Demo Data from SIGIR 2026 / reviewers in the wild / expert
Giandomenico Gagliardi
dblp:257/4519
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Smart cities and intelligent transportation › traffic modeling
traffic simulation |
0.6 | 1 | 2022 | Vehicular Traffic Simulation in the City of Turin From Raw Data · IEEE Trans. Mob. Comput. 2022 |
Network performance modeling
simulation testbed |
0.2 | 1 | 2022 | Vehicular Traffic Simulation in the City of Turin From Raw Data · IEEE Trans. Mob. Comput. 2022 |
Vehicular, aerial and satellite networks
vehicular networks |
0.2 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Vehicular Traffic Simulation in the City of Turin From Raw DataabstractThe 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 TurinabstractTraffic 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-RT | 3 |