From Tap to Track
/ service 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 However, the current ticket-buying experience feels outdated and confusing, particularly for first-time users and visitors.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
my role
Created a service design strategy, mapping end-to-end passenger and operational journeys to identify friction points and design opportunities across both physical and digital touchpoints.
Integrated AI features and data layers into the service blueprint, aligning passenger needs with operational insights to deliver a future-ready, adaptive solution.
Designed intuitive flows, wireframes, and high-fidelity prototypes focused on reducing confusion and improving accessibility.
Developed a scalable design system to ensure visual and functional consistency, with the flexibility to be extended across other Dublin transport services such as Dublin Bus and DART.
28
days
24
insights
2
journey maps
2
blueprints
1
interactive prototype
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 decided to work on this proof of concept due to a few personal experiences. I live very close to the Luas Green Line in Dublin and am a regular Luas commuter, so I use the Leap Card and rarely buy tickets from the machine.
It's happened a few times that when I am waiting at the stop near my house, a few folks who are either tourists or non-locals, are standing in front of the ticket machine, confused, frustrated and looking around for help. Some of them approached me to help them out while other times, I volunteered to help. I also noticed a few commuters giving up and opting to book a cab instead. This piqued my curiosity and I used the machine a few times to purchase tickets as an experiment to understand what was going wrong. I started talking to people during off-peak hours, interviewing them, and collecting observations. With all the insights in hand, I decided to reimagine what an updated, modern ticket purchasing experience could look like.
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
Passengers/Commuters
Luas Operations Team
Customer Support
Backend Development Team
Ticket/Leap Card Validation Team
research insights/pain points
Outdated & complicated process to buy paper tickets - creates confusion in users
Machines or validators sometimes fail, leaving users unsure how to proceed.
Users face confusion about when/where they have to board the tram, where to get off and where to change trams.
Machines reject large notes, run out of change, or can’t process certain cards.
as-is journey maps
The above image is a snippet of the current journey map for purchasing a ticket at the Luas stations. The current experience of topping up the leap card (commuter card for public transport in Dublin) was also mapped. Both are based on desk research, user interviews and ethnographic studies.
These maps are a combination of empathy and journey mapping containing user actions, corresponding thoughts and emotions, and pain points.and opportunities.
personas
solution
Transformed the current journey into a Gen AI-powered experience that creates a seamless, user-focused ticketing journey for all passenger types, from tourists to daily commuters.
1.
Designed an intuitive, AI-powered ticketing experience that adapts to different commuter archetypes, offering personalised recommendations based on user needs.
2.
Enhanced search functionality to make stop selection effortless — for example, if a user types in a general area, the system suggests nearby Luas stops automatically.
3.
Added context-aware suggestions for nearby activities and points of interest, enhancing the travel experience for tourists and leisure travellers.
4.
Built AI-driven fare optimisation, analysing selected ticket types to offer better-value alternatives where savings are available.
5.
Implemented step-by-step payment guidance with visual cues — including LED indicators and on-screen prompts — to direct users through each stage, reducing confusion and delays.
6.
Integrated QR codes on both digital and printed tickets, allowing passengers to track their journey live and receive timely alerts for when to disembark.
7.
Added a Day Pass option, simplifying travel for tourists by eliminating the need to purchase separate tickets throughout the day.
8.
Created a future-proof service blueprint with 2 additional layers to visualise AI interactions on the frontend and data collection/analysis on the backend.
9.
Introduced a clean, interactive route and zone map to provide a simple, visual guide for stop selection and journey planning.
10.
Enabled data-driven insights to forecast demand, analyse passenger behaviour by time of day, and continuously refine the ticketing experience.
service design blueprint
3.
Scalability mindset:
3.
While focused on Luas, the system was designed to be future-ready, scalable across other Dublin public transport systems.
2.
AI as an enabler:
2.
Leveraging AI made the overall experience more intuitive and engaging, demonstrating how emerging technologies can humanise complex systems.
1.
Overcoming research barriers:
1.
Some users were hesitant to participate in interviews, which limited direct feedback. To work around this, I observed passenger behaviour at stops, gathering valuable insights without disrupting their journey.
key takeaways
1.
Simplifies travel for both daily commuters and first-time users, especially tourists.
2.
Reduces confusion and delays at ticket machines through clear, guided interactions.
3.
Provides real-time, personalised support while also giving operators actionable insights through data analytics.
4.
Future-proofs Dublin’s transport ecosystem with a scalable, AI-driven framework.
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.
Want to see how this work translates into product design? Continue to the product design section of the same case study.
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