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Giandomenico Gagliardi

dblp:257/4519 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-0351-4862ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Computer networks · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%
Computer networks
1 paper
Vehicular, aerial and satellite networks · 50% Network performance modeling · 50%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Smart cities and intelligent transportation › traffic modeling
traffic simulation
0.612022
Vehicular Traffic Simulation in the City of Turin From Raw Data · IEEE Trans. Mob. Comput. 2022
Network performance modeling
simulation testbed
0.212022
Vehicular Traffic Simulation in the City of Turin From Raw Data · IEEE Trans. Mob. Comput. 2022
Vehicular, aerial and satellite networks
vehicular networks
0.212022
Vehicular Traffic Simulation in the City of Turin From Raw Data · IEEE Trans. Mob. Comput. 2022

Methods — techniques the papers use, named apart from their topics

traffic simulation · 1.1data-driven modeling · 1.1
YearPublicationVenuePosition
2022 Vehicular Traffic Simulation in the City of Turin From Raw Data
abstract
The testing of vehicular communication technologies, the study of urban mobility patterns, the evaluation of new traffic policies cannot dispense from vehicle mobility simulation. As is often the case, the larger the dataset, the better. Indeed, in recent years, many projects in the fields of mobility or vehicular communication have sought new traffic simulators with extended areas of investigation, possibly covering a whole city and its suburbs. In this spirit, we have modeled an urban traffic simulation in a 600-Km2area in and around the Municipality of Turin, leveraging the SUMO tool. This paper aims at reporting in detail the methodology we followed in the creation of this dataset. Our results demonstrate that a complete modeling of such a wide area is possible at the expense of minor simplifications, reaching a very good level of approximation.
Marco Rapelli, Claudio Casetti, Giandomenico Gagliardi
IEEE Trans. Mob. Comput.3
2019 TuST: from Raw Data to Vehicular Traffic Simulation in Turin
abstract
Traffic simulations are becoming a standard way to study urban mobility patterns, to evaluate new traffic policies and to test modern vehicular technologies. For this reason, in recent years, mobility projects pushed towards an increase in the demand of traffic simulators and towards an extension of their area of investigation, aiming at covering a whole city and its suburbs. In this paper we describe the methodology we followed in the creation of a large-scale traffic simulation of a 400-Km2area around the Municipality of Turin. Our preliminary results demonstrate that a complete modeling of such a wide tool is possible at the expense of minor simplifications.
Marco Rapelli, Claudio Casetti, Giandomenico Gagliardi
DS-RT3