The Budapest Transportation Planning Model

A Cube Cloud demonstration model

Andreas KöglmaierRegional Director

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

Content

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

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

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

Role of modeling in Master Plan

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

Model data

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

Software used for Budapest model

10 initial road/public transit scenarios (Phase I)• 5 low budget scenarios • 5 high budget scenarios

2 final scenarios Special analyses

• Area wide toll• Unified tariff system

Project level analysis: 56 road and PT projects (Phase II)

Model scenarios

Model structureExternal 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

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

Assignment

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 Internet: movement from a desktop-bound, ‘locked’ environment to an internet-based,

‘open’, sharable, ‘work from anywhere/anytime’ environment

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

Cloud-Computing: placement of the models, data and software in a cloud-computing environment lowering hardware costs locally while providing ‘unlimited’ high-spec resources

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

Lessens IT complexity: much of the IT burden of modeling is shifted from the user to the vendor

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

Csaba KelenAddress: Kozlekedes Ltd, H-1052 Budapest, Bécsi utca 5Phone: +36.1.235.2020/105Fax: +36.1.235.2021Email: csaba.kelen@kozlekedes.hu

Acknowledgement

Thank you!

Andreas KöglmaierRegional Directorakoeglmaier@citilabs.com