Bike Renting Data Analysis: The Case of Dublin City

TL;DR

This paper analyzes Dublin's bike rental data to identify usage patterns at key stations, aiding resource planning and bike rebalancing strategies for improved urban transportation efficiency.

Contribution

It introduces a data-driven approach to analyze bike usage patterns and validates these patterns with new data, enhancing resource management in bike-sharing schemes.

Findings

Identified usage patterns at busy and quiet stations

Validated patterns with new data for consistency

Provided insights for better bike rebalancing strategies

Abstract

Public bike renting is more and more popular in cities to incentivise a reduction in car journeys and to boost the use of green transportation alternatives. One of the challenges of this application is to effectively plan the resources usage. This paper presents some analysis of Dublin bike renting scheme based on statistics and data mining. It provides available bike patterns at the most interesting bike stations, that is, the busiest and the quietest stations. Consistency checking with new data reinforces confidence in the patterns obtained. Identifying available bike patterns helps to better address user needs such as organising the rebalancing of the bike numbers between stations in advance of demand.