Using common graphics hardware for multi-agent traffic simulation with CUDA

Strippgen, David; Nagel, Kai

doi:10.4108/icst.simutools2009.5666

Cited by 33 publications

(17 citation statements)

References 14 publications

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“…Multi-agent simulations have also been realized on GPUs (Graphics Processing Unit) using the CUDA (Compute Unified Device Architecture) framework [30,31]. The authors of [6] present a new technique to simulate agent-based models on GPUs.…”

Section: Related Workmentioning

confidence: 99%

“…Since then, extensions for this model as well as other approaches have been published such as two-lane traffic models [17,22], different driver behaviors [24] or 4-Way intersections [19]. In contrast to our work, [31] uses graphics processing units (GPU) and NVIDIAs CUDA framework to accelerate traffic simulation. As a generalization they also use the term agent.…”

Section: Nagel-schreckenberg Algorithmmentioning

confidence: 99%

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Efficient Traffic Simulation Using Agents within the Global Cellular Automata Model

Schäck

Hoffmann

Heenes

2011

IJNC

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We present a mapping of the well known Nagel-Schreckenberg algorithm for traffic simulation onto the Global Cellular Automata (GCA) model using agents. The GCA model consists of a collection of cells which change their states synchronously depending on the states of their neighbors like in the classical CA (Cellular Automata) model. In contrast to the CA model the neighbors can be freely and dynamically selected at runtime. The vehicles are considered as agents that are modeled as GCA cells. An agent is linked to its agent in front, and an empty cell is linked to its agent behind. In the current generation t the position of an agent is already computed for the generation t+2. Thereby the agents movements and all cell updates can directly be calculated as defined by the cell rule. No searching of specific cells during the computation is necessary. Compared to an optimized CA algorithm (with searching for agents) the GCA algorithm executes significantly faster, especially for low traffic densities and high vehicle speeds. Simulating one lane with a density of 10% on an FPGA multiprocessor system resulted in a speed-up (measured in clock ticks) of 14.75 for a system with 16 NIOS II processors.

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Section: Related Workmentioning

confidence: 99%

Section: Nagel-schreckenberg Algorithmmentioning

confidence: 99%

Efficient Traffic Simulation Using Agents within the Global Cellular Automata Model

Schäck

Hoffmann

Heenes

2011

IJNC

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“…Strippgen has verified the effectiveness of GPU for the traffic micro-simulation in the case that all the vehicles in the simulation move with a constant speed [10,11].…”

mentioning

confidence: 98%

A GPU based trafficparallel simulation module of artificial transportation systems

Wang

Shen

2012

Proceedings of 2012 IEEE International Conference on Service Operations and Logistics, and Informatics

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Traffic micro-simulation is an important tool in the Intelligent Transportation Systems (ITS) research. In the micro simulation, a bottom up system can be built up by the interactions of vehicle agents, road agents, traffic lights agents, etc. The Artificial societies, Computational experiments, and Parallel execution (ACP) approach suggests integrating other metropolitan systems such as logistic, infrastructure, legal and regulatory, weather and environmental systems to build an Artificial Transportation System (ATS) to help solve ITSproblems. This is reasonable as the transportation system is complex that is affected by many systems interacting with each other. However, there is a challenge that the computing burden can be very heavy as there can be many agents of different kinds interacting in parallel in ATS. In recent years, the Graphics Processing Units (GPUs) have been applied successfully in many areas for parallel computing. Compared with the traditional CPU cluster, GPU has an obvious advantage of low cost of hardware and electricity consumption. In this paper, we build a parallel traffic simulation module of ATS with GPU. The simulation results are reasonable and a maximum speedup factor of 105 is obtained compared with the CPU implementations.

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“…Bitonic sort has also been implemented in [13] using Imagine stream processor. An overview of sorting queues for traffic simulations is covered in [14]. Their approach is to study the behavior of relatively large groups of transport agents.…”

Section: Related Workmentioning

confidence: 99%