Institute for Transport Studies, University Karlsruhe Adding Value to Your Data: Analysis of Travel...

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


Agenda

Problem Statement and Objective

Available Data: MOP, EVS

Imputing Automobile Expenditures

Approach

Results

The Problem of Imputing Public Transport Expenditures

Conclusions


Problem Statement and Objective

Little Knowledge about travel expenses and particularly relationship of expenses and mobility behavior

Reason: No sufficient Data available:

Survey Income Expenditure Mobility

EVS MOP MiD

Approach: Close this gap by imputing mobility expenditure


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 – Depreciation 99

Car – Repair 47

Car – Tax 12

Car – Insurance 36

Car – Fuel 82

Public Transport 21

Total 297


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


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


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



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 …



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)


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

€ per month and automobile EVS (2003) MOP (2005)

Depreciation 96 79 Tax 11 13 Insurance 35 30 Repair and Maintenance 45 55 Fuel 79 103 Total 266 281


By type of registration


Expenditures per Car

0

50

100

150

200

250

300

350

400

450

500

Private Company Car of Self-Employed

Company Car ofEmployer

Eu

ro p

er M

on

th


By Age


Expenditures per Month

0

50

100

150

200

250

300

350

400

450

0 - 3 Years 4 - 6 Years 7 - 9 Years > 9 Years

Vehicle Age

Eu

ro p

er

Mo

nth Fuel

Repair

Tax

Insurance

Depreciation


Distribution of total costs per month


Automobile Total Costs Per Month

0

5

10

15

20

25

total costs per month [€]

shar

e o

f fl

eet

[%]


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 month EVS [€] MOP [€]

Car – Depreciation 99 96

Car – Repair 47 56

Car – Tax 12 13

Car – Insurance 36 35

Car – Fuel 82 93

Total 276 293



Expenditures per household and month by population of residence

Automobile Expenditures per Household and Month

0

50

100

150

200

250

300

350

400

< 20.000 20.000 - 100.000 >100.000

Population of Residence

Eu

ro p

er M

on

th

Fuel

Fixed Costs



Expenditures per household and month by incomce

Automobile Expenditures per Household and Month

0

50

100

150

200

250

300

350

400

450

500

< 1.000 Euro 1.000 - 2.000Euro

2.000 - 3.000Euro

> 3.000 Euro

Monthly Household Income

Eu

ro p

er M

on

th

Fuel

Fixed Costs



Distribution of Automobile Expenditures per Household and Month

0

5

10

15

20

25

Expenditure per HH and Month

Sh

are

of

HH

[%

]

Households without car & Households only with company car


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



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 €



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 €



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

Institute for Transport Studies, University Karlsruhe Adding Value to Your Data: Analysis of Travel...

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