MODELLING RESPONSES TO TOMORROW S SOCIETY. Luis Willumsen

Transcription

1 MODELLING RESPONSES TO TOMORROW S SOCIETY Luis Willumsen

2 The future is always now even uncertain more uncertain Political: those left behind, populism Behavioural: peak-car, social networks Technology: Autonomous Vehicles, AI Work: free-lancers, distant presence Shopping, Leisure, News Energy sources and prices Additive Manufacturing Environment 2

3 Modelling should help dealing with this uncertainty Uncharted waters require envisioning possible futures Current models may rely on assumptions no longer valid How do we develop better modelling tools? 3

4 New data sources should help Mobile devices Sensors 4

5 Back to Basics: How to model transport in tomorrow s society 1 Ask the right question 2 Recognise the difficulties 3 Accept limitations 4 Identify what matters most 5 Select an approach 6 Test and improve 5

6 1 Ask the right question What How is is do the we best most provide model accurate the to best forecast model transport to transport forecast policy and transport investment impacts impacts in advice future s in future s for society? future s society? society (supported by models)? 6

7 2 Four key difficulties Homo Economicus Choice: basis of our models Humans are partly emotional and collaborative, not 100% rational, selfish and omniscient Brain is continuously rewired, influenced by others, experience and values: we change our minds The stability of model parameters We do different things every day; non-recurrent trips are increasingly important New technologies, evolution of demographics, work practices and social values The Activity Regularity Assumption Changing context 7

8 No onal Uncertainty 3 Accept limitations 60% No onal sources of uncertainty with variability in scenarios 50% 40% Scenario uncertainty 30% 20% Future data requirements 10% Base Year Data Model quality 0% Years a er original forecast 8

9 No onal Uncertainty 3 Accept limitations with greater uncertainty 60% No onal sources of uncertainty with variability in scenarios (a) 50% 40% Greater Scenario uncertainty 30% 20% Future data requirements 10% Base Year Data Model quality 0% Years a er original forecast 9

10 A single future is not forecastable 10

11 4 Identify what matters most Policies that work well under different futures Policies that work under one future but may be adapted easily and quickly to other contexts Projects that work well under all probable futures Projects that can be adapted to different contexts at low cost and quickly (Real Options) Plans and Policies that are flexible and can be adapted to changing futures THIS IS NOT THE WAY WE THINK TODAY 11

12 5 Select an approach In the modelling short-term, use existing tools better Develop improved modelling tools in the medium term Use of new data sources will help Some new tools will not be models but approaches to deal with multiple futures: scenario planning, visualisations, collaborative forecasting and decision making. 12

13 Better use of existing modelling tools Acknowledge uncertainty Use conventional tools but allow for adjustments due to imperfect assumptions: for example lags in responses Identify winners and losers and by how much Account separately for large and small loses/gains Recognise the subjective and judgemental elements in models and forecasts PLANNING

14 Pointers for new improved models 1 Research to deal with the four key difficulties: Homo Sapiens instead of Homo Economicus Very difficult; Prospect Theory only a step Activity Regularity Assumption Difficult; but new data will enable us to model a greater variety of days Fixed Preferences (parameters) Need to understand better how preferences change and why New contexts, spotted swans Deal with uncertainty: scenario development; share knowledge, wisdom of crowds 14

15 Revenue Factor Pointers for new improved models 2 Learn to live with, and use, uncertainty Old and New tools to deal with uncertainty Delphi, Drivers Analysis, Horizon Scanning, etc. Better understanding and presentation of forecast uncertainty Model de-construction Stochastic Risk Analysis Scenario Planning Real Options Year

16 Pointers for new improved models 3 More experimental (rather than theoretical) research, new data sources Research precursors to the new transport issues: UBER, MaaS Car Clubs/Sharing Gig Economy Regional differences Internet procurement Distant presence, VR From operational and behavioural perspectives We need a shared, intelligent monitoring of these trends before they can be incorporated in models 16

17 THANK YOU