From Tap to Track

/ product design

Reimagining an intelligent Luas ticketing experience

PoC for

Luas Tram System, Dublin

Industry

Automotive & Transportation

Duration

4 weeks

Scope of work

Research, Service Design, Product Design, Design System

Deliverables

Insights & pain-points, Archetypes, As-is journey maps, Service blueprint, Product design artefacts

challenge

Luas is the tram system in Dublin, Ireland and plays a vital role in the city’s daily commute and tourism. However, the current ticket-buying experience feels outdated and confusing, particularly for first-time users and visitors.

Poorly designed ticket machine interfaces and unclear signage often leave passengers unsure of what to do, leading them to seek help from others, which creates delays and crowding.

The challenge was to create a smarter, more intuitive ticketing system that makes travel seamless and stress-free for all kinds of commuters.

The aim was to identify the different pain points in the current experience and to understand how AI can address the gaps and enhance the future experience.

approach

  • Desk Research

  • Ethnographic Field Study

  • Persona/ Archetype Mapping

  • As-is Journey Blueprinting

  • Pain Point Analysis

  • Future-state Blueprinting

  • Feature Identification

  • Wireframes - Low/High Fidelity

  • Rapid Prototyping

  • High Fidelity Prototype

  • Design System

solution

A smarter, more intuitive ticketing experience powered by AI, designed to streamline purchasing, reduce queues, and personalise travel for Dublin’s commuters and visitors.

The new journey supports scalable growth, decreases reliance on in-person support, and significantly improves ticket purchase success rates — ultimately making public transport more accessible, efficient, and user-friendly.

  • End-to-End Service Design Approach

  • Intuitive, simple and clean user interface powered by AI

  • Personalisation in responses

28

days

349

variables

690

components

18

text styles

2

brand themes

1

design system

problem statement

How might we improve the experience of the using the ticketing machine at Luas stops in Dublin so that the users find it easier and faster to purchase tickets for the tram, reducing pressure on support teams and ultimately making Dublin’s public transport smarter, seamless, and scalable?

personal note:

I chose to work on this proof of concept because of a few personal experiences. I live close to the Luas Green Line in Dublin and use it almost every day. As a regular commuter, I rely on my Leap Card and hardly ever buy physical tickets from the machines. That familiarity and the struggle from users while using the machines made me curious — could the ticketing experience be simpler, warmer, and more intuitive for everyone? I talk a bit more about what motivated me in the section on service design thinking for this case study.

When I started reimagining the purchasing experience for Luas ticketing machine, my goal was to design something that truly understands its users — an experience that feels empathetic, intuitive, and encouraging. It should be able to sense what a person needs quickly and respond efficiently, without making them feel rushed or confused.

I also drew from my experiences growing up in India, where technology use varies so widely. There, I’ve seen folks preferring to interact with highly digital services and others more comfortable using analogue systems. I remember buying train tickets at the counter — the conversations with the staff behind the counter being warm, helpful, and confident (not always :P). They’d suggest better trains or seats, make sure you got good value for your money, and even ask clarifying questions if something didn’t add up. They could tell instantly whether someone was a first-time traveller or a regular and would adapt their approach accordingly.

That kind of human sensitivity is what I’ve tried to capture in this design — the ability for an interface to feel just as thoughtful, observant, and reassuring as the person behind the counter once did.

1 week

research, field study

2 days

as-is journey

4 days

future blueprint

2 days

low-fi wireframes

4 days

hi-fi wireframes

1 week

hi-fi prototype

stakeholders

  1. Passengers/Commuters

  2. Luas Operations Team

  3. Customer Support

  4. Backend Development Team

  5. Ticket/Leap Card Validation Team

research insights/pain points

  1. Outdated & complicated process to buy paper tickets - creates confusion in users

  2. Machines or validators sometimes fail, leaving users unsure how to proceed.

  3. Users face confusion about when/where they have to board the tram, where to get off and where to change trams.

  4. Machines reject large notes, run out of change, or can’t process certain cards.

wireframes

solution

Stepper component added to guide the users through the process, improving clarity, reducing cognitive load, and boosting confidence by showing progress and what’s next.

Simple, clear map for users to interact with, select stops and know the distance to destination.

AI-powered suggestions about places to explore around the selected destination.

More comprehensive selection of tickets so that the user knows exactly what to choose from with the price difference.

Clear and easy design to add or reduce the number of tickets with price changes visible upfront.

Additional option of Day Passes for adults and children, and option for user to switch to Group Ticket to improve convenience.

Personalised ticket recommendations with price difference and savings

Clear information regarding the difference between each ticket type

The relevant terminals are indicated clearly through images (and LED indicators on machines) to avoid confusion and delays

Guided experience

Clear currency information

Option to change payment to card always available for users

Confirmation message and progress bar to provide clarity.

Detailed guidance on their journey including visuals representation

QR code on both digital and printed tickets for users to scan and track their journeys live

design system

1.

AI-powered recommendations for ticket bundles and travel deals lead to higher-value purchases.

2.

Reduces confusion and delays at ticket machines through clear, guided interactions.

3.

Real-time travel insights help optimise scheduling, reduce crowding, and improve long-term planning.

4.

Future-proofs Dublin’s transport ecosystem with a scalable, AI-driven framework.

key takeaways

1.

AI as a design accelerator

It was my first time using using Generative AI for rapid ideation and prototyping, learning to balance speed with design quality through strategic prompt engineering.
I learnt that while AI accelerates the creative process, human insight adds the nuance towards crafting truly intuitive and meaningful solutions.

2.

Start small - prioritisation of features:

Practised prioritising features by starting with a lean MVP, focusing on core functionality first, and planning for future scalability.

3.

Limitations:

As this was a conceptual project, I wasn’t able to validate designs through user testing, but I approached the problem with research-backed insights and industry best practices.

I strengthened skills in designing AI-powered user journeys, which can be applied to real-world transport and ticketing challenges.

impact

personal note:

This project is still very much a work in progress for me. Every day, I wake up with new ideas — small or big — that could make the service better, more thoughtful, and more impactful. I don’t see it as something that’s “done,” but as something that keeps evolving as I learn and observe more. My goal is to continue refining it, making it stronger and more scalable over time.

Thank you for taking the time to explore it with me.

Go to service design phase

Curious about the bigger picture? Continue to service design section to see how this case study was shaped from a research and journey mapping perspective.

sindhubhat18@gmail.com

© 2025 by Sindhu Ganapathi Bhat

sindhubhat18@gmail.com

© 2025 by Sindhu Ganapathi Bhat