Scenario Analysis for Shared Autonomous Driving Fleets
summary
A SUMO-based Scenario Builder simulates morning and evening demand on a Mannheim map converted from OpenStreetMap (OSM Web Wizard) using city traffic counters and real carsharing stations turned into parking lots, then runs iterative simulations over fleet sizes to report passenger wait times, CO2/energy and per-km operator costs. Routing options include simple single-passenger, look-ahead routing with AI prediction, and shared rides with lateness parameters; an example run shows 300 requests with 34 fulfilled during a live demo and the code is on GitHub.
AI-written summary · about