Ing. Giacinto Occhiogrosso, Italian National Police
Dr. Tommaso Fornaciari, Italian National Police
Dr. Fabio Leuzzi, Italian National Police
Dr. Salvatore Iengo, Italian National Police
Dr. Onofrio Febbraro, Italian National Police
Dr. Fulvio Rotella, Italian National Police
Dr. Emiliano Del Signore, Italian National Police

Organizing committee:

Dr. Fabio Leuzzi
Dr. Onofrio Febbraro

Invited Speaker:

Dr. Diego Piacentini, Government Commissioner for the Digital Transformation
Team (tbc)

Venue:

The conference will be held in via Tuscolana 1556, 00174, Roma (Italy), October
25-26, 2017.

Key dates:

shared-task system submission deadline: July 21, 2017;
paper submission deadline: August 21, 2017;
paper acceptance notification: September 18, 2017;
conference registration opening: September 19, 2017;
camera-ready paper version: October 2, 2017;
conference registration deadline: October 9, 2017.

Contacts:

fabio.leuzzi@poliziadistato.it
onofrio.febbraro@poliziadistato.it
Acronym	TRAP 2017 +
End date	October 26, 2017 +
Event type	Conference +
Has coordinates	41° 53' 36", 12° 28' 59"Latitude: 41.893319444444 Longitude: 12.482933333333 +
Has location city	Rome +
Has location country	Category:Italy +
Homepage	https://trap2017.poliziadistato.it/ +
IsA	Event +
Start date	October 25, 2017 +
Submission deadline	August 21, 2017 +
Title	TRAP 2017 : First European Conference on Traffic Mining Applied to Police Activities +