909 Parallelization of Intelligent Multi-Agent based Traffic and Environment Simulator MATES

敏浩, 小橋; 信太郎, 文屋; 忍, 吉村

doi:10.1299/jsmecmd.2008.21.75

Cited by 2 publications

(3 citation statements)

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“…Even in the free-flow state, the computational time for the simulation predominates in the OD estimation. Our work supports that 75% of the computation time is spent in routing operation through a simulation run [36]. By introducing dynamic rerouting into the simulator, the computational time for the simulation is expected to be several times longer than in the Control case.…”

Section: Computational Efficiency and Scalabilitysupporting

confidence: 69%

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting

Abe¹,

Fujii²,

Yoshimura³

2023

Computer Modeling in Engineering &Amp; Sciences

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Traffic simulators are utilized to solve a variety of traffic-related problems. For such simulators, origin-destination (OD) traffic volumes as mobility demands are required to input, and we need to estimate them. The authors regard an OD estimation as a bi-level programming problem, and apply a microscopic traffic simulation model to it. However, the simulation trials can be computationally expensive if full dynamic rerouting is allowed, when employing multi-agent-based models in the estimation process. This paper proposes an efficient OD estimation method using a multi-agent-based simulator with restricted dynamic rerouting to reduce the computational load. Even though, in the case of large traffic demand, the restriction on dynamic rerouting can result in heavier congestion. The authors resolve this problem by introducing constraints of the bi-level programming problem depending on link congestion. Test results show that the accuracy of the link traffic volume reproduced with the proposed method is virtually identical to that of existing methods but that the proposed method is more computationally efficient in a wide-range or high-demand context.

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Section: Computational Efficiency and Scalabilitysupporting

confidence: 69%

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting

Abe¹,

Fujii²,

Yoshimura³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…However, further investigation is needed to determine the optimal recalculation intervals for minimizing the dynamic link costs. Additional studies could also implement and evaluate the proposed method in parallelized MATES, such as reported by Kohashi et al [3].…”

Section: Resultsmentioning

confidence: 99%

“…Thus, in this paper we focus on the route search function in a microscopic traffic simulator. Specifically, we present the practical implementation of a simplified network approach and evaluation to solve complicated and dynamic problems over a large area in a system called ADVENTURE_Mates (Multi-Agent based Traffic and Environment Simulator), developed by the authors [1,2,3,12].…”

Section: Introductionmentioning

confidence: 99%

Speedup of Dynamic Route Search for Large-scale Microscopic Traffic Simulation

Mita

Uchida

Fujii

et al. 2018

JASSE

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The current approach to solving large-scale traffic problems is with the simulation of microscopic vehicle behaviors. However, few simulators are capable of conducting such simulations due to the high computational cost. In this study, we propose a new route search method using a simplified network to achieve speedup. We developed a practical implementation for a microscopic traffic simulator and applied the proposed method to a real road network in Okayama city under a dynamic routing condition. The results show that the proposed method reduces the number of links and nodes for route searches by 80% and the route search execution time by 98%.

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909 Parallelization of Intelligent Multi-Agent based Traffic and Environment Simulator MATES

Cited by 2 publications

References 0 publications

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting

Efficient Origin-Destination Estimation Using Microscopic Traffic Simulation with Restricted Rerouting

Speedup of Dynamic Route Search for Large-scale Microscopic Traffic Simulation

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