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

2. 2 Eltis Café @ ECOMM 2008 June 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. 3 Eltis Café @ ECOMM 2008 June 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

4. 4 Eltis Café @ ECOMM 2009 year Public Transport passengers change- rate (values in millions) 1995 170,0 2001 183,47,9% 2007* 183,80,2% success of mobility management ??? * means: preliminary … starting with mobility management measures in the year 2000 … establish mobility management in the following years

5. 5 Eltis Café @ ECOMM 2009 success of mobility management … could be ? year PT- passengers income by ticket- sales change -rate (values in millions) 1995170,0 117,0 2001183,4 137,317,3% 2007*183,8 167,021,6% … but in the same two periods we have a strong increase of income by ticket sales (based on a higher price level)

6. 6 Eltis Café @ ECOMM 2009 success of mobility management … yes ! year inhabi- tants emplo- yees inhab.+ employ. change -rate (all values in thousands) 1995653548 1.201 2001646603 1.249 4,0% 2007*668610 1.2782,3% … the increase of customer potential (inhabitants and employees) is less in the second period

7. 7 Eltis Café @ ECOMM 2009 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%

8. 8 Eltis Café @ ECOMM 2009 The problem: effect estimation of measures … we can see non effect of fuel prices on the developement of PT passengers

9. 9 Eltis Café @ ECOMM 2009 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.

10. 10 Eltis Café @ ECOMM 2009 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 ?

11. 11 Eltis Café @ ECOMM 2009 2.The classical (best) approach: Evaluation of treatment groups and placebo-groups But also (Herbert Kemming, germany): … The control group method … and its problems

12. 12 Eltis Café @ ECOMM 2009

13. 13 Eltis Café @ ECOMM 2009 3.Estimation of change in the mobility/traffic area (modal split, PT passenger numbers, …) using statistical data (inhabitants, number of cars, commuters, PT offer, …)

14. 14 Eltis Café @ ECOMM 2009 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

15. 15 Eltis Café @ ECOMM 2009

16. 16 Eltis Café @ ECOMM 2009

17. 17 Eltis Café @ ECOMM 2009 … on the next slide – see the combination

18. 18 Eltis Café @ ECOMM 2009

19. 19 Eltis Café @ ECOMM 2009 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

20. 20 Eltis Café @ ECOMM 2009 Combining structural data with passenger- numbers in public- transport

21. 21 Eltis Café @ ECOMM 2009 Combining structural data with passenger- numbers in public- transport

22. 22 Eltis Café @ ECOMM 2009 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 Combining structural data with passenger- numbers in public- transport

23. 23 Eltis Café @ ECOMM 2009 4.Comparison of estimated and measured values The convincing argument: Decisive – is the final result ! In german: … was hinten rauskommt.

24. 24 Eltis Café @ ECOMM 2009 Combining structural data with passenger- numbers in public- transport … now we can see the difference between the (realized) number of PT passengers and the expected number (target value) of PT passengers …

25. 25 Eltis Café @ ECOMM 2009 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

26. 26 Eltis Café @ ECOMM 2009 5.Example: Development of the number of PT passengers in Frankfurt from 1995 to 2010

27. 27 Eltis Café @ ECOMM 2009

28. 28 Eltis Café @ ECOMM 2009 ~ 20 Mio. EURO

29. 29 Eltis Café @ ECOMM 2009 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.

30. 30 Eltis Café @ ECOMM 2009 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

31. 31 Eltis Café @ ECOMM 2009 … and so – we reach her/him: the multi-modal mobility-user

32. 32 Eltis Café @ ECOMM 2009 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 & PT 16% car & bike 14% PT & bike 5% PT & car & bike 15%

33. 33 Eltis Café @ ECOMM 2009 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 & PT 5% car & bike 4% PT & bike 15% PT & car & bike 25%

34. 34 Eltis Café @ ECOMM 2009

35. 35 Eltis Café @ ECOMM 2009 Thank you for your attention and patience! Walter Bien