The Budapest Transportation
Planning Model
A Cube Cloud demonstration model
Andreas Köglmaier
Regional Director
Content
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Overview of transport issues in Hungary and Budapest
The transport system of Budapest
The Budapest transport plan
Model structure
The Cube Cloud trial account
Using the Budapest model to test Cube Cloud
Geographic context: Hungary
Budapest conurbation (Model area)
Year
Population
1950
1,600,000
1960
1,800,000
1970
2,000,000
1980
2,100,000
1990
2,000,000
2000
1,800,000
2010
1,700,000
Source: Budapest statisztikai évkönyve
Budapest road network
Public transport modes in Budapest
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Metro (two systems) 33 km
Tram 155 km
Local Rail
Trolleybus, Bus
Cogwheel railway, funicular
Steam train, chair lift
Budapest metro
Source: Budapest statisztikai évkönyve
Budapest mode share
Role of modeling in Master Plan
 Transportation infrastructure and traffic project’s impact
analysis
 Data provision for cost-benefit analyses
 Data supply for environmental analyses
Support establishment of project selection and project
prioritisation
Model data
 Road and public transit infrastructure
• Road network
• MÁV, HÉV és Metro railway networks
• Public transport network and timetables (2008)
• BKV
• MÁV
• Volánbusz and others
 Household surveys
• 2004 évi BKV household survey
• 2007 évi S-bahn household survey
Model data
 Traffic counts
• Roadway traffic (2004-2008)
• BKV public transport patronage data (2007)
• MÁV public transport patronage data (2005)
• Year 2006 MÁV és Volán traffic counts in the outbound
direction within Budapest (2006)
 Population, employment, vehicle ownership forecasts
Software used for Budapest model
 Trip table calibration: CUBE Analyst
• Highway trip table calibration (AM peak, PM peak,
evening, night)
• Transit trip calibration (AM peak, PM peak, daily)
 Trip table forecast:
• Multiple regression analyses: SPSS
• Matrix manipulation: CUBE Voyager 5.0
 Mode choice model
• Calibration: Biogeme 1.7
• Incremental logit model: CUBE Voyager 5.0
 Highway and transit assignment: CUBE Voyager 5.0
Model scenarios
 10 initial road/public transit scenarios (Phase I)
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5 low budget scenarios
5 high budget scenarios
 2 final scenarios
 Special analyses
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Area wide toll
Unified tariff system
 Project level analysis: 56 road and PT projects
(Phase II)
Model structure
External data
• Transport networks, timetables
• Land use data
• Population, employment, vehicle ownership
• Costs (tariffs, patrol, parking)
Trip table and skim table calibration
• Raw trip tables from Household surveys -> calibration by traffic
counts
• Time skims (using time talbes and posted speeds) -> calibrate by real
time/floating car data
Trip table forecasts
• Growth rate method (multiple regression model)
• Peak hour spreading model (elasticity model)
Mode choice model
• Calibration of utility models (Household surveys)
• Incremental logit model (9 segments by purpose and area)
Highway and Transit assignments
• Highways: equilibrium method
• Public transportation: multi-path logit assignment with capacity
constraint
Assignment
 Highway assignment
• Four time periods (AM, PM, evening, night)
• Equilibrium assignment with 3 vehicle classes
• Fixed number of iterations between 8-40
• Daily volumes derived by the linear combination of 4
periods via using factors by road and area type
 Public transport assignment
• Daily assignment (AM peak timetable)
• Multi path assignment
• Capacity constrained (crowding) model with six
iterations
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Budapest Model on Cube Cloud
Test the benefits of Cube Cloud
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Internet: movement from a desktop-bound, ‘locked’ environment to an
internet-based, ‘open’, sharable, ‘work from anywhere/anytime’
environment
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Community Resource: model application and planning analysis done by
non-experts using common web-browsers moving models to an active
role in collaborative transportation planning
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Cloud-Computing: placement of the models, data and software in a
cloud-computing environment lowering hardware costs locally while
providing ‘unlimited’ high-spec resources
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Lower costs for the user: movement from locally licensed desktops to a
software as a service model. Monthly subscription business model
allowing many to use the model at low, or even, no cost
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Lessens IT complexity: much of the IT burden of modeling is shifted
from the user to the vendor
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Data and Software Integration: easier to integrate with external
systems: development reviews, regional air quality analysis, pavement
maintenance systems, traffic and transit ITS systems and to receive and
use data from data probes, detectors and static data sources
Acknowledgement
Csaba Kelen
Address: Kozlekedes Ltd, H-1052 Budapest, Bécsi utca 5
Phone: +36.1.235.2020/105
Fax: +36.1.235.2021
Email: csaba.kelen@kozlekedes.hu
Thank you!
Andreas Köglmaier
Regional Director
akoeglmaier@citilabs.com