Nitra, Slovakia: passenger flow analytics

Nitra, Slovakia: passenger flow analytics

Key Facts

Locations:

Nitra (Slovakia)

Opening and closing date:

19/01/2026 - 12/03/2026

Funds available (up to):

60,000 EUR

Challenge area:

Multimodality

Offered by:

EIT Urban Mobility

Passengers in a bus

The Challenge

How can Nitra obtain accurate, anonymised boarding and alighting data across its bus network to improve public transport planning?

Nitra, a municipality in Slovakia, wants to pilot a lightweight, automated solution to collect anonymised boarding and alighting data on selected bus lines. The city aims to gain reliable, detailed insights into passenger flows to improve route planning and capacity management while reducing reliance on manual data collection. The pilot is intended to demonstrate a scalable, privacy-compliant approach that can later support full-fleet deployment and SUMP implementation.

Who can apply?

The RAPTOR Open Call 2026 is open to single small and medium-sized enterprises (SMEs). Proposals must be submitted by one legal entity only; consortia are not permitted. 

Applicants must: 

  • Qualify as an SME under the European Commission definition 
  • Be legally established in an EU Member State or a Horizon Europe associated country 
  • Propose a solution addressing one RAPTOR 2026 City Challenge 

Applicants may apply to multiple city challenges, but only one project can be funded per applicant. 

For full requirements, visit the website

Background

Nitra has modernised its bus fleet and introduced cashless ticketing, which removed a key source of passenger count data. Only a small share of buses are equipped with automatic passenger counters, leaving planners with an incomplete picture of passenger movements. As a result, service planning relies on fragmented data, manual surveys, and assumptions rather than real demand. Although all buses are fitted with CCTV systems, the data is not yet fully utilised for passenger analysis. Access to accurate, aggregated boarding and alighting data is therefore essential to support data-driven public transport planning and SUMP implementation.