Tobias Kuhnimhof, Institute for Transport Studies, University Karlsruhe

1. 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. Agenda • Problem Statement and Objective • Available Data: MOP, EVS • Imputing Automobile Expenditures • Approach • Results • The Problem of Imputing Public Transport Expenditures • Conclusions

3.  • Approach: Close this gap by imputing mobility expenditure Problem Statement and Objective • Little Knowledge about travel expenses and particularly relationship of expenses and mobility behavior • Reason: No sufficient Data available:

4. 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:

5. 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 Selling old car Payment of vehicle insurance, tax Purchase of new car Reporting period

6. 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. Trips by Car: KM x Fuel/KM x Fuel Price = Expenditure Train Use on Business Trip: No Expenditure for private HH Shared Ride / on Foot: No Expenditure Private Train Use: KM x Price = Expenditure 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:

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

9. 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. 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. Results – Expenditures per Car • By type of registration

12. Results – Expenditures per Car • By Age

13. Results – Expenditures per Car • Distribution of total costs per month

14. Results – Automobile Expenditures per Household • Expenditures per household and month – Comparison of EVS- and MOP-Data • Company cars not included • Satisfactory conformity of results

15. Results – Automobile Expenditures per Household • Expenditures per household and month by population of residence

16. Results – Automobile Expenditures per Household • Expenditures per household and month by incomce

17. Results – Automobile Expenditures per Household Households without car & Households only with company car

18. The Problem of Imputing Public Transport Expenditures • 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

19. The Problem of Imputing Public Transport Expenditures • 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 €

20. The Problem of Imputing Public Transport Expenditures • 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 €

21. Conclusions • 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?