Current Trends in Road Traffic Network Division for Distributed or Parallel Road Traffic Simulation

Potužák, Tomáš

doi:10.1109/ds-rt55542.2022.9932112

Cited by 2 publications

(1 citation statement)

References 63 publications

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“…Speeding up large-scale microscopic SUMO [28] simulation has been tackled by dividing the network into sub-networks for parallel processing [29], [30], [31] or utilizing simulation of varying simulation scale [32]. In an EGO-centric simulation, detailed (microscopic) simulation is only necessary for the vicinity of the EGO vehicle, while the rest of the network can be modeled in a less detailed (mesoscopic) fashion.…”

Section: Introductionmentioning

confidence: 99%

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

2023

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Simulating automotive functions that rely on interaction with other vehicles (e.g., perceptionbased control or algorithms relying on inter-vehicular communication) created a demand for traffic simulation in the automotive field as well. Large-scale traffic simulation can be used to generate long, synthetic drive-cycles for EGO vehicles with realistic traffic. An EGO vehicle is defined as the vehicle the scenario revolves around, presumably running a control algorithm to be tested. On the other hand, simulating an entire district or city with thousands of vehicles present is superfluous and comes with a heavy computational burden while only the vicinity of the EGO vehicle is relevant. On the other hand, major traffic patterns can that could still influence the nearby traffic (e.g., traffic disruptions farther away) but can be simulated with lesser accuracy. Thus, simulation accuracy far from the EGO vehicle can be traded for simulation speed. This paper achieves this trade-off by co-simulating SUMO in microscopic and mesoscopic modes using Libsumo API. Microsimulated traffic is continuously spawned in an EGO-centered sub-network based on traffic states in the mesoscopic simulation. Simulation results in large urban scenarios suggest that the behavior of the EGO vehicle in terms of velocity distribution, headway distribution, and lane changes accurately matches pure microsimulation while simulation speed increased by 3 − 10 times. This result assumes linear time complexity control algorithms with respect to the vehicle number and a single EGO vehicle. Reducing the number of microsimulated vehicles with co-simulation yields even larger simulation speed gains for more computationally complex algorithms. The aggregate (macroscopic) traffic parameters match for both the micro-, meso-, and co-simulated cases. Thus coupling the two simulators does not distort the mesoscopic simulation.

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Section: Introductionmentioning

confidence: 99%

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

2023

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ENHANCE: Multilevel Heterogeneous Performance-Aware Re-Partitioning Algorithm For Microscopic Vehicle Traffic Simulation

Siguenza-Torres,

Wieder,

Meng

et al. 2024

ACM Trans. Model. Comput. Simul.

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Driven by our work on a large-scale distributed microscopic road traffic simulator, we present ENHANCE, a novel re-partitioning approach that allows incorporating fine-grained simulator-specific cost models into the partitioning process to account for the actual performance characteristics of the simulator. The use of explicit cost models enables partitioning for heterogeneous resources, which are a common occurrence in cloud deployments. Importantly, ENHANCE can be used in conjunction with other partitioning approaches by further enhancing partitions according to provided cost models. We demonstrate the benefits of our approach in an experimental evaluation showing performance improvements of up to 29% against METIS under heterogeneous conditions. Taking a different perspective, the partitioning produced by ENHANCE can provide similar performance as METIS, but using up to 20% fewer resources.

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Current Trends in Road Traffic Network Division for Distributed or Parallel Road Traffic Simulation

Cited by 2 publications

References 63 publications

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

EGO-Centric, Multi-Scale Co-Simulation to Tackle Large Urban Traffic Scenarios

ENHANCE: Multilevel Heterogeneous Performance-Aware Re-Partitioning Algorithm For Microscopic Vehicle Traffic Simulation

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