How is3unicologne/urban-mobility-simulation works
Urban-mobility-simulation is a discrete-event platform for studying ride-hailing, shared mobility, public transport, charging infrastructure, and mobility markets. A configured run builds its simulation context and world, creates demand, then lets users seek service alternatives that operators search and allocate; vehicles execute trips and operations while metrics track outcomes. The graph preserves documented service and charging branches. Edges are limited to relationships visible in excerpts or the README; unsampled wiring is not inferred.
Main entry points: simulation.py Simulation Runner · operator.py Service Operators · search_engine.py Search Engines · world.py World
Components 25
Run Core
- Scenario Configuration
simulation/helper/configuration/configuration.py - Simulation Runner
simulation/simulation.py - Simulation Context
simulation/context/simulation_context.py
Demand And Users
- World
simulation/world/world.py - Demand Factory
simulation/world/demand/demand_factory.py - User Agent
simulation/agent/user/user.py
Mobility Services
- Transport Request
simulation/operator/data_transfer_object/transport_request.py - Mobility Search
simulation/world/mobility_search_engine.py - Service Operators
simulation/operator/operator.py - Ride-Hailing Service
simulation/operator/ride_hailing_operator.py - Shared Mobility Service
simulation/operator/shared_mobility_operator.py - Public Transport
simulation/operator/public_transit_operator.py - Search Engines
simulation/operator/search_engine.py - Trip Alternatives
simulation/operator/data_transfer_object/transportation_alternative.py - Vehicle Allocation
simulation/operator/vehicle_allocation_engine.py
Fleet And Operations
- Vehicle Fleets
simulation/operator/vehicle_fleet.py - Vehicles
simulation/vehicle/vehicle.py - Routes And Transit
simulation/infrastructure/route.py - Charging Infrastructure
simulation/infrastructure/charging_stations/charging_infastructure.py - Operations Management
simulation/operator/operations_management/operations.py - Mobility Markets
simulation/world/markets/auction.py
Metrics And Visualization
- Metrics Tracking
simulation/metrics/metrics_tracker.py - Live Visualization
visualization/visualization_manager.py - Reinforcement Learning
rl/env.py
Other components
- Researcher
Connections 27
- Researcher → Scenario Configuration: configures
README.md - Scenario Configuration → Simulation Runner: configures
simulation/simulation.py - Simulation Runner → Simulation Context: creates
simulation/simulation.py - Simulation Runner → World: creates
simulation/simulation.py - Simulation Runner → Demand Factory: starts demand
simulation/simulation.py - Simulation Runner → Metrics Tracking: creates
simulation/simulation.py - Simulation Context → World: simulation state
- Demand Factory → World: generates demand
- World → User Agent: serves users
- User Agent → Transport Request: creates request
simulation/agent/user/user.py - Mobility Search → Service Operators: queries operators
simulation/world/mobility_search_engine.py - Service Operators → Search Engines: selects engine
simulation/operator/search_engine.py - Search Engines → Trip Alternatives: returns options
simulation/operator/ride_hailing_search_engine.py - Service Operators → Ride-Hailing Service: specializes as
- Service Operators → Shared Mobility Service: specializes as
- Service Operators → Public Transport: specializes as
- Ride-Hailing Service → Search Engines: uses search
- Shared Mobility Service → Search Engines: uses search
- Public Transport → Search Engines: uses search
- Vehicle Allocation → Vehicle Fleets: allocates vehicles
- Vehicle Fleets → Vehicles: registers vehicle
simulation/vehicle/vehicle.py - Vehicles → Routes And Transit: traverses route
simulation/vehicle/vehicle.py - Operations Management → Vehicle Fleets: manages fleet
- Operations Management → Charging Infrastructure: manages charging
- Mobility Markets → Service Operators: coordinates services
- Metrics Tracking → Live Visualization: feeds dashboard
visualization/visualization_manager.py - Reinforcement Learning → Simulation Runner: controls simulation
rl/env.py