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Effectiveness of the Two-Step Dynamic Demand Estimation model on large networks
Cantelmo, Guido; Viti, Francesco; Derrmann, Thierry
2017In Proceedings of 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Peer reviewed
 

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Keywords :
O-D estimation; Two-Steps optimisation; Quasi-Dynamic assumption; bilevel optimisation; GSM data
Abstract :
[en] In this paper, the authors present a Two-Step approach that sequentially adjusts generation and distribution values of the (dynamic) OD matrix. While the proposed methodology already provided excellent results for updating demand flows on a motorway, the aim of this paper is to validate this conclusion on a real network: Luxembourg City. This network represents the typical middle-sized European city in terms of network dimension. Moreover, Luxembourg City has the typical structure of a metropolitan area, composed of a city centre, ring, and suburb areas. An innovative element of this paper is to use mobile network data to create a time-dependent profile of the generated demand inside and outside the ring. To support the claim that the model is ready for practical implementation, it is interfaced with PTV Visum, one of the most widely adopted software tools for traffic analysis. Results of these experiments provide a solid empirical ground in order to further develop this model and to understand if its assumptions hold for urban scenarios.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Cantelmo, Guido ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Viti, Francesco  ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit
Derrmann, Thierry ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Effectiveness of the Two-Step Dynamic Demand Estimation model on large networks
Publication date :
28 June 2017
Event name :
5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Event date :
28-6-2017
Audience :
International
Journal title :
Proceedings of 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
Publisher :
IEEE
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
FnR Project :
FNR6947587 - Improving Demand Estimation With Activity Scheduling, 2013 (01/02/2014-31/01/2018) - Guido Cantelmo
Funders :
6947587 IDEAS (Fonds National de Recherche FNR)
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since 07 November 2017

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