benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 1 Quick & Simple – a new approach for benefit-estimation of mobility-management (MM) and other soft-policies by statistical data Walter Bien, head of sales und customer-care traffiQ – local public transport authority (city of frankfurt, germany) European Conference on Mobility Management, ECOMM 2008, London

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 2 Overview 1. The problem: effect estimation of measures 2. Structural data: important for modal-choice / available 3. Combining structural data with passenger numbers in PT 4. Example: developement and results in the frankfurt urban region 5. Next steps and chances

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 3 1. The problem: effect estimation of measures (a) year Public Transport passengers change- rate , ,47,9% 2007*183,80,2% success of mobility management ??? * means: preliminary

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 4 1. The problem: effect estimation of measures (b) success of mobility management … could be ? year PT- passengers income by ticket- sales change- rate ,0 117, ,4 137,317,3% 2007*183,8 167,021,6%

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 5 1. The problem: effect estimation of measures (c) success of mobility management … yes ! year inhabi- tants emplo- yees inhab.+ employ. change- rate ,0% 2007* ,3%

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 6 1. The problem: effect estimation of measures (d)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 7 1. The problem: effect estimation of measures (e)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 8 2. Structural data: important for modal-choice / available (a)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 9 2. Structural data: important for modal-choice / available (b)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Structural data: important for modal-choice / available (c) … on the next slide – see the combination

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 11

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Combining structural data with passenger-numbers in public-transport (a) 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

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Combining structural data with passenger- numbers in public- transport (b)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Combining structural data with passenger- numbers in public- transport (c)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Combining structural data with passenger- numbers in public- transport (d)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Example: developement and results in the Frankfurt urban region (a) 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.

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Example: developement and results in the Frankfurt urban region (b)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Example: developement and results in the Frankfurt urban region (c)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Example: developement and results in the Frankfurt urban region (d) ~ 20 Mio. EURO

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Next steps and chances (a) 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.

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) Next steps and chances (b) 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!

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 22 … and so – we reach her/him: the multi-modal mobility-user 5. Next steps and chances (c)

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 23 car (at all) 82% bike (at all) 40% Today: modal-choice of the inhabitants of Frankfurt (~ persons) 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%

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 24 car (at all) 69% bike (at all) 56% Tomorrow: Sustainable developement in modal-choice PT (at all) 57% car (only) 35% bike (only) 16% PT (only) 17% car & PT 5% car & bike 4% PT & bike 15% PT & car & bike 25%

benefit-estimation for mobility management by stat. data Walter Bien ECOMM-2008 (London, June-2008) 25 Thank you for your attention and patience! Walter Bien