UnivTrevesV1, LNCS, Analysis, bibRecord, 000001

Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments

Identifieur interne : 000001 ( LNCS/Analysis ); précédent : 000000; suivant : 000002

Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments

Auteurs : Andreas D. Lattner [Allemagne] ; Jörg Dallmeyer [Allemagne] ; Ingo J. Timm [Allemagne]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2011.

RBID : ISTEX:E24A56BEF29C753D00A01508064C0CD7A81656ED

Abstract

Abstract: The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.

Url:

https://api.istex.fr/document/E24A56BEF29C753D00A01508064C0CD7A81656ED/fulltext/pdf

DOI: 10.1007/978-3-642-24603-6_9

Affiliations:

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ISTEX:E24A56BEF29C753D00A01508064C0CD7A81656ED

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<front><div type="abstract" xml:lang="en">Abstract: The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.</div>
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Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments

Learning Dynamic Adaptation Strategies in Agent-Based Traffic Simulation Experiments

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Abstract

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