Face aux externalités négatives (par exemple, congestion, émissions de CO, pollution sonore) des systèmes de transport, il est important de disposer d’outils de simulation adéquats pour aider à mettre en place une politique de mobilité plus respectueuse de l’environnement et économiquement acceptable par les populations. Dans ce papier, nous proposons un cadre de simulation multi-agent permettant d’évaluer les politiques d’inter-modalité existantes et de pouvoir en explorer de nouvelles. Notre cadre de simulation intègre un modèle de choix discret tenant compte de l’alternative intermodale, combinant la voiture personnelle et un transport public pour une meilleure prise de décision du choix modal de l’agent. Comme expérimentations, nous proposons une évaluation de la politique de mobilité durable basée sur la mise à disposition de parkings relais pour favoriser l’intermodalité dans un réseau multimodal à grande échelle. Nous avons pu faire ressortir le rôle que jouent ces infrastructures dans les pratiques intermodales notamment pour les déplacements issus des zones périphériques vers l’hypercentre. Le cadre de simulation ainsi proposé peut servir de support d’aide à la prise de décision pour permettre l’évaluation de différentes mesures de mobilité.
Due to transport systems’ negative externalities (e.g., congestion, CO emissions), it is essential to have well-adapted simulation tools to study a sustainable mobility policy. In this paper, we propose a multi-agent framework allowing to evaluate existing intermodality policies and to be able to explore new ones. It investigates a discrete choice model considering the intermodal alternative, combining private car and public transport for the agent’s better decision-making of the modal choice. As experiments, we evaluate the sustainable mobility policy based on providing park-and-ride facilities to promote intermodality in a large-scale multimodal network. We highlight the role played by these infrastructures in intermodal practices, particularly for trips from peripheral areas to the hyper-center. The proposed simulation framework can be used to support decision-making to evaluate different mobility measures.
Accepté le :
Publié le :
Keywords: Agent-based model (ABM), Logit model, Lnter-modality.
Azise O. Diallo 1, 2 ; Arnaud Doniec 1 ; Guillaume Lozenguez 1 ; René Mandiau 3
CC-BY 4.0
@article{ROIA_2024__5_1_95_0,
author = {Azise O. Diallo and Arnaud Doniec and Guillaume Lozenguez and Ren\'e Mandiau},
title = {Approche centr\'ee agent pour l{\textquoteright}intermodalit\'e bas\'ee sur des donn\'ees r\'eelles},
journal = {Revue Ouverte d'Intelligence Artificielle},
pages = {95--129},
publisher = {Association pour la diffusion de la recherche francophone en intelligence artificielle},
volume = {5},
number = {1},
year = {2024},
doi = {10.5802/roia.66},
language = {fr},
url = {https://roia.centre-mersenne.org/articles/10.5802/roia.66/}
}
TY - JOUR AU - Azise O. Diallo AU - Arnaud Doniec AU - Guillaume Lozenguez AU - René Mandiau TI - Approche centrée agent pour l’intermodalité basée sur des données réelles JO - Revue Ouverte d'Intelligence Artificielle PY - 2024 SP - 95 EP - 129 VL - 5 IS - 1 PB - Association pour la diffusion de la recherche francophone en intelligence artificielle UR - https://roia.centre-mersenne.org/articles/10.5802/roia.66/ DO - 10.5802/roia.66 LA - fr ID - ROIA_2024__5_1_95_0 ER -
%0 Journal Article %A Azise O. Diallo %A Arnaud Doniec %A Guillaume Lozenguez %A René Mandiau %T Approche centrée agent pour l’intermodalité basée sur des données réelles %J Revue Ouverte d'Intelligence Artificielle %D 2024 %P 95-129 %V 5 %N 1 %I Association pour la diffusion de la recherche francophone en intelligence artificielle %U https://roia.centre-mersenne.org/articles/10.5802/roia.66/ %R 10.5802/roia.66 %G fr %F ROIA_2024__5_1_95_0
Azise O. Diallo; Arnaud Doniec; Guillaume Lozenguez; René Mandiau. Approche centrée agent pour l’intermodalité basée sur des données réelles. Revue Ouverte d'Intelligence Artificielle, Volume 5 (2024) no. 1, pp. 95-129. doi : 10.5802/roia.66. https://roia.centre-mersenne.org/articles/10.5802/roia.66/
[1] Microscopic Traffic Simulation using SUMO, The 21st IEEE International Conference on Intelligent Transportation Systems, IEEE (2018) https://elib.dlr.de/124092/ | DOI
[2] AnyLogic : des solutions et outils logiciels de modélisation de simulation au service des entreprises, 2018 (https://www.anylogic.fr/, [Accessed October-25th-2018])
[3] Travel demand modeling for multi-agent transport simulations : Algorithms and systems, Ph. D. Thesis, ETH Zurich (2007)
[4] A review on agent-based technology for traffic and transportation, The Knowledge Engineering Review, Volume 29 (2014) no. 3, pp. 375-403 | DOI
[5] Discrete choice analysis : theory and application to travel demand, 9, MIT press, 1985
[6] A short introduction to PandasBiogeme (2020) (Technical report)
[7] Simuler le trafic routier à partir de données réelles, Revue d’Intelligence Artificielle, Volume 30 (2016) no. 3, pp. 329-352 | DOI
[8] Prévision de la demande de transport, Ph. D. Thesis, Université Lumière-Lyon II (2002)
[9] Public transport pricing policy : Empirical evidence from a fare-free scheme in Tallinn, Estonia, Transportation Research Record, Volume 2415 (2014) no. 1, pp. 89-96 | DOI
[10] Modélisation multimodale des déplacements de voyageurs Concevoir un modèle de choix modal (2015) (Technical report)
[11] Upgrading the city : Enabling intermodal travel behaviour, Technological Forecasting and Social Change, Volume 89 (2014), pp. 222-235 | DOI
[12] Agent-based simulation from anonymized data : An application to Lille metropolis, The 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021), March 23-26, 2021, Warsaw, Poland, Volume 184, Elsevier (2021), pp. 164-171 | DOI
[13] Comparative evaluation of road traffic simulators based on modeler’s specifications : An application to intermodal mobility behaviors, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART), Volume 1 (2021), pp. 265-272 | DOI
[14] Usage des parkings relais dans les comportements de déplacements intermodaux : génération de demande de population d’agents à partir de données réelles, Collectifs cyber-physiques – Vingt-neuvièmes Journées Francophones sur les Systèmes Multi-Agents, Bordeaux, France, June 28-30, 2021 (Jean-Paul Jamont, ed.), Cépaduès (2021), pp. 83-92
[15] Agent-Based Intermodal Behavior for Urban Toll, Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, July 19-22, 2022, Proceedings (Hamido Fujita; Philippe Fournier-Viger; Moonis Ali; Yinglin Wang, eds.) (Lecture Notes in Computer Science), Volume 13343, Springer (2022), pp. 397-408 | DOI
[16] Estimation of minority modes of transportation based on machine learning approach, The 13th International Conference on Ambient Systems, Networks and Technologies (ANT 2022), March 22-25, 2022, Porto, Portugal (Elhadi M. Shakshuki; Ansar-Ul-Haque Yasar, eds.) (Procedia Computer Science), Volume 201, Elsevier (2022), pp. 265-272 | DOI
[17] GAMA 1.6 : Advancing the art of complex agent-based modeling and simulation, International Conference on Principles and Practice of Multi-Agent Systems, Springer (2013), pp. 117-131 | DOI
[18] An analysis of parking behaviour using discrete choice models calibrated on SP datasets, 44th Congress of the European Regional Science Association : “Regions and Fiscal Federalism”, 25th - 29th August 2004, Porto, Portugal, Louvain-la-Neuve : European Regional Science Association (ERSA) (2004)
[19] Introducing the eqasim pipeline : From raw data to agent-based transport simulation, Procedia Computer Science, Volume 184 (2021), pp. 712-719 | DOI
[20] Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data, Transportation Research Part C : Emerging Technologies, Volume 130 (2021), 103291 | DOI
[21] Pairing discrete mode choice models and agent-based transport simulation with MATSim, Transportation Research Board (TRB) 98th Annual Meeting, Transportation Research Board (2019) | DOI
[22] Multi-Agent Transport Simulation MATSim (Andreas Horni; Kai Nagel; Kay Axhausen, eds.), Ubiquity Press, 2016, 103291, 618 pages | DOI
[23] New computer models unify ecological theory : computer simulations show that many ecological patterns can be explained by interactions among individual organisms, BioScience, Volume 38 (1988) no. 10, pp. 682-691 | DOI
[24] Developing a standard definition of intermodal transportation, Transportation Law Journal, Volume 27 (2000) no. 3, pp. 345-352
[25] Creating an open MATSim scenario from open data : The case of Santiago de Chile (2016) no. 16-02 (Working Paper)
[26] Road pricing and public transport pricing reform in Paris : complements or substitutes ?, Economics of Transportation, Volume 3 (2014) no. 2, pp. 175-187 | DOI
[27] Embedding intermodal mobility behavior in an agent-based demand model, Procedia computer science, Volume 130 (2018), pp. 865-871 | DOI
[28] Meso lighthill-whitham and richards model designed for network applications, Transportation Research Board (TRB) 91st Annual Meeting (2012) no. 12-0387
[29] Les méthodes de mesure et de représentation de l’accessibilité dans les méthodes d’évaluation des projets de transport interurbains et périurbains de voyageurs : méthode, indicateurs, applications et limites de la contactabilité (2012) (Rapport technique)
[30] On kinematic waves II. A theory of traffic flow on long crowded roads, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, Volume 229 (1955) no. 1178, pp. 317-345 | DOI
[31] Behaviour based on decision matrices for a coordination between agents in a urban traffic simulation, Appl. Intell., Volume 28 (2008) no. 2, pp. 121-138 | DOI
[32] The four step model, Handbook of transport modelling (D. A. Hensher; K. J. Button, eds.), Volume 1, Emerald Group Publishing Limited, 2007, pp. 35-53 | DOI
[33] The activity-based approach, Handbook of Transport Modelling, Volume 1, Emerald Group Publishing Limited, 2007, pp. 55-73 | DOI
[34] Prototype model of household activity-travel scheduling, Transportation Research Record, Volume 1831 (2003) no. 1, pp. 114-121 | DOI
[35] et al. Technical solutions to overcrowded park and ride facilities (2007) (Technical report)
[36] Intermodality in a context of poor transport integration : the case of Sub-Saharan African cities, Transport Research Arena (TRA) 5th Conference (2014)
[37] Combining means of transport as a users’ strategy to optimize traveling in an urban context : empirical results on intermodal travel behavior from a survey in Berlin, Journal of Transport Geography, Volume 71 (2018), pp. 72-83 | DOI
[38] Public transit route mapping for large-scale multimodal networks, ISPRS International Journal of Geo-Information, Volume 6 (2017) no. 9, 268 | DOI
[39] A critical review on population synthesis for activity- and agent-based transportation models, Transportation Systems Analysis and Assessment (Stefano De Luca; Roberta Di Pace; Boban Djordjevic, eds.), IntechOpen London, 2019, pp. 1-16 | DOI
[40] Activity-based models of travel demand : promises, progress and prospects, International Journal of Urban Sciences, Volume 18 (2014) no. 1, pp. 31-60 | DOI
[41] The household activity pattern problem : general formulation and solution, Transportation Research Part B : Methodological, Volume 29 (1995) no. 1, pp. 61-77 | DOI
[42] Shock waves on the highway, Operations research, Volume 4 (1956) no. 1, pp. 42-51 | DOI | Zbl
[43] Mise en perspectives des Enquête Nationales Transport, INRETS/DEST Département Economie et Sociologie des Transports, 2008
[44] Artificial Intelligence : A Modern Approach, Pearson Education, 2003
[45] Multimodal transport pricing : first best, second best and extensions to non-motorized transport, Transport Reviews, Volume 32 (2012) no. 2, pp. 181-202 | DOI
[46] Discrete choice methods with simulation, Cambridge university press, 2009
[47] Modélisation et simulation à base d’agents : exemples commentés, outils informatiques et questions théoriques, Dunod, 2008
[48] An introduction to agent-based modeling : modeling natural, social, and engineered complex systems with Netlogo, MIT Press, 2015 http://ccl.northwestern.edu/netlogo/
[49] An introduction to multiagent systems, John Wiley & Sons, 2009
[50] Comparing methods for generating a two-layered synthetic population, Transportation research record, Volume 2675 (2021) no. 1, pp. 136-147 | DOI
[51] The MATSim Open Berlin Scenario : A multimodal agent-based transport simulation scenario based on synthetic demand modeling and open data, Procedia computer science, Volume 151 (2019), pp. 870-877 | DOI
Cité par Sources :