Transport & infrastructure

In-depth analysis

KO1 — Real-time public transport data

• Mechanism: Mandatory adoption of the GTFS-RT (General Transit Feed Specification — Realtime) standard for every public transport operator serving a settlement above 10,000 inhabitants. Vehicles are equipped with hybrid GPS + cellular trackers, and the data becomes public via a central API (unifying MÁV — Hungarian State Railways, Volánbusz and BKK — the Budapest transport operator). Third parties (Google Maps, Moovit, Hungarian start-ups) are free to build applications on top of it. Occupancy data is supplied by anonymised Wi-Fi/BLE counters.
• Quantified target: By 2028, real-time data is available in 95% of the 100 largest cities; passenger-information accuracy exceeds 90% (max. 2-minute deviation from actual).
• International precedent: Estonia — in 2012 the country made public transport free nationwide in rural areas, but the real breakthrough came from real-time data integration (Tallinn 2013): ridership rose by 14% in the first year, mainly due to reduced transfer uncertainty.
• Trade-off / risk: Making real-time data public exposes delays — in the short term this creates political pressure on operators, which can lead to reactive “firefighting” rather than structural development. In addition, the cost of data production is disproportionately high for smaller operators.

KO2 — Smart traffic management

• Mechanism: Deployment of an adaptive signal system (SCATS/SCOOT type) at the 20 highest-volume junctions, based on real-time inductive loops plus camera-based traffic counting. The system uses an ML model to predict traffic 15–30 minutes ahead and dynamically adjusts green phases. During congested periods a dynamic congestion charge is introduced in Budapest’s inner zone, with revenues hypothecated exclusively to public transport development.
• Quantified target: A 15% reduction in average peak-hour travel time in the 10 largest cities by 2030; a 20% reduction in private-car traffic in Budapest’s inner zone.
• International precedent: Stockholm — after the 2006 introduction of the congestion charge, inner-city traffic fell by 22%, air quality showed measurable improvement (NO₂ −8–14%), and public opinion — initially opposed by a majority — voted in a referendum to keep the system.
• Trade-off / risk: The congestion charge is regressive — it hits low-income commuters disproportionately if there is no parallel public transport alternative. Stockholm only worked because revenues were immediately channelled into denser bus services.

KO3 — Electric mobility plan

• Mechanism: EV charger deployment is directed not by grant tenders but by a demand-prediction model: using vehicle GPS data, registration statistics and road-segment traffic, we identify “charging deserts” (stretches of the main road network of ≥30 km without a charger). Deployment follows a PPP (Public-Private Partnership) model, where the state covers the grid-connection cost and the private sector handles operation.
• Quantified target: By 2028, 10,000 public charging points (up from the current ~2,500), including 2,000 fast chargers (≥150 kW); on the main road network a charger available at most every 25 km.
• International precedent: The Netherlands — by 2024, more than 150,000 public charging points for ~17 million inhabitants (EU-leading), but the key to success was local authorities installing “lamp-post chargers”, thereby solving the residential-charging problem for apartment-block dwellers.
• Trade-off / risk: Rapid EV-charger deployment loads the electricity grid — rural transformer capacity is inadequate in many places, and grid upgrades take 3–5 years to deliver. If charging infrastructure outruns grid development, grid instability and trip-outs can follow.

KO4 — Data-based railway development priorities

• Mechanism: Creation of a rail-investment priority matrix: ranking is based on passenger data (ticket sales + automatic passenger counting), the absence of alternative transport options, the economic multiplier effect and the cost-benefit ratio. The matrix is updated annually and published publicly — political override is handled on a “comply or explain” basis: deviations from the ranking are permitted but must be justified in writing.
• Quantified target: Raise the share of electrified rail lines from 37% to 50% by 2032; cut travel times between Budapest and the county capitals by an average of 20%; increase passenger traffic on the developed lines by 25%.
• International precedent: Switzerland — the 1982 “Bahn 2000” programme optimised the clockface timetable and interchange nodes, not absolute speed. Result: rail passenger traffic doubled in 20 years at a fraction of the investment cost of a high-speed network.
• Trade-off / risk: Data-based prioritisation favours the busiest (typically Budapest-centric) lines, which can deepen regional inequality. An “equity correction factor” is needed to overweight lines serving disadvantaged regions.

KO5 — Micromobility and cycle-friendly development

• Mechanism: Installation of cyclist traffic counters (inductive loop + IR sensor) in the 30 largest cities, used to set the priority order for cycle path construction. E-scooter regulation essentials: max. 25 km/h, mandatory insurance, geofenced exclusion zones (pedestrian areas), and an operator licence fee paid to the city which must be spent on cycling infrastructure.
• Quantified target: Raise the cycling modal share in the 10 largest regional cities from 5% to 12% by 2030; in Budapest from 8% to 15%; cut cyclist fatalities by 50%.
• International precedent: Seville (Spain) — between 2006 and 2010 built 164 km of segregated cycle paths (essentially from zero), and the cycling modal share rose from 0.5% to 7% in 4 years. The key to success was a connected network, not isolated segments.
• Trade-off / risk: Creating cycle paths at the expense of car traffic lanes causes short-term congestion increases and generates political resistance. The Seville model only worked because a large network was built at once — the “piecemeal” approach fails to reach critical mass.