Two city rental scheme bikes parked on the path beside Galway bay.; Two city rental scheme bikes parked on the path beside Galway bay.;

Regional Cities Bike Share Scheme, Galway, Limerick and Cork

Helping run Ireland’s regional bike share schemes

Bike share schemes have revolutionised city travel for millions of people worldwide, supporting a transition to more active and sustainable urban transport modes while severely reducing traffic-related emissions and congestion.

Inexpensive, easy-to-use and now reaching critical mass, public bike sharing schemes allow everyone to experience the benefits of this green and healthy mode of transport – at a marginal cost. Evolving tech has been one of the drivers behind the growth of these affordable, active transport schemes: frictionless payments, trackable devices and operational logistics have all helped to get people out of their cars and onto a pair of wheels, freeing up street space to enable public transport flows.

As one of the most successful bike share schemes in Europe, dublinbikes has transformed cycling and public transport in Dublin since its launch in 2009. Similar schemes were then rolled out across regional cities in Ireland, with Arup appointed in 2014 to implement and manage the operations phase of the new Coca-Cola Zero bikes® in Galway, Limerick and Cork.

Successful bike share schemes all share two common factors: ease of use and availability. Predictive analytics have the potential to optimise bike rental schemes, ensuring the right amount of bikes and docking stations are available throughout the city.

Project Summary


3 Irish cities

+700bikes in the scheme

+75docking stations

Cycle schemes: encouraging active lifestyles

Arup was appointed by the National Transport Authority (NTA) in 2012 to progress the bike rental scheme from feasibility stage through to detailed design, including holding a public consultation and managing the procurement of a contractor.

Arup is currently managing the operations of the bike share schemes in Galway, Limerick and Cork.

These self-service bike rental schemes, with an annual and short-term subscription model, allow users to check out a bike from a number of docking stations throughout the city, making it attractive to commuters and tourists alike.

The scheme involved the provision of bicycles and associated on-street infrastructure at strategic locations throughout the three cities, with 195 bicycles at 22 locations in Galway, 215 at 23 docking stations in Limerick and 330 bikes at 32 stations in Cork.


After appointing a contractor, Arup’s consultants managed the construction and handover phases – including site supervision, liaising with all stakeholders, contract management and a significant focus on technology testing and commissioning.

Arup is currently managing the operations phase of the contract, which includes monitoring scheme performance and approving operational payments, liaising with the operator, the NTA, relevant local authorities and any other stakeholders or interested third parties. This also involves collaboration with all relevant parties to resolve any operational issues as they arise on a month-to-month basis.

Machine learning optimises urban bike availability

Bike share schemes are heavily reliant on technology to deliver a seamless, last-mile service: the management and redistribution of bicycles, as well as the proactive maintenance of all equipment. Real-time performance monitoring can be tracked through the website, including user registration; while smartphone apps deliver live parking information.

Predictive analytics are also indispensable for the efficient running of public bike share schemes, enabling users to rent and return bikes with ease. Driven by commuter flows, public bike share scheme usage is typically skewed, moving from residential areas or transport hubs towards business districts in the morning, while reversing the trend after work. Operators must be quick to identify and address these system imbalances throughout the course of the day to better meet demand.

Arup recently carried out a research pilot project, predicting usage with an accuracy rate of 85%, based on data from the bike scheme in Cork. Our engineers developed machine learning models to predict usage patterns across the network. Machine learning is ideally suited to the difficult aspect of rebalancing as it can consider the complex combination of factors that affect usage patterns. This usage prediction model has the potential to help with the management of the scheme; if it is possible to anticipate usage patterns, the operator can redistribute bikes in advance to pre-empt any issues with crowded or empty stations.