RoboUber Fleet Simulation.
A discrete-event taxi fleet simulation for exploring dispatch coordination, agent bidding, and route planning.
ENGINEERING DEEP DIVE
Inside the system.
Dispatch coordination involves competing fares, available taxis, and routes through a changing road graph. A discrete-event simulation provides a controlled environment for exploring those interactions.
Taxi agents use rule-based fare bids, a central dispatcher assigns work, and interchangeable route implementations include A*, Dijkstra, and depth-first search. Headless runs record telemetry, while a Pygame interface visualizes the road network and agents.
Explore fare bidding, dispatch decisions, route implementations, and saved telemetry. The experiments use a simulated road network and rule-based agents; each routing strategy has its own cost treatment.
FROM THE REPOSITORY
What's inside.
- 01
Coordinates rule-based taxi bids through a central fare dispatcher.
- 02
Implements A*, Dijkstra, and depth-first route strategies.
- 03
Includes headless experiments, recorded telemetry, and a Pygame visualizer.
These notes summarize the reviewed implementation and available artifacts. Open the original source for code, documentation, and subsequent changes.
Open the original repository