Intersection Modeling Using Generalized Fuzzy Graph Coloring

Boudaakat, Sidina; Basmassi, Mohamed Amine; Rebbani, Ahmed; Chentoufi, Jihane Alami; Benameur, Lamia; Bouattane, Omar

doi:10.1007/978-3-030-66840-2_111

Cited by 2 publications

(4 citation statements)

References 26 publications

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“…The modeling layer uses the IMGFG technique [17] where a generalized fuzzy graph coloring approach is used to model any signalized intersection. All possible conflicts between outgoing lanes and the congestion level in lanes at the intersection are presented in this generalized fuzzy graph called the situational graph.…”

Section: The Modeling Layermentioning

confidence: 99%

“…In this perspective, this paper presents a new approach called evolutionary reinforcement learning multi-agents system (ERL-MA) which is an independent multi-agents system composed of two layers. The first one is the modeling layer that uses the information provided by the intersection modeling using generalized fuzzy graph (IMGFG) [17] to understand the junction constraint and complexity. The second one is the decision layer which is composed of two activities: activity per cycle and activity per phase.…”

Section: Introductionmentioning

confidence: 99%

“…Modeling traffic behavior has been for years interesting issue research, traditionally accomplished using a variety of methods. As queuing theory [20], Dotoli and Fanti [21], cell transmission model [22] and intersection graph [17], [23], [24]. Boudaakat et al [17] have introduced the intersection modeling IMGFG, where a generalized fuzzy graph concept with fuzzy vertices and fuzzy edges, through the modeling of an isolated intersection in two stages, steady elements in the first stage which leads to fuzzy graph with crisp vertices and a situational status which leads to a generalized fuzzy graph.…”

Section: Introductionmentioning

confidence: 99%

“…As queuing theory [20], Dotoli and Fanti [21], cell transmission model [22] and intersection graph [17], [23], [24]. Boudaakat et al [17] have introduced the intersection modeling IMGFG, where a generalized fuzzy graph concept with fuzzy vertices and fuzzy edges, through the modeling of an isolated intersection in two stages, steady elements in the first stage which leads to fuzzy graph with crisp vertices and a situational status which leads to a generalized fuzzy graph. In this work, the IMGFG is adopted as the modeling technique because this approach has the potential to present precisely the congestion points and conflicts level.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evolutionary reinforcement learning multi-agents system for intelligent traffic light control: new approach and case of study

Basmassi

Boudaakat²,

Chentoufi³

et al. 2022

IJECE

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<span>Due to the rapid growth of urban vehicles, traffic congestion has become more serious. The signalized intersections are used all over the world and still established in the new construction. This paper proposes a self-adapted approach, called evolutionary reinforcement learning multi-agents system (ERL-MA), which combines computational intelligence and machine learning. The concept of this work is to build an intelligent agent capable of developing senior skills to manage the traffic light control system at any type of junction, using two powerful tools: learning from the confronted experience and the assumption using the randomization concept. The ERL-MA is an independent multi-agents system composed of two layers: the modeling and the decision layers. The modeling layer uses the intersection modeling using generalized fuzzy graph technique. The decision layer uses two methods: the novel greedy genetic algorithm (NGGA), and the Q-learning. In the Q-learning method, a multi Q-tables strategy and a new reward formula are proposed. The experiments used in this work relied on a real case of study with a simulation of one-hour scenario at Pasubio area in Italy. The obtained results show that the ERL-MA system succeeds to achieve competitive results comparing to urban traffic optimization by integrated automation (UTOPIA) system using different metrics.</span>

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Section: The Modeling Layermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%