1. Institute for Transport Studies, University Karlsruhe Adding Value to Your Data: Analysis of Travel Expenses Based on Trip Diary and Enriched Odometer Reading Data Tobias Kuhnimhof, Institute for Transport Studies, University Karlsruhe

2. Institute for Transport Studies, University Karlsruhe Agenda Problem Statement and Objective Available Data: MOP, EVS Imputing Automobile Expenditures Approach Results The Problem of Imputing Public Transport Expenditures Conclusions

3. Institute for Transport Studies, University Karlsruhe Problem Statement and Objective Little Knowledge about travel expenses and particularly relationship of expenses and mobility behavior Reason: No sufficient Data available: SurveyIncomeExpenditureMobility EVS  MOP  MiD   Approach: Close this gap by imputing mobility expenditure

4. Institute for Transport Studies, University Karlsruhe EVS – The German Income and Expenditure Survey 3 month income and expenditure report N = 75.000 (0,2% of all private households) Conducted every 5 years (most current survey: EVS 2003) Not compulsory Micro-data available for research purposes Results: Expenditure per household and month€ Car – Depreciation99 Car – Repair47 Car – Tax12 Car – Insurance36 Car – Fuel82 Public Transport21 Total297

5. Institute for Transport Studies, University Karlsruhe EVS – The German Income and Expenditure Survey Problems when using EVS-data for analysis of travel expenses No evaluation of travel expenses in connection with mobility behaviour possible No micro-analysis of individual expenditures / no distribution of expenditures possible because most mobility expenditures are not continuous: Example EVS is an appropriate basis of comparison for mean values Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Purchase of new car Payment of vehicle insurance, tax Selling old car Reporting period

6. Institute for Transport Studies, University Karlsruhe The German Mobility Panel – Mobility Diary and Odometer Survey MOP Mobility Survey: One week trip diary in 3 consecutive years Annual sample of ~ 1.000 households, ~ 2.000 persons Subset of MOP households enters into odometer survey sample N = ~ 400 vehicles Details of the car: Make, Model, Year of construction … 3 month odometer reading survey Each fuelling of the car is reported with: Liters, Price, Full or not?, Mileage of vehicle Data used for this expenditure analysis: Fall 2004 / Spring 2005

7. Institute for Transport Studies, University Karlsruhe The Idea of Imputing Mobility Expenditures Imputing fixed costs based on car data, season ticket,… Imputing out-of-pocket costs based on 7-day activity and mobility diary: Private Train Use: KM x Price = Expenditure Train Use on Business Trip: No Expenditure for private HH Shared Ride / on Foot: No Expenditure Trips by Car: KM x Fuel/KM x Fuel Price = Expenditure

8. Institute for Transport Studies, University Karlsruhe Imputing Automobile Expenditures Offline (e.g. ADAC) and online (e.g. autobudget.de) databases for car value and expenditure estimation available  quite similar results Necessary Assumptions: Type of insurance Annual mileage … Total costs per year: Depreciation … Monthly expenditures: Repairs Tax … Car Details: Make, Model Year of construction …

9. Institute for Transport Studies, University Karlsruhe Imputing Automobile Expenditures Assumptions for imputing automobile costs using autobudget.de: Type of financing (leasing, instalment purchase, “cash”) doesn’t matter in terms of monthly cost Holding period: 5 years Automobile insurance: Vehicle age > 7 years  obligatory insurance only Younger vehicles  the younger the better the insurance Fuel prices (spring 2005): Petrol: 1.18 €/Liter Diesel:1.04 €/Liter Automobile expenditures were only imputed for households with complete information about all vehicles in the household: N=317 Cars (212 Households)

10. Institute for Transport Studies, University Karlsruhe Results – Expenditures per Car Expenditures per car and month – Comparison of EVS- and MOP-Data Differences in expenditures for fuel can be attributed to increases of fuel prices 2003  2005 Satisfactory conformity of results

11. Institute for Transport Studies, University Karlsruhe By type of registration Results – Expenditures per Car

12. Institute for Transport Studies, University Karlsruhe By Age Results – Expenditures per Car

13. Institute for Transport Studies, University Karlsruhe Distribution of total costs per month Results – Expenditures per Car

14. Institute for Transport Studies, University Karlsruhe Results – Automobile Expenditures per Household Expenditures per household and month – Comparison of EVS- and MOP-Data Company cars not included Satisfactory conformity of results Expenditure per household and monthEVS [€]MOP [€] Car – Depreciation9996 Car – Repair4756 Car – Tax1213 Car – Insurance3635 Car – Fuel8293 Total276293

15. Institute for Transport Studies, University Karlsruhe Results – Automobile Expenditures per Household Expenditures per household and month by population of residence

16. Institute for Transport Studies, University Karlsruhe Results – Automobile Expenditures per Household Expenditures per household and month by incomce

17. Institute for Transport Studies, University Karlsruhe Results – Automobile Expenditures per Household Households without car & Households only with company car

18. Institute for Transport Studies, University Karlsruhe Costs for public transport = fixed costs (Bahncard, season tickets) + out of pocket costs (tickets) Assumptions: Persons with disabilities ride for free Season ticket holders ride for free when commuting and in city of residence Bahncard holders: 25% reduction on trains Business trips pose no expense to private households The Problem of Imputing Public Transport Expenditures

19. Institute for Transport Studies, University Karlsruhe Prices have to be assumed for: – Urban transport single fare – Monthly season ticket prices (normal / reduced) – Railway prices Actual public transport prices paid - sources of information: Deutsche Bahn (=German Rail): total revenue / total passenger KM travelled = 0,08 € / KM KVV (Karlsruhe urban transport association): total revenue / total no. of trips = 0,53 € / Trip EVS: Monthly public transport expenditures by private households= 21 € The Problem of Imputing Public Transport Expenditures

20. Institute for Transport Studies, University Karlsruhe Assuming (low) prices: – Urban transport single fare = 1 € – Monthly season ticket prices (normal / reduced)= 20 € / 15 € – Railway prices(Bahncard = 50 €)= 0.1 € / KM Actual public transport prices paid - sources of information: Deutsche Bahn (=German Rail): total revenue / total passenger KM travelled = 0,08 € / KM Σ(total expenditures for rail KM & Bahncard / rail-KM)= 0.08 € / KM KVV (Karlsruhe urban transport association): total revenue / total no. of trips = 0,53 € / Trip Σ(total expenditures for single fare & season ticket / # trips)= 0.69 € / Trip EVS: Monthly public transport expenditures by private households= 21 € MOP: Monthly public transport expenditures by private households= 30 € The Problem of Imputing Public Transport Expenditures

21. Institute for Transport Studies, University Karlsruhe Satisfactory results of imputing automobile costs: Maybe not exact in each individual case But apparently no general bias Now possible Analysis of automobile expenditure distribution Analysis of automobile expenditure in relation with mobility behaviour Not yet satisfactory results of imputing public transport costs Bias: Travellers in data set seem to spend too much on public transport Possible explanations: - Bias in data set ? - Job ticket paid by employer? - Public transport expenditures in EVS too low? - Better assumptions and / or regional differentiation necessary? Conclusions