Multi-Modal Urban Mobility & EV Fleet Simulation

MobilitySimulator models the movement of millions of autonomous vehicles, electric buses, cyclists, and pedestrians across metropolitan road networks. By coupling microscopic traffic physics with electrical distribution grid models, it optimizes urban transport planning.

Simulating the Twin Impacts of Fleet Electrification and Autonomy on Cities

Deploying 10,000 autonomous robotaxis in a major city fundamentally alters traffic congestion, parking demand, and local electricity distribution feeders. MobilitySimulator runs agent-based simulations spanning entire metropolitan regions, modeling individual trip decisions, battery discharge rates, and dynamic charging queue times.

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

• Coupling Multi-Agent Traffic (MATSim) with Electrical Grid (OpenDSS) Simulators
• Bipartite Graph Matching and Fleet Rebalancing for Autonomous Robotaxis

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