My-TRAC · Project

Smart Travel App That Reads Passenger Mood to Improve Public Transport Decisions

transportPilotedTRL 6

Imagine a travel app that works like a friend who knows your commute — it picks up on your mood, learns your habits, and gives you genuinely useful suggestions instead of generic timetables. My-TRAC built exactly that: a mobile companion that uses emotion-sensing and AI to recommend the best routes, departure times, and connections across trains, metros, and buses. On the flip side, transit operators get a dashboard showing aggregated passenger behavior and sentiment — like a real-time mood map of their network — so they can spot trouble before complaints pile up. All of this runs on the user's own phone, so personal data never leaves their device.

By the numbers

pilot locations with real transit operators

consortium partners

countries represented in consortium

SMEs in consortium

total project deliverables

50%

industry ratio in consortium

The business problem

What needed solving

Public transport operators struggle to understand why passengers choose certain routes, times, or modes — and they find out about dissatisfaction only after ridership drops or complaints spike. Existing travel apps give the same generic directions to everyone, ignoring the passenger's context, mood, or preferences. Operators lack real-time aggregated insight into how passengers actually feel about their service.

The solution

What was built

My-TRAC delivered a working mobile travel companion app with affective computing (emotion-sensing), AI-driven route and departure-time recommendations, crowdsourcing features, and group travel suggestions. They also built an operators' dashboard for visualizing aggregated, anonymized passenger movement and sentiment data. A user-choice modelling system that predicts mode, station, route, and departure-time preferences was developed and tested across 4 pilot transit networks.

Audience

Who needs this

Urban metro and rail operators wanting to improve passenger experienceMaaS platform companies integrating multimodal transportSmart city consultancies advising on transit optimizationTransit technology vendors building next-generation passenger appsRegional transport authorities planning service improvements

Business applications

Who can put this to work

Public Transit Operations

enterprise

Target: Metro and rail operators managing urban networks

If you are a metro or rail operator dealing with declining ridership and poor passenger satisfaction scores — this project developed an AI-powered mobile app tested across 4 pilot locations that reads passenger sentiment in real time and feeds aggregated, anonymized behavior data into an operators' dashboard. This lets you spot service pain points before they become complaints and adjust operations dynamically.

Mobility-as-a-Service Platforms

mid-size

Target: MaaS providers integrating multimodal transport

If you are a MaaS platform struggling to keep users engaged beyond basic route planning — this project built a travel companion with affective computing and group recommendation features that go far beyond standard trip planners. The app was designed to seamlessly integrate rail, metro, and other public transport services into one interface, increasing user retention through personalized, context-aware suggestions.

Transport Data Analytics

SME

Target: Smart city consultancies and transport planning firms

If you are a transport consultancy that needs real passenger movement and sentiment data to advise city authorities — this project created a modelling system that simulates user choices for mode, station, route, and departure time. Tested with operators like NS in the Netherlands and ATTIKO Metro in Greece, it provides aggregated travel pattern insights while maintaining privacy-by-design compliance.

Frequently asked

Quick answers

What would it cost to implement this system for our transit network?

The project data does not include specific licensing or implementation costs. The system was developed under the Shift2Rail programme and tested with 4 European operators. Contact the coordinator at Universitat Politecnica de Catalunya to discuss commercial terms and deployment pricing.

Can this scale to a large metropolitan network with millions of daily riders?

The app was piloted across 4 different transit systems in the Netherlands, Greece, Spain, and Portugal, covering different network sizes and operator types. The privacy-by-design approach — running AI models on the user's own mobile device — means the system scales without proportional server infrastructure growth.

Who owns the intellectual property and can we license this technology?

The consortium of 10 partners across 5 countries developed the IP under an EU Shift2Rail grant. Licensing arrangements would need to be negotiated with the consortium, led by Universitat Politecnica de Catalunya. The 4 SMEs and 5 industry partners in the consortium suggest commercial exploitation was planned from the start.

How does this handle passenger data privacy and GDPR compliance?

Privacy-by-design is a core principle: all models and algorithms run on the user's mobile device rather than on central servers. Data shared with operators is aggregated and anonymized, meaning no individual passenger can be identified from the operators' dashboard.

How long would it take to integrate this with our existing passenger information systems?

The project ran for over 3 years and produced 18 deliverables including the modelling system and operator interface. Based on available project data, the app was designed to integrate with existing rail and public transport operator systems, as demonstrated by pilots with NS, ATTIKO Metro, FGC, and Fertagus.

Does this actually predict disruptions or just show historical data?

The app provides predictive information concerning disruptions and disturbances. Rather than simply displaying raw data, it uses AI algorithms to analyze patterns and deliver improved recommendations to passengers before problems escalate.

What makes this different from existing transit apps like Citymapper or Google Maps?

Unlike standard routing apps, My-TRAC uses affective computing to understand a traveler's state of mind and adapts its recommendations accordingly. It also offers crowdsourcing, group recommendations, and a dedicated operator interface — features absent from consumer mapping tools.

Consortium

Who built it

The My-TRAC consortium brings together 10 partners from 5 countries (Belgium, Greece, Spain, Netherlands, Portugal), with a healthy 50% industry ratio — 5 industry partners alongside 3 universities and 1 research organization. Four of the partners are SMEs, which signals practical commercialization intent rather than a purely academic exercise. The geographic spread across Southern and Northern Europe, combined with pilot operators in 4 different countries (NS, ATTIKO Metro, FGC, Fertagus), means the technology has been validated across diverse transit cultures, regulatory environments, and network sizes. The coordinator is Universitat Politecnica de Catalunya, a top technical university in Barcelona with strong transport engineering credentials.

UNIVERSITAT POLITECNICA DE CATALUNYACoordinator · ES
ELLINIKO METRO MONOPROSOPI AEparticipant · EL
AETHON ENGINEERING SINGLE MEMBER PCparticipant · EL
ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXISparticipant · EL
EXPERIS MANPOWERGROUP SLparticipant · ES
UNION INTERNATIONALE DES TRANSPORTS PUBLICSparticipant · BE
SPARSITY SLthirdparty · ES
STRA, SAparticipant · PT
TECHNISCHE UNIVERSITEIT DELFTparticipant · NL
UNIVERSIDAD DE SALAMANCAparticipant · ES

How to reach the team

Universitat Politecnica de Catalunya (Barcelona, Spain) — reach out to their transport engineering or smart mobility department

Order a report on this consortium See UNIVERSITAT POLITECNICA DE CATALUNYA profile →

Next steps

Talk to the team behind this work.

Want an introduction to the My-TRAC team? SciTransfer can connect you with the right people and provide a detailed technology brief tailored to your transit network.

Order a report on this topic More in Transport & Mobility