To improve the efficiency and convenience of PT services

Macroscopic Modelling of Budapest Traffic – Unified Traffic Model (EFM)

The Unified Traffic Model (EFM) is a macroscopic transport modelling tool used to analyse and support planning decisions in Budapest. It evaluates traffic flow changes caused by network developments at both city-wide and neighbourhood levels, using demand represented through a structured modelling framework.

 Core Characteristics

Traffic demand is described through flows derived from the traditional four-step modelling approach, enhanced with both static and dynamic attributes. The model enables efficient evaluation of a wide range of development scenarios—from small-scale interventions to large infrastructure projects worth billions of euros. The EFM is designed to be resource-efficient and flexible:

• Scenario modelling typically requires 0.5–2 days
• Input data can be based on high-level estimates rather than detailed forecasts
• Supports rapid iteration and ad hoc idea testing
• Over 450 scenarios were modelled between 2024–2025
• Automatically generates inputs for cost-benefit analysis

 

Key Applications

The model plays a central role in both strategic and operational planning:

• Strategic planning support
  • Network Development Strategy
  • Agglomeration Strategy (within Budapest’s SUMP)
• Operational planning support
  • Public transport line extensions
  • Tram and metro replacement planning

 

EFM 3.0 Enhancements (2024)

A major upgrade introduced a shift toward a tour-based demand model, significantly improving the realism of travel representation. Key improvements include:

• Representation of 36 travel chain types
• Addition of new zones and refined airport modelling
• Improved modelling of suburban public transport
• Integration of Park-and-Ride (P+R) behaviour
• Dedicated tourism demand modelling
• Automated data integration for calibration
• Updated future scenarios: 2034 and 2050

 

Advantages of the Travel Chain-Based Model

The new approach captures travel behaviour more realistically by modelling complete travel chains rather than single trips:

• Represents demand of 3.1 million residents (~7 million trips/day)
• Supports intermodal travel and transfers
• Captures remote and hybrid working patterns
• Includes 12 population segments, 10 travel purposes
• Provides detailed demand matrices by purpose, mode, and time
• Includes feedback mechanisms to avoid overestimation

 

New Perspective: Tourism Demand

A separate model addresses non-resident travel demand, based on app-based geolocation data:

• Tracks visitor travel patterns and temporal distribution
• Differentiates trip purposes (e.g. accommodation vs. other)
• Estimates total tourism-related demand as a network-level attribute 

 

Towards More Realistic Public Transport Modelling

While current modelling focuses mainly on technical variables (e.g. travel time, cost), ongoing research aims to integrate qualitative factors into mode choice and assignment, such as:

• Accessibility (barrier-free infrastructure)
• Passenger information systems
• Safety and reliability
• Staff behaviour
• Comfort (cleanliness, air conditioning)