393 research outputs found
Sensitivity analysis and how to choose parameters to calibrate
No full textThe MULTITUDE Project (Methods and tools for supporting the Use, caLibration and validaTIon of Traffic simUlations moDEls) is an Action (TU0903) supported by the EU COST office (European Cooperation in Science and Technology) and focuses on the issue of uncertainty in traffic simulation, and of calibration and validation as tools to manage it. It is driven by the concern that, although modelling is now widespread, we are unsure how much we can trust our results and conclusions. Such issues force into question the trustworthiness of the results, and indeed how well we are using them.
The project consists of 4 Working Groups (WGs) which hold short, focussed, meetings on topics of interest and propose work items on key issues. Additionally the project holds an annual meeting, as well as training schools, where the latest thinking can be passed on to young researchers and practitioners.
This report covers much of the technical work performed by Working Group 4 ‘Synthesis, dissemination and training’, and has been contributed to by:
- Costas Antoniou, NTUA, GR
- Jaume Barcelo, UPC, ES
- Mark Brackstone, IOMI, UK
- Hilmi Berk Celikoglu, ITU, TR
- Biagio Ciuffo, JRC, IT
- Vincenzo Punzo, JRC/UNINA, IT
- Pete Sykes, PS-TTRM, UK
- Tomer Toledo, Technion, IL
Peter Vortisch, KIT, DE
Peter Wagner, DLR, DE
This document assesses the current situation regarding guidelines for traffic simulation model calibration and validation worldwide, discusses the problems currently faced, and suggests potential ways in which they can be addressed, both directly, and indirectly through the development of the overall field of traffic simulation as a whole
"Reconstructed trajectories" dataset from the US FHWA NGSim project I80-1 dataset
No full textIl dataset contiene le traiettorie di veicoli "ricostruite" dal dataset originale - I80 (04:00pm-04:15pm) - ricavato nell'ambito del programma Next Generation Simulation (NGSIM) della US Federal Highway Administration. Il dataset di traiettorie "ricostruite" è pubblicato sul sito governativo USA "ITS DataHub" (si veda l'URL riportato sopra), insieme al dataset originale ricavato nel progetto. Il dataset è stato ottenuto con la metodologia descritta nell'articolo: M. Montanino, V. Punzo, 2015. Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns. Transportation Research Part B: Methodological 80, 82-106. Si veda anche il report disponibile sul sito del progetto MULTITUDE - http://www.multitude-project.eu/reconstructed-ngsim.htm
C. M. Stracke, D. Griffiths, D. Pappa, S. Bećirović, E. Polz, L. Perla, A. Di Grassi, S. Massaro, M. P. Skenduli, D. Burgos, V. Punzo, D. Amram, X. Ziouvelou, D. Katsamori, S. Gabriel, N. Nahar, J. Schleiss, P. Hollins. Analysis of Artificial Intelligence Policies for Higher Education in Europe, International Journal of Interactive Multimedia and Artificial Intelligence, vol. 9, no. 2, pp. 124-137, 2025, http://dx.doi.org/10.9781/ijimai.2025.02.011
No full textThis paper analyses 15 AI policies for higher education from eight European countries, drawn from individual
universities, from consortia of universities and from government agencies. Based on an overview of current
research findings, it focuses the comparison of different aspects among the selected AI policies. The analysis
distinguishes between four potential target groups, namely students, teachers, education managers and policy
makers. The paper aims at contributing to the further development and improvement of AI policies for higher
education through the identification of commonalities and gaps within the existing AI policies. Moreover, it
calls for further and in particular evidence-based research to identify the potential and practical impact of AI
in higher education and highlights the need to combine AI use in (higher) education with education about AI,
often called as AI literac
On string stability of a mixed and heterogeneous traffic flow: A unifying modelling framework
No full textUrged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and connected, automated vehicles (CAVs), research has recently focused at making the most of CAVs capabilities to mitigate the instability of the whole, i.e. mixed, traffic flow. In all works, however, either the two sub-flows are studied under a simplifying but unrealistic assumption of flow homogeneity, or drivers’ and vehicles heterogeneity is not correctly taken into account within each sub-flow. We show here that the only condition developed so far to study a car-following model string stability for a heterogeneous flow, is inaccurate. Therefore, we propose a methodology to model string stability that considers drivers’ and vehicles heterogeneity, which is the essence of a real traffic. Uncertain transfer functions are introduced to map the probability distributions of car-following model parameters into a L2 stability measure of a mixed and heterogeneous traffic. Specifically, they allow us to move from the stability analysis of a car-following model, or of a controller, to the stability analysis of a traffic flow, as interpreted by that model, or controller. Eventually, several other theoretical contributions on stability analysis are given in the paper, aiming at reconciling approaches from different fields. Among these, a mathematical justification of the equivalence between the asymptotic stability of a closed-loop platoon system – which has been studied through the famous “traffic wave ansatz” on a ring-road – and the L2 stability of an open-loop platoon system
Car-following models evaluation through accurate experimental data
No full textINRETS, Arcueil (Paris
Car-following models evaluation through accurate experimental data
No full textINRETS, Arcueil (Paris
RailBit
No full textIL PROGRAMMA E' UN SOFTWARE DI SIMULAZIONE DEL TRAFFICO FERROVIARIO, FUNZIONANTE SU PC E MAC IN AMBIENTE OPERATIVO WINDOWS, MAC OS E LINU
NinjaPark
No full textIl software abilita una gestione efficiente della sosta su strada condividendo informazioni sulla sosta all'interno della comunità di utenti
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