Self-Balancing Decentralized Distributed Platform for Urban Traffic Simulation

Microscopic traffic simulation is the most accurate tool for predictive analytics in urban environments. However, the amount of workload (i.e., cars simulated simultaneously) can be challenging for classical systems, particularly for scenarios requiring faster than real-time processing (e.g., for em...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2017-05, Vol.18 (5), p.1190-1197
Main Authors: Bragard, Quentin, Ventresque, Anthony, Murphy, Liam
Format: Article
Language:English
Subjects:
Analytics
Computation
Computational modeling
Computer simulation
Distributed simulation
dynamic load-balancing
Emergency management
Heuristic algorithms
Load modeling
Microscopy
Simulation
Synchronization
time-stepped simulation
Traffic management
Urban environments
Vehicle dynamics
Vehicles
Workload
Workloads
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Summary:Microscopic traffic simulation is the most accurate tool for predictive analytics in urban environments. However, the amount of workload (i.e., cars simulated simultaneously) can be challenging for classical systems, particularly for scenarios requiring faster than real-time processing (e.g., for emergency units having to make quick decisions on traffic management). This challenge can be tackled with distributed simulations by sharing the load between simulation engines running on different computing nodes, hence balancing the processing power required. This paper studies the performance of dSUMO, i.e., a distributed microscopic traffic simulator. dSUMO is fully decentralized and can dynamically balance the workload between its computing nodes, hence showing important improvements against classical, centralized and not dynamic, solutions.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2016.2603171