Traffic simulators are effective tools to support decisions in urban planning systems, to identify criticalities, to observe emerging behaviours in road networks and to configure road infrastructures, such as road side units and traffic lights. Clearly the more realistic the simulator the more precise the insight provided to decision makers. This paper provides a first step toward the design and calibration of traffic micro-simulator to produce realistic behaviour. The long term idea is to collect and analyse real traffic traces collecting vehicular information, to cluster them in groups representing similar driving behaviours and then to extract from these clusters relevant parameters to tune the microsimulator. In this paper we have run controlled experiments where traffic traces have been synthetized to obtain different driving styles, so that the effectiveness of the clustering algorithm could be checked on known labels. We describe the overall methodology and the results already achieved on the controlled experiment, showing the clusters obtained and reporting guidelines for future experiments.

Driving behaviour clustering for realistic traffic micro-simulators / Petraro A.; Caselli F.; Milano M.; Lippi M.. - ELETTRONICO. - (2017), pp. 18-24. (Intervento presentato al convegno 31st European Conference on Modelling and Simulation, ECMS 2017 tenutosi a hun nel 2017) [10.7148/2017-0018].

Driving behaviour clustering for realistic traffic micro-simulators

Lippi M.
2017

Abstract

Traffic simulators are effective tools to support decisions in urban planning systems, to identify criticalities, to observe emerging behaviours in road networks and to configure road infrastructures, such as road side units and traffic lights. Clearly the more realistic the simulator the more precise the insight provided to decision makers. This paper provides a first step toward the design and calibration of traffic micro-simulator to produce realistic behaviour. The long term idea is to collect and analyse real traffic traces collecting vehicular information, to cluster them in groups representing similar driving behaviours and then to extract from these clusters relevant parameters to tune the microsimulator. In this paper we have run controlled experiments where traffic traces have been synthetized to obtain different driving styles, so that the effectiveness of the clustering algorithm could be checked on known labels. We describe the overall methodology and the results already achieved on the controlled experiment, showing the clusters obtained and reporting guidelines for future experiments.
2017
Proceedings - 31st European Conference on Modelling and Simulation, ECMS 2017
31st European Conference on Modelling and Simulation, ECMS 2017
hun
2017
Petraro A.; Caselli F.; Milano M.; Lippi M.
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1356523
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