geo-infer-transport
Transportation network analysis and traffic modeling. Use when analyzing road networks, simulating traffic (BPR model), forecasting traffic (EWMA), computing emissions, or optimizing transport routes.
Best use case
geo-infer-transport is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Transportation network analysis and traffic modeling. Use when analyzing road networks, simulating traffic (BPR model), forecasting traffic (EWMA), computing emissions, or optimizing transport routes.
Teams using geo-infer-transport should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-TRANSPORT/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-transport Compares
| Feature / Agent | geo-infer-transport | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Transportation network analysis and traffic modeling. Use when analyzing road networks, simulating traffic (BPR model), forecasting traffic (EWMA), computing emissions, or optimizing transport routes.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-TRANSPORT
## Instructions
### Core Capabilities
- **Traffic simulation**: BPR (Bureau of Public Roads) microsimulation with V/C ratios
- **Traffic forecasting**: EWMA model with trend estimation and confidence intervals
- **Network analysis**: Critical link identification, connectivity metrics
- **Emissions**: Transportation emissions calculation (integrates with LOG)
- **Route optimization**: Multi-criteria route selection
### Key Imports
```python
from geo_infer_transport.core.traffic import simulate_traffic, forecast_traffic
from geo_infer_transport.core.network import TransportNetwork
from geo_infer_transport.core.routing import RoutingEngine
```
## Examples
```python
from geo_infer_transport.core.traffic import simulate_traffic
demand = {"matrix": [[100, 50], [30, 80]]}
result = simulate_traffic(demand, simulation_hours=1, time_step_seconds=15)
print(f"Completed trips: {result['statistics']['completed_trips']}")
```
## Guidelines
- `simulate_traffic` uses real BPR delay function (not hardcoded)
- `forecast_traffic` uses EWMA + trend estimation (not fake cyclic variation)
- Emissions bridged from LOG module via optional import
- Test: `uv run python -m pytest GEO-INFER-TRANSPORT/tests/ -v`
### Integrations
- **LOG** → Emissions calculator and route optimization
- **ECON** → Transportation cost for trade flows
- **EMERGENCY** → Evacuation route planning
- **HEALTH** → Healthcare accessibility travel times
- **SPACE** → Road network spatial indexing