Outline

1. Dominika Kalinowska andHartmut KuhfeldDIW Berlin, GermanyAssessment of car use and ist determinants for GermanyPresentation at the COST Action 355 MeetingMadrid, May 09 - 11 2006

2. Outline • Dynamics of car ownership and use over time • Analysing the development of road travel demand – investigation of resulting research question • Data requirements – a new data source on motorised vehicle mileage for Germany

3. Number of trips per day, in mio. Km per person per day, in mio. 250 2500 1022 225 2250 81 200 2000 71 175 1750 852 Trip purposes 150 1500 80 515 Leisure time Service/ shopping 125 1250 Business 51 Education 100 1000 Work 340 218 75 750 239 15 17 93 50 500 15 496 14 108 37 357 33 25 250 0 0 1982 2002 1982 2002 Sources: MiD 2002, KONTIV 1982 Trip purpose distribution, Germany 1982 and 2002

4. MiD total East West 10% 8% 17% Bicycle 15% 26% 22% Car passenger 8% 8% 9% 45% 42% 16% Walk MiD persons > 10 years of ag KONTIV 1976 23% 9% 9% 16% 34% 24% 11% 8% 12% 46% 34% 8% 45% KONTIV 1982 KONTIV 1989 Transit 11% 12% 10% 28% 28% 12% Car driver 10% 12% 38% 38% Sources: MiD 2002, KONTIV 1976, 1982, 1989 Mode choice in Germany 1976, 1982, 1989 and 2002

5. 100% 32% 12% 10% 8% 13% 28% 1% 2% 58% 29% 0% 30% 2% 46% 59% 44% 75% 61% Number of cars in the household 57% 67% 3 and more 55% 2 1 0 50% 3 and more 41% 2 40% 39% 1 0 29% 25% 11% 10% 7% 4% 3% 0% 3 Persons 4 Persons 1 Person 2 Persons and more Sources: MiD 2002, Size of the household KONTIV 1982 Household car ownership, Germany 1982 and 2002

6. Car fleet in 1,000 Average mileage in 1,000 km Mileage in mio. km 30 50.000 600.000 25 500.000 40.000 20 400.000 30.000 Gasoline/ business Diesel/ business 15 300.000 Diesel/ private 20.000 Gasoline/ private 10 200.000 10.000 5 100.000 0 0 0 1993 2002 1993 2002 1993 2002 Passenger cars and their use, Germany 1993 and 2002 Sources: Car mileage survey 1993 and 2002, calculated by DIW Berlin.

7. Dynamics of car ownership and use over time • Main observations: • Passenger car purchase and ownership undergo a dynamic development over time, adapting to changing conditions • Size and structure of the passenger car fleet in Germany is continuously changing, shifting towards, e.g., cost efficiency as well as consumer preferences • Motorization is growing, in particular with regard to multi-car ownership • Car is the dominating travel mode and its share in the modal-split is growing – prospects of a saturation? • By far the most frequent trip purposes with an increasing occurrence are leisure, shopping and service

8. Car travel demand analysis – an illustration for Germany • … using car mileage survey data from 1993 and 2002, containing explanatory variables linked to car ownership and use, on the vehicle as well as on the holder/ user level Technical vehicle attributes: • Mode of drive, • Age of the vehicle, • Odometer, • Displacement in ccm, • Engine power in kw. • Sociodemographic characteristics of the holder/ user: • Number of household members, • Number of vehicles owned by the household, • Sex, • Age, • Professional attainment, • Type of contract (full time vs. part time job).

9. Size of the car fleet in mio. Average annual km travelled in 1,000 km 0 20 40 60 0 5 10 15 20 25 30 1993 2002 Type of engine Gasoline 1993 2002 Diesel 1993 2002 Car holder Private 1993 2002 Business 1993 2002 Engine type and holder Private/ gasoline 1993 2002 Private/ diesel 1993 2002 Business/ gasoline 1993 2002 Business/ diesel 1993 2002 Car fleet structure and vehicle use, Germany 1993 and 2002 Sources: Car mileage survey 1993 and 2002, calculated by DIW Berlin.

10. Car mileage – model estimation and analysis of variance Sources: Car mileage survey 1993 and 2002, calculated by DIW Berlin.

11. Private cars – full model analysis of variance Sources: Car mileage survey 1993 and 2002, calculated by DIW Berlin.

12. Interaction effects between car user’s age and survey year Including interactions Sources: Car mileage survey 1993 and 2002, calculated by DIW Berlin.

13. Main findings • Convergence between age groups over time 1993-2002 and over the life cycles of the drivers • Convergence on the sociodemographic level, e.g., for gender – increase of car stock and overall mileage is mainly due to female car ownership • Selectivity on the technical level of vehicles – “Power users” drive diesel vehicles

14. Analysing the evolution of passenger travel demand • In general extensive and costly mobility data is required to understand the observed development of travel demand • National mobility surveys (in Germany MiD 2002 or KONTIV for the previous years) • Car mileage surveys (Fahrleistungserhebung 1993 and 2002) • Mobility Panel (MoP) • Numerous additional data from official statistics and other sources • Despite wide ranging data sources, estimation of mobility parameters and explaining of travel behaviour creates ongoing data needs

15. Data sources for estimation of car km traveled • National travel or mileage surveys are complex and costly, and therefore are conducted in wide spread time intervals • Road traffic counts often display deficiencies, as to area-wide or time of the day and day of the week coverage, etc. • German freight transport statistics covers only heavy weight lorries • Mobility Panel (MoP) provides up-to-date data for monitoring current developments, on the other hand it is not a fully random sample and rather small to be representative • Fuel sales statistics require additional assumptions as to average fuel consumptions, at the same time leaving the question of “grey imports” unanswered • Possible additions to currently existing data bases?

16. Odometer readings from vehicle inspection data • To improve the quality and add to the reliability of the German data on car use as the annual total as well as vehicle average per year, new data basis and estimation method are being established • The project of surveying and implementing odometer readings is conducted on behalf of the German Transport Ministry (BMVBS) and started the end of 2006 • The first stage of the project is concerned with data collection • In the second stage, sampled data on odometer readings is enriched by additional vehicle information from official statistics (KBA) • Finally extrapolation weights are calculated to obtain representative mileage estimation results for the German vehicle fleet total

17. Odometer readings data collection • Odometer readings are collected by the German institutions responsible for the obligatory technical vehicle inspections (TÜI) together with additional vehicle data • No legal obligation exists so far to enforce the provision of the odometer data recorded by the TÜIs – data was made available on our request on a voluntary basis, after negotiations, and for a cost compensation • 4 out of all the existing 27 TÜIs agreed to supply their data records for 2006, giving more than 5 mio. observations or 24% of all inspections conducted during one year compared to a total of 55 mio. motorized vehicles registered in Germany • The overall quality of the data is good to very good, containing only very few coding errors or outliers

18. Structure of the data • Data provided by the TÜIs contains: • Code of the vehicle manufacturer, • Code of the vehicle type, • Month and year of vehicle’s first registration, • The odometer reading, • Date the reading was recorded, • Optionally a regional marker indicating the location of the inspecting institution • All data sets provided are merged together and can be extended using the code combination of vehicle type and manufacturer by detailed technical vehicle attributes

19. General results at first glance – where are the problems • Weaknesses to tackle: • Outliers, • 5-digit odometers, • Vehicles inspected before reaching 3 years from first registration date, i.e. before the first obligatory inspection date, • Vehicles inspected by holder companies (like public busses) • Strengths of the data: • Preliminary calculations of mileage for vehicle category totals as well as annual averages of km per vehicle correspond well to existing estimates of motorized vehicle mileage traveled • Numbers of sampled observations in distinct manufacturer-type-code combinations are high enough to allow for reasonable extrapolations • The possibility of data extensions improve the potential of the data for further analyses

20. Thank you for your attention! Any questions or comments?

21. Verfahren Hersteller (3 Stellen num.), Typ (3 Stellen alphanum.) Mon.Jhr Erstzulassung (4 Stellen) Mon.Jhr Ablesung (4 Stellen)Km-Zähler-Stand (6 Stellen) Fahrleistungserhebung 2002 – Pkw125 Herstellerbis zu 224 Typen je Herstellerbis zu 8 Jahre Erstzulassung je Typ

22. Log-transformation of the dependent variable .2 Daily mileage [km / day] per driven vehicle 1993 2002 total Mean 44.0 39.5 41.3 Median 36.4 31.4 32.9 Shares Shares 0 0 0 300 Car km/ day per vehicle driven by a private user Sources: Car mileage survey 1993 and 2002. DIW Berlin calculations.

23. Selected explanatory variables of car use • Motivation, purpose and requirements: • Infrastructure use • Road safety research • Environmental research and investigation of externalities such as e.g. green house gas emissions (CO2, ect.) • Methodology: • Basis is the domestic petrol and diesel consumption as annually reported by the German Petrol Industry Economic Association (Mineraloel Wirtschaftsverband, MWV) • Additional determinants of car km are the number of registered vehicles as annually reported by the KBA and average fuel consumption rates specified for defined vehicle categories (e.g. differentieated as to type and model, age or drive train of the vehicle)

24. Odometer readings in the EU • ... werden angewandt zur Berechnung von Fahrleistungen in: • Dänemark • Niederlande • Schweiz • Finnland • Schweden • Lettland