A new approach for MM benefit-estimation Walter Bien ECOMM 2009 May 15 – San Sebastián

A new approach for MM benefit-estimation

Walter Bien

ECOMM 2009May 15 – San Sebastián

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Eltis Café @ ECOMM 2008June 5th - London www.eltis.org

Effect Estimation within changing framework/conditions

The classical (best) approach:Evaluation of treatment groups and “placebo”-groups

Estimation of change in the mobility/traffic area (modal split, PT passenger numbers, …) using statistical data (inhabitants, number of cars, commuters, PT offer, …)

Comparison of estimated and measured values

Example: Development of the number of PT passengers in Frankfurt from 1995 to 2010

Overview

Eltis Café @ ECOMM 2009 www.eltis.org

3

Eltis Café @ ECOMM 2008June 5th - London www.eltis.org

1. Effect Estimation within changing framework/conditions

Eltis Café @ ECOMM 2009 www.eltis.org

Compare: The “fat car driver”

vs. the “slim biker”

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Eltis Café @ ECOMM 2009

year

Public Transport

passengerschange-

rate

(values in millions)

1995 170,0

2001 183,4 7,9%

2007* 183,8 0,2%

„success“

of mobility management ???

* means: preliminary

… starting with mobility management measures in the year 2000

… establish mobility management in the following years

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„success“of mobility management

… could be ?

yearPT-

passengers

income by

ticket-sales

change-rate

(values in millions)

1995 170,0 117,0

2001 183,4 137,3 17,3%

2007* 183,8 167,0 21,6%

… but in the same two periods we have a strong increase of income by ticket sales (based on a higher price level)

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„success“

of mobility management

… yes !

yearinhabi-tants

emplo-yees

inhab.+employ.

change-rate

(all values in thousands)

1995 653 548 1.201

2001 646 603 1.249 4,0%

2007* 668 610 1.278 2,3%

… the increase of customer potential (inhabitants and employees) is less in the second period

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„success“ of mobility management:

… Yes (in a special manner) if we assume that there would be a decrease of the number of PT passengers and a less increase of income without mobility management …

period

Public Transport passen-

gers

Income by

ticket sales

Inhabitants &

employees

Compare of the change rates

1995-2001 7,9% 17,3% 4,0%

2001-2007 0,2% 21,6% 2,3%

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PT-passengers and PT-income (1995 - 2008) compared to fuel-price (index numbers: base 1995 = 100)

20082007200620052004200320022001200019991998199719961995

90

100

110

120

130

140

150

160

170

180

Index

PT - income (local)

fuel price

Public Transport passengers

The problem: effect estimation of measures

… we can see non effect of fuel prices on the developement of PT passengers

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Public Transport: offer and usage (1995 - 2008) (index numbers: base 1995 = 100)

95

100

105

110

115

120

125

20082007200620052004200320022001200019991998199719961995

level of capacity

PT - offer

PT - usage

The PT offer is stable in the first period while the usage icreases for 15%.

In the second period PT offer and also the usage is grown up for 6-7%-points.

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2. The classical (best) approach: Evaluation of treatment groups and “placebo”-groups

Remember – (Eric Schreffler; S. Diego):

The data never lie

– but do we so ?

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2. The classical (best) approach: Evaluation of treatment groups and “placebo”-groups

But also (Herbert Kemming, germany):

… The control group method… and its problems

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3. Estimation of change in the mobility/traffic area (modal split, PT passenger numbers, …) using statistical data (inhabitants, number of cars, commuters, PT offer, …)

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In the slides before we have to deal with this kind of data:

Number of Public Transport Passengers

PT income by ticket sales

Inhabitants (in city/region)

Employees (in city/region)

Fuel price

PT offer (in km*places - offered)

PT usage (in km*places - used)

… and all this data are almost available – and can be used (in combination with some others) to estimate effects of measures.

Structural data: important for modal-choice / „available“

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95

100

105

110

20082007200620052004200320022001200019991998199719961995

95

100

105

110Index

households in Frankfurt

number of household members

inhabitants younger than 18

inhabitants of Frankfurt

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95

100

105

110

115

120

20082007200620052004200320022001200019991998199719961995

95

100

105

110

115

120Index

employees - working in Frankfurt

employees - living in Frankfurt

number of cars in Frankfurt

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… on the next slide

– see the combination

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95

100

105

110

115

120

20082007200620052004200320022001200019991998199719961995

95

100

105

110

115

120Index

households in Frankfurt

number of household members

inhabitants younger than 18

employees - working in Frankfurt

number of cars in Frankfurt

employees - living in Frankfurt

inhabitants of Frankfurt

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The weighted combination of 4 single-indicator values is a good fitting indicator for the developement of PT-passenger-numbers:

Inhabitants of frankfurt (weight: 1)+ (reciprocal) number of cars (weight: 2)+ employees (working) in frankfurt (weight: 3)+ number of commuters to frankfurt (weight: 4)

---------------------------------------------------------------------------average of the indicators above = indicator for pt-passengers

Combining structural data with passenger-numbers in public-transport

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Combining structural data with passenger- numbers in public-transport

from structural data to an indicator-value (index numbers: base 1995 = 100)

80

90

100

110

120

130

20082007200620052004200320022001200019991998199719961995

inhabitants of frankfurt

employees (working) infrankfurt

number of cars (in reciprocalmanner)

number of commuters tofrankfurt

(weighted) average of theindicators above

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PT-passengers and indicator-value (index numbers: base 1995 = 100)

20082007200620052004200320022001200019991998199719961995

80

90

100

110

120

130

140

150

Index

indicator-value (combining structural data)

PT - income (local)

PT passengers in frankfurt


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Now we can construct a so called „Target Value“ for the number of PT passengers.

This is a weighted combination of the indicator-value before (combined by the 4 structural data) and the PT-offer (see slide no.8):

Indicator Value (weight: 2) + PT offer (weight: 1) -------------------------------------------------------------

average of the indicators above = Target Value for PT-passengers


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4. Comparison of estimated and measured values

The convincing argument:

Decisive – is the final result !

In german: “… was hinten rauskommt.”

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PT-passengers, indicator-value and a target value for passenger-number (index numbers: base 1995 = 100)

20082007200620052004200320022001200019991998199719961995

80

90

100

110

120

130

140

150

Index

indicator-value (combining structural data)

PT - income (local)

target für PT-passengers

PT passengers in frankfurt… now we can see the difference between the (realized) number of PT passengers and the expected number (target value) of PT passengers …

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1. It becomes possible to determine the effects of other measures

- such as mobility management or further soft-policies in PT

(advertisement, special efforts of information...) - separately

and also prove their economic efficiency.

2. Regarding the Frankfurt-area this approach shows that since

the year 2000 with rising tendency, the applied measures have

generated additional fare income within a two-digit million range

(of EUROs).

3. The lower costs (for mobility management) must lead to a

continuation and legitimate the spending of money not only

from an organisational/company-internal but also from a

political and public point of view.

Conclusion

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5. Example:

Development of the number of PT passengers in Frankfurt from 1995 to 2010

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the "result" of mobility-management in Frankfurt (2001 to 2007)

20082007200620052004200320022001200019991998199719961995

90

100

110

120

130

140

Index

PT - income (local)

target-value for PT passengers

PT passengers in Frankfurt

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the "result" of mobility-management in Frankfurt (2001 to 2007)

20082007200620052004200320022001200019991998199719961995

95

100

105

110

115

120

Index

target-value for PT passengers

PT passengers in Frankfurt

~ 20 Mio. EURO

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Next steps and chances

If the economic effects of mobility management and other soft

traffic policies can be estimated quantitatively in an easy way with

only few available indicators, low priced basic conditions for these

measures can be achieved.

The broad application and testing of this methodology would

induce an equal treatment of soft policies and mobility

management with rather "hardware-oriented" measures as for

example new travel offers (temporal/spatial), new vehicles or

price-arrangements in the PT-sector.

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In a further step a methodology can be developed, which

permits effect estimations for mobility management in

advance, like it has already been implemented in the

German-speaking-area by the so-called "standardized

evaluation" for all kind of infrastructure measures.

And that means:

New and equal opportunities for mobility management!

Next steps and chances

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… and so – we reach her/him:

the “multi-modal” mobility-user

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car(at all)

82%

bike(at all)

40%

Modal-choice of the inhabitants of Frankfurt (~ 670.000 p.)

PT (at all)

43%

car (only)

37%

bike (only)

6%

PT (only)

7%

car & PT16%

car& bike14%

PT & bike 5%

PT & car & bike 15%

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car(at all)

58%

bike(at all)

57%

Sustainable developement in modal-choice

PT (at all)

59%

car (only)

24%

bike (only)

13%

PT (only)

14%

car & PT5%

car& bike

4%

PT & bike 15%

PT & car

& bike

25%

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Thank you

for your attention and patience!

Walter Bien

Documents

A new approach for MM benefit-estimation Walter Bien ECOMM 2009 May 15 – San Sebastián