v2rmp 0.4.0

rmpca — Route Optimization TUI & Agent Engine
Documentation

rmpca - Route Optimization TUI & Agent Engine

Crates.io Documentation License

A powerful Terminal User Interface (TUI) and Command-Line Interface (CLI) for route optimization using both the Chinese Postman Problem (CPP) and multi-vehicle Vehicle Routing Problem (VRP) algorithms. Extract road networks from Overture Maps or OpenStreetMap, compile them into efficient binary formats, and optimize routes with turn penalties and depot constraints.

Features

  • 🖥️ Interactive TUI: Beautiful terminal interface built with ratatui
  • 🤖 Agent-First CLI: Purpose-built agent command for JSON-task automation
  • 🗺️ Data Extraction: Extract road networks from Overture Maps S3 (Parquet) or OpenStreetMap PBF files
  • 🧹 GeoJSON Cleaning: Repair geometries, deduplicate edges, and optimize graph topology
  • 🔧 Binary Compilation: Convert GeoJSON to optimized .rmp binary format with CRC32 integrity checking
  • 🚗 Route Optimization (CPP): Solve the Chinese Postman Problem with turn penalties and oneway support
  • 🚛 VRP Engine: Multi-vehicle routing with multiple solvers:
    • Clarke-Wright Savings: Classic heuristic for capacity and distance
    • Sweep Algorithm: Geometric partitioning
    • Local Search: 2-Opt and Or-Opt path improvements
  • 📁 Resource Discovery: list command to discover maps and routes programmatically
  • ⚡ Asynchronous Runtime: Fully non-blocking I/O using tokio for high-performance extraction and processing

Installation

From crates.io

cargo install v2rmp

From source

git clone https://github.com/spacialglaciercom-lab/v2rmp.git
cd v2rmp
cargo build --release

Modes of Operation

1. Interactive TUI

Launch the full interface by running rmpca without arguments.

rmpca

2. Command-Line Interface (CLI)

Use rmpca <COMMAND> for scripts, agents, and batch processing. All commands support a --json flag for structured output.

# Discover resources
rmpca list maps --json
rmpca list routes --json

# Run a task via Agent (JSON payload)
rmpca agent --task task.json --json

# Multi-vehicle VRP
rmpca vrp -i map.rmp --vehicles 5 --solver clarke-wright --json

# Single-vehicle CPP Optimization (Outputs GPX)
rmpca optimize -i map.rmp -o route.gpx --depot "40.71,-74.01"

CLI Commands

Command Description
extract Fetch data from Overture Maps or OSM
clean Repair and simplify GeoJSON networks
compile Convert GeoJSON to efficient .rmp binary
optimize Single-vehicle CPP route optimization
vrp Multi-vehicle Routing Problem solver
agent Execute complex tasks from JSON payloads
list Enumerate maps, routes, and solvers
pipeline Run extract → clean → compile → optimize

AI Agent Task Format

The agent command consumes a JSON payload, allowing agents to trigger complex workflows without manual flag management.

{
  "type": "optimize",
  "input": "manhattan.rmp",
  "output": "delivery_route.gpx",
  "num_vehicles": 1,
  "solver_id": "clarke_wright",
  "oneway": "respect",
  "left_penalty": 1.5,
  "depot": [40.7128, -74.0060]
}

Usage (TUI)

1. Extract Road Network

  • Navigate to Extract Data view
  • Set bounding box (format: min_lat,min_lon,max_lat,max_lon)
  • Toggle between OSM and Overture sources
  • Output: extract_YYYYMMDD_HHMMSS.geojson

2. Compile to Binary

  • Navigate to Compile Map view
  • Set input GeoJSON file path
  • Output: .rmp binary file

3. Browse & Optimize

  • Use Browse Cached Maps to select a compiled map
  • Configure turn penalties and depot in the Optimize Route view
  • Run optimization to generate a GPX/GeoJSON route

Binary Format (.rmp)

The .rmp format is a compact binary representation optimized for graph traversals:

[4 bytes]  Magic "RMP1"
[4 bytes]  Node count (u32 LE)
[4 bytes]  Edge count (u32 LE)
[N * 16]   Nodes: lat(f64) + lon(f64)
[E * 17]   Edges: from(u32) + to(u32) + weight_m(f64) + oneway(u8)
[4 bytes]  CRC32 checksum

Library Usage

use v2rmp::core::{extract, compile, optimize, vrp};

// VRP Solver Example
let vrp_input = vrp::VRPSolverInput {
    stops: my_stops,
    vehicles: 3,
    ..Default::default()
};
let solver = vrp::get_solver("clarke-wright")?;
let output = solver.solve(&vrp_input).await?;

Data Sources

Overture Maps

  • Status: ✅ Production Ready
  • Source: AWS S3 public bucket (overturemaps-us-west-2)
  • Format: Parquet/WKB via reqwest and parquet crates

OpenStreetMap

  • Status: ✅ Production Ready
  • Source: Local .osm.pbf files
  • Parser: osmpbf crate

Performance

  • Snapping: 1-meter precision node deduplication
  • Compression: ~90% reduction vs GeoJSON
  • Speed: Optimization on 10,000+ edges in <500ms

Roadmap

  • v0.3.0: VRP Engine Integration (Clarke-Wright, Sweep, 2-Opt)
  • v0.3.5: Agent & List commands for machine-to-machine workflows
  • v0.4.0: Fully Async pipeline and OSM PBF support
  • v0.5.0: Time Window support (VRPTW) and 3D terrain-aware routing

License

Licensed under either of:

at your option.