Predicting the Location of Bicycle-sharing Stations using OpenStreetMap Data

TL;DR

This paper introduces a novel method using OpenStreetMap data and spatial embedding techniques to predict optimal locations for bicycle-sharing stations, aiding urban planning especially in smaller cities.

Contribution

It presents a transfer learning-based approach leveraging publicly available data to assist in bicycle station placement without requiring specialized local expertise.

Findings

Effective city segmentation into micro-regions using Uber H3 grid

Identification of promising station locations based on transfer learning

Supports urban planners with a data-driven decision mechanism

Abstract

Planning the layout of bicycle-sharing stations is a complex process, especially in cities where bicycle sharing systems are just being implemented. Urban planners often have to make a lot of estimates based on both publicly available data and privately provided data from the administration and then use the Location-Allocation model popular in the field. Many municipalities in smaller cities may have difficulty hiring specialists to carry out such planning. This thesis proposes a new solution to streamline and facilitate the process of such planning by using spatial embedding methods. Based only on publicly available data from OpenStreetMap, and station layouts from 34 cities in Europe, a method has been developed to divide cities into micro-regions using the Uber H3 discrete global grid system and to indicate regions where it is worth placing a station based on existing systems in different cities using transfer learning. The result of the work is a mechanism to support planners in their decision making when planning a station layout with a choice of reference cities.