Towards an Multilevel Agent-based Model for Traffic Simulation

Tchappi, Igor; Kamla, Vivient Corneille; Galland, Stéphane; Kamgang, Jean Claude

doi:10.1016/j.procs.2017.05.416

Cited by 29 publications

(21 citation statements)

References 8 publications

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“…They are considered the most accurate and realistic. However, this representation has some limitations especially when upscaling: the computation cost indeed evolves rapidly with the number of individuals [27,28]. These models are also difficult to calibrate because of their large number of parameters.…”

Section: Related Workmentioning

confidence: 99%

Exploration of Model Coupling Strategies in a Hybrid Agent-Based Traffic Simulation

Erdelyi

Amblard

Gaudou

et al. 2022

Multi-Agent-Based Simulation XXII

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Traffic simulation is a tool used by urban planners to assess the impact of new urban designs and public policies on mobility. Over the years, numerous traffic models have been proposed, each model offering different levels of details and performances. Multi-level model coupling is an interesting approach to combine the advantages of complementary representations while limiting their drawbacks. In this paper, we design and evaluate the performances of hybrid traffic models combining a microscopic model (IDM) with a mesoscopic model (event-driven and queue-based). The results show that microscopic models have more diversity in terms of behaviors but reduce the vehicle average speed and mesoscopic models are more efficient in terms of computational time but display a higher vehicle speed. Their hybridization then enables to find a balance between scalability and the variety of the observed behaviors.

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Section: Related Workmentioning

confidence: 99%

Exploration of Model Coupling Strategies in a Hybrid Agent-Based Traffic Simulation

Erdelyi

Amblard

Gaudou

et al. 2022

Multi-Agent-Based Simulation XXII

View full text Add to dashboard Cite

show abstract

“…They define a hierarchy between the simulated elements (creating so called "holonic" systems), which allows for aggregating them at will, and to create the "best" scale depending on the context. The work reported in [21] is based on the same holonic idea but adapts this concept to multi-agent vehicle simulations. The authors in [22,23] propose a method to aggregate and disaggregate agents of the simulation in groups such that the size of the groups and the level of aggregation can be chosen during the simulation.…”

Section: Literature Review 21 Scales In Mobility Simulationsmentioning

confidence: 99%

A Middleware-Based Approach for Multi-Scale Mobility Simulation

et al. 2021

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Modeling and simulation play an important role in transportation networks analysis. In the literature, authors have proposed many traffic and mobility simulations, with different features and corresponding to different contexts and objectives. They notably consider different scales of simulations. The scales refer to the represented entities, as well as to the space and the time representation of the transportation environment. However, we often need to represent different scales in the same simulation, for instance to represent a neighborhood interacting with a wider region. In this paper, we advocate for the reuse of existing simulations to build a new multi-scale simulation. To do so, we propose a middleware model to couple independent mobility simulations, working at different scales. We consider all the necessary processing and workflow to allow for a coherent orchestration of these simulations. We also propose a prototype implementation of the middleware. The results show that such a middleware is capable of creating a new multi-scale mobility simulation from existing ones, while minimizing the incoherence between them. They also suggest that, to have a maximal benefit from the middleware, existing mobility simulation platforms should allow for an external control of the simulations, allowing for executing a time step several times if necessary.

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“…With the development of traffic flow theories, three main categories of models were formed: a macroscopic approach that treats the traffic flow as a whole [9,10], the microscopic approach that considers individual vehicles and treats interactions between single vehicles [9,11,12], and the mesoscopic approach that combines the vehicle entities' description with the macroscopic behavior [9,13].…”

Section: Traffic Modeling Approachesmentioning

confidence: 99%

An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

Riouali

Benhlima

Bah

2019

JSAN

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The tremendous increase in the urban population highlights the need for more efficient transport systems and techniques to alleviate the increasing number of the resulting traffic-associated problems. Modeling and predicting road traffic flow are a critical part of intelligent transport systems (ITSs). Therefore, their accuracy and efficiency have a direct impact on the overall functioning. In this scope, a new approach for predicting the road traffic flow is proposed that combines the Petri nets model with a dynamic estimation of intersection turning movement counts to ensure a more accurate assessment of its performance. Thus, this manuscript extends our work by introducing a new feature, namely turning movement counts, to attain a better prediction of road traffic flow. A simulation study is conducted to get a better understanding of how predictive models perform in the context of estimating turning movements.

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Towards an Multilevel Agent-based Model for Traffic Simulation

Cited by 29 publications

References 8 publications

Exploration of Model Coupling Strategies in a Hybrid Agent-Based Traffic Simulation

Exploration of Model Coupling Strategies in a Hybrid Agent-Based Traffic Simulation

A Middleware-Based Approach for Multi-Scale Mobility Simulation

An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling

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