Dynamic Models: An Example from the London Cycling System Dr James Woodcock: University of Cambridge Dr Rachel Aldred: University of Westminster Dr Alex Macmillan: UCL
Sixteen Reasons Other Than Prediction to Build Models: Epstein 2008 Why Model? 1.Explain (very distinct from predict) 2.Guide data collection 3.Illuminate core dynamics 4.Suggest dynamical analogies 5.Discover new questions 6.Promote a scientific habit of mind 7.Bound (bracket) outcomes to plausible ranges 8.Illuminate core uncertainties. 9.Offer crisis options in nearreal time 10.Demonstrate tradeoffs / suggest efficiencies 11.Challenge the robustness of prevailing theory through perturbations 12.Expose prevailing wisdom as incompatible with available data 13.Train practitioners 14.Discipline the policy dialogue 15.Educate the general public 16.Reveal the apparently simple (complex) to be complex (simple)
Understanding Complex Systems Complex systems include many interacting variables that change over time The pattern of interaction drives of system behaviour over time Interaction between variables is characterised by feedback loops Accumulation of stocks is important, including people, information, or resources Time matters. The pattern of cause and effect may change variables at different rates over time, creating tensions between short and longterm policy effects Models should make complexity tractable
Reinforcing & Balancing Loops
London Cycling Mode Share 6.0% 5.0% 4.0% Desired Historic Feared 3.0% 2.0% 1.0% 0.0% 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026
London Road Traffic Killed/ Seriously Injured Year 2000= 100 for total KSI 120 100 Hoped for Total Feared Total Hoped for Cyclists 80 Feared Cyclists Historic 60 40 20 0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026
London Cycling System Overall Diagram driver experience, awareness and acceptance cycling dominated by young to midage white men cycling among opinion leaders STIGMATISATION OF CYCLING ADVOCACY AND EFFECTIVE INTERVENTION R CYCLISTS ACROSS LONDON POPULATION population perception of safety and attractiveness R NORMALISATION OF CYCLING cycling part of everyday life B EXPERIENCED AND REPORTED DANGER R cyclists killed/seriously injured SAFETY IN NUMBERS cyclist injury rate advocacy for improved conditions R investment in improved conditions media portrayal of cycling as dangerous
Advocacy, Knowledge & Effective Investment CYCLISTS ACROSS LONDON POPULATION effective spend makes cycling feel safer r5 advocacy & activism about cycling, safety & infras tructure political will to improve cycling conditions b1 poor investment leads to disillusionment disillusioned activists "ignored warnings" population perception of safety b2 good infrastructure reduces pressure to act r3 effective spend lowers risks & increases cycling good examples r6 good examples encourage advocacy institutional knowledge & skills r4 knowledge loop effective policy spend "do something" policy spend r1 cycling injury rate visible spend initally encourages more cycling "doing something" initially gets people cycling r2 do something spend leads to ignored warnings
Safety in Numbers driver knowledge, awareness & acceptance of cyclists uptake CYCLISTS ACROSS LONDON POPULATION quitting r3 safety by awareness & acceptance deliberate aggressive behaviour 'accidentally' dangerous behaviour r1 safety by design population perception of cycling safety political will to improve conditions quality, maintenance & enforcement of cycling conditions b1 danger in numbers congestion on narrow cycle lanes CYCLIST FATAL AND SERIOUS INJURY RATE motor traffic volume r2 safety by mode shift
Conclusions We need models that capture dynamic complexity: why cycling not in choice set Relations between policy & advocacy culture & infrastructure stuff & meaning processes of normalisation/estrangement & stigmatisation Data & methods Models should make complexity tractable
Experienced & Reported Danger population perception of safety r2 new enthusiasts positive word of mouth uptake rate quality of initial experience b4 "trending" is news CYCLISTS ACROSS LONDON POPULATION b3 negative experience leads to active discouragement cycling a growing trend quit rate CYCLIST INJURIES b1 b5 cycling part of everyday life r1 b2 more injuries discourage policy makers no story in the everyday ratio cyclist deaths:other road traffic deaths cyclist cautiousness media focus on deaths deters strategic cycling promotion media portrayal & personal experience of cycling as dangerous b6 promotion has consequences negative word of mouth political accountability for cyclist KSI b7 safety campaigns make people nervous cyclist behavioural safety campaigns
Transport for London Road Network (TLRN)
UK Total Distance Driven & Cycled 800 700 Driven Cycled 25 Billion passenger km driven 600 500 400 300 200 20 15 10 5 Billion passenger km cycled 100 0 0 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2006 Source: National Transport Statistics UK
Trend in Cyclist Casualties by Severity http://londontransportdata.wordpress.com/category/subject/cycling/