# Towards Data-Driven Multilinear Metro Maps

## TL;DR

This paper introduces a data-driven method for creating multilinear metro maps with arbitrary slopes, extending traditional octolinear layouts by using mixed-integer linear programming and clustering techniques.

## Contribution

It adapts existing octolinear algorithms to support any set of orientations and proposes a data-driven approach to select these orientations based on input data.

## Key findings

- Successfully extended metro map algorithms to arbitrary slopes.
- Demonstrated effectiveness on real-world metro map examples.
- Provided a method to optimize orientation sets for better map schematization.

## Abstract

Traditionally, most schematic metro maps in practice as well as metro map layout algorithms adhere to an octolinear layout style with all paths composed of horizontal, vertical, and 45{\deg}-diagonal edges. Despite growing interest in more general multilinear metro maps, generic algorithms to draw metro maps based on a system of $k \ge 2$ not necessarily equidistant slopes have not been investigated thoroughly. In this paper we present and implement an adaptation of the octolinear mixed-integer linear programming approach of N\"ollenburg and Wolff (2011) that can draw metro maps schematized to any set C of arbitrary orientations. We further present a data-driven approach to determine a suitable set C by either detecting the best rotation of an equidistant orientation system or by clustering the input edge orientations using a k-means algorithm. We demonstrate the new possibilities of our method using several real-world examples.