Multimodale urbane Mobilitäts- & E-Flotten-Simulation
MobilitySimulator modelliert die Bewegung von Millionen autonomer Fahrzeuge, E-Busse, Radfahrer und Fußgänger in Ballungsräumen. Durch Kopplung von Verkehrsphysik mit Stromnetzmodellen wird Verkehrsplanung optimiert.
Mobility Simulation Core Zonal Architecture
Central Controller: Mobility Simulation Core — Multi-Agent Urban Platform
Zonal Control Units & TSN Ring
- Agent-Based Trip Demand Generator
- Microscopic SUMO / MATSim Traffic Solver
- Electrical Distribution Grid Power Flow
- Dynamic Public Transit Optimizer
- Urban Policy & Geofencing Scenario Evaluator
Simulation der doppelten Auswirkungen von Flotternelektrifizierung und Autonomie auf Städte
Der Einsatz von 10.000 autonomen Robotaxis in einer Großstadt verändert Verkehr, Parkraumbedarf und Stromnetze grundlegend. MobilitySimulator führt agentenbasierte Simulationen für ganze Metropolen durch und modelliert Fahrten, Akkustände und Lade-Wartezeiten.
Core Engineering Areas
- Agent-Based Activity Simulation — Simulating individual commuter choices: home departure times, modal choices (car, transit, autonomous shuttle), and destination routing based on dynamic utility functions.
- Traffic and Power Grid Coupling — Connecting vehicle battery drain telemetry directly into electrical distribution substation load models to evaluate charging congestion.
- Autonomous Robotaxi Fleet Dispatch — Applying centralized and distributed matching algorithms to assign available autonomous vehicles to incoming ride requests with minimal wait times.
Technical Articles
Related Cluster Architecture Specifications
- AutomotiveSDV — Full SDV Stack & Reference Architecture
- A2HCard — Agent-to-Human (A2H) Interface Cards & In-Cabin Autonomous Interaction Manifests
- A2HumanCard — Standardized In-Vehicle AI Agent Capability Discovery, Trust & Authorization Cards
- AIDrivenSynBio — AI-Driven Synthetic Biology for Automotive In-Cabin Air Purification, Bio-Engineered Pathogen Scavengers & Molecular Biosensors