Network Resource Usage of the German Toll System: Lessons from a Realistic Simulation Model

Using Simulations to Study the Efficiency of Update Control Protocols

“…The outline of the article is as follows: Section 2 provides a brief overview of the simulation model, the changes to the one described in [2] and it introduces two independently developed models of the driving patterns used in the simulation runs. Section 3 explains the major addition to the simulation model: a closedloop control of the update process that can be configured in four distinct modes of operations.…”

“…Non-deterministic, non-linear effects as well as emergent behavior [3] possibly manifest only at the level of the whole system: "Emergent behavior is that which cannot be predicted through analysis at any level simpler than that of the system as a whole" [4]. Taking the existing simulation model of the Toll Collect system [1,2] we use simulations at a scale of 1:1 to determine the behavior and the efficiency of fleet-wide updates for different update control protocols.…”

“…Taking an existing large-scale realistic discrete event simulation model [1,2] of a mobile distributed system with more than 700 000 units we investigate the efficiency of control protocols for the software and data updates of on-board-units (OBUs)…”

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Taking an existing large-scale realistic discrete event simulation model [1,2] of a mobile distributed system with more than 700 000 units we investigate the efficiency of control protocols for the software and data updates of on-board-units (OBUs)

. Adding four different closed-loop control protocols to the existing simulation model we explain and investigate the technical implications of the control protocols: OBUs that allow incoming network connections can be reached at any time (Always-On or Sometimes-On).

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Enhanced simulation performance through parallelization using a synthetic and a real-world simulation model

“…Increasingly the performance in defining and executing models becomes vital due to the increased complexity of systems and processes as well as the customer requirement to create holistic, integrated, high accuracy models up to real world scale. Several use cases of simulations are only possible once the simulation performance is 'good enough': simulating the long-term dynamic behavior, iterative optimization loops, automatic test batteries, real-time models (higher reactivity to market demands and changes), and automated specification and modeling processes (including model transformation/generation) [9]. In this context Parallel Discrete Event Simulation (PDES) [10] helps to provide the necessary simulation performance.…”