Exploring the heavy goods vehicle fleet behaviour through simulations: notes from the German toll system

On the Necessity for High-availability Data Center Backends in a Distributed Wireless System

“…Considering the active HGVs the activity follows of course a strong daily and weekly pattern (see e.g. [4]). To determine the buffer size needed to mitigate a given duration of server unavailability we depend on the total amount of toll data gathered by a given OBU over time.…”

“…This system collects the tolls from heavy goods vehicles (HGVs) driving on federal toll roads in Germany (for more details see e.g. [4] and references therein). About 90% of the tolls are collected automatically by the more than 950000 onboard-units deployed in the HGVs (upper part in figure 1), for the remainder the users choose either a manual log-on at a toll station terminal or the internet log-on to pay the tolls for a planned route in advance.…”

Modeling Regional Reliability of 2G, 3G, and 4G Mobile Data Networks and Its Effect on the German Automatic Tolling System

“…x Driving Patterns: The transition between regions is included. Previously the simulation model used to generate the driving patterns created only points in time (when the HGV was powered on or off, passed a section on the toll road signified by a toll event) and did not use any geographic information [16], [17]. For the current work we extended the driving patterns by adding regions (corresponding to the regions served by a SGSN or SGW of a mobile data network operator).…”

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

“…To that extent the simulation model includes the most important processes (receiving toll data from the OBUs and sending updates to the OBUs) in a realistic way at a scale of 1:1. The dynamic behavior -with a strong daily and weekly rhythm -the predictions of the simulation model can and must be verified against data from the real-world system [22,23] and typically show a good correlation [24].…”