1. The Budapest Transportation Planning Model A Cube Cloud demonstration model Andreas Köglmaier Regional Director

2. 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

3. Geographic context: Hungary

4. Budapest conurbation (Model area) YearPopulation 19501,600,000 19601,800,000 19702,000,000 19802,100,000 19902,000,000 20001,800,000 20101,700,000 Source: Budapest statisztikai évkönyve

5. Budapest road network

6. Public transport modes in Budapest Metro (two systems) 33 km Tram 155 km Local Rail Trolleybus, Bus Cogwheel railway, funicular Steam train, chair lift

7. Budapest metro Source: Budapest statisztikai évkönyve

8. Budapest mode share

9. Transportation infrastructure and traffic projects 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

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. Budapest Model on Cube Cloud

24. 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

25. 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 Acknowledgement

26. Thank you! Andreas Köglmaier Regional Director akoeglmaier@citilabs.com