# Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System

arXiv: https://arxiv.org/abs/2404.01320 · HTML: https://mlsanity.com/paper/2404.01320/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach](https://mlsanity.com/paper/1712.04997/) (2017)
- [Parallel Heuristics for Scalable Community Detection](https://mlsanity.com/paper/1410.1237/) (2014)
- [Structural Textile Pattern Recognition and Processing Based on Hypergraphs](https://mlsanity.com/paper/2103.11271/) (2021)
- [A Similarity Measure for Weaving Patterns in Textiles](https://mlsanity.com/paper/1810.04604/) (2018)
- [Bike Renting Data Analysis: The Case of Dublin City](https://mlsanity.com/paper/1704.06802/) (2017)
- [On community structure validation in real networks](https://mlsanity.com/paper/1710.06611/) (2017)
- [Community Structures, Interactions and Dynamics in London's Bicycle Sharing Network](https://mlsanity.com/paper/1804.05584/) (2018)
- [Opinion Spam Recognition Method for Online Reviews using Ontological Features](https://mlsanity.com/paper/1807.11024/) (2018)

## Abstract (arXiv)

Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency.