Multi-scale modelling is a powerful approach that has been successfully exploited in the context of simulation of traffic and transportation systems. While the paradigm allows the simulation of large cities in a already efficient fashion, the consideration of detailed environments for a precise simulation of pedestrian traffic can be still a demanding task, especially in iterative approaches for the search of optimal solutions. In this context, the paper proposes the application of a supervised machine learning algorithm to learn the observables of a microscopic model of pedestrian dynamics in the simulated environment. The aim is to generate a simpler model that (i) is able to describe the dynamic travel times of pedestrians in the scenario and (ii) can replace the microscopic model in the iterative search of optimal solutions. After a formal description of the approach, the paper provides preliminary results with its application in benchmark scenarios, aimed at analysing its reliability in controlled conditions.

Crociani, L., Lämmel, G., Vizzari, G., Bandini, S. (2018). Learning obervables of a multi-scale simulation system of urban traffic. In Proceedings of the Tenth International Workshop on Agents in Traffic and Transportation (ATT 2018), Stockholm, Sweden, July 14, 2018 (pp.40-48). CEUR-WS.

Learning obervables of a multi-scale simulation system of urban traffic

Crociani, Luca
Primo
;
Vizzari, Giuseppe
Penultimo
;
Bandini, Stefania
Ultimo
2018

Abstract

Multi-scale modelling is a powerful approach that has been successfully exploited in the context of simulation of traffic and transportation systems. While the paradigm allows the simulation of large cities in a already efficient fashion, the consideration of detailed environments for a precise simulation of pedestrian traffic can be still a demanding task, especially in iterative approaches for the search of optimal solutions. In this context, the paper proposes the application of a supervised machine learning algorithm to learn the observables of a microscopic model of pedestrian dynamics in the simulated environment. The aim is to generate a simpler model that (i) is able to describe the dynamic travel times of pedestrians in the scenario and (ii) can replace the microscopic model in the iterative search of optimal solutions. After a formal description of the approach, the paper provides preliminary results with its application in benchmark scenarios, aimed at analysing its reliability in controlled conditions.
paper
Artificial intelligence, Iterative methods, multi-agent systems, simulation, supervised learning
English
10th International Workshop on Agents in Traffic and Transportation, ATT 2018
2018
Ana Lúcia C. Bazzan, Luca Crociani, Ivana Dusparic, Sascha Ossowski
Proceedings of the Tenth International Workshop on Agents in Traffic and Transportation (ATT 2018), Stockholm, Sweden, July 14, 2018
2018
2129
40
48
http://ceur-ws.org/Vol-2129/paper18.pdf
none
Crociani, L., Lämmel, G., Vizzari, G., Bandini, S. (2018). Learning obervables of a multi-scale simulation system of urban traffic. In Proceedings of the Tenth International Workshop on Agents in Traffic and Transportation (ATT 2018), Stockholm, Sweden, July 14, 2018 (pp.40-48). CEUR-WS.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/202252
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