multimodalrouter 0.1.4

A graph-based routing library for dynamic routing.

Multi Modal Router

The Multi Modal Router is a graph-based routing engine that allows you to build and query any hub-based network. It supports multiple transport modes like driving, flying, or shipping, and lets you optimize routes by distance, time, or custom metrics. It can be expanded to any n-dimensional space making it versatile in any coordinate space

NEWS: v0.1.3 now on pypi (installation guide)

NOTE: This project is a work in progress and features might be added and or changed

In depth Documentation

installation guide

graph module documentation

code examples

command line interface documentation

utilities documentation

Features

Building Freedom / Unlimited Usecases

The graph can be build from any data aslong as the required fields are present (example). Whether your data contains real world places or you are working in a more abstract spaces with special coordinates and distance metrics the graph will behave the same (with minor limitations due to dynamic distance calculation, but not a problem when distances are already precomputed. solutions).

Example Usecases

• real world flight router

  • uses data with real flight data and actuall airport coordinates
  • builds a graph with airport Hubs
  • connects airports based on flight routes
  • finds the shortest flights or multi leg routes to get from A to B
  • simple example implementation here

• social relation ship graph

  • uses user data like a social network where users are connected through others via a group of other users
  • builds a graph with users as Hubs
  • connects users based on know interactions or any other connection meric
  • finds users that are likely to share; interests, friends, a social circle, etc.

• coordinate based game AI and pathfinding

  • uses a predefined path network (e.g. a simple maze)
  • builds the garph representation of the network
  • finds the shortest way to get from any point A to any other point B in the network
  • you can checkout a simple example implementation for a maze pathfinder here

Important considerations for your usecase

Depending on your usecase and datasets some features may not be usable see solutions below

potential problems based on use case

Please check your data for the following

distance present	coordinate format	unusable features	special considerations
YES	degrees	None	None
YES	not degrees	runtime distance calculations	set drivingEnabled = False or do this
NO	degrees	None	distances must be calculated when preprocessing
NO	not degrees	ALL	U cant build the graph with neither distances or supported coordinates! solution

example dataframe with the required fields

License

see here