A system dynamics approach to transport modelling
Upcoming SlideShare
Loading in...5
×
 

A system dynamics approach to transport modelling

on

  • 274 views

 

Statistics

Views

Total Views
274
Views on SlideShare
257
Embed Views
17

Actions

Likes
0
Downloads
5
Comments
0

1 Embed 17

https://twitter.com 17

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment
  • This is similar to a Bass diffusion model or product diffusion – link to agent based modelling and later example on AFV uptake
  • Found our stuff scribbled on the margins of documents leaked from the negotiations

A system dynamics approach to transport modelling A system dynamics approach to transport modelling Presentation Transcript

  • A System Dynamics Approach to Transport Modelling Simon Shepherd Institute for Transport Studies University of Leeds (UK) S.P.Shepherd@its.leeds.ac.uk
  • Aims • Introduction Systems Dynamics • Some examples • Challenges
  • System Dynamics • System dynamics is a computer-aided approach to policy analysis and design. It applies to dynamic problems arising in complex social, managerial, economic, or ecological systems -- literally any dynamic systems characterized by interdependence, mutual interaction, information feedback, and circular causality
  • Introduction :principles of Systems Dynamics • Representation of systems Qualitative Quantitative Verbal description Cause-effect diagrams Flow charts Equations
  • Elements of CLD Entities: are elements which affect other elements and get affected themselves. An entity represents an unspecified quantity. See Stocks later Number of motorways + - s o Links: Entities are related by causal links, shown by arrows. Each causal link is assigned a polarity, either positive (+, s) or negative (-, o) to indicate how the dependent entity changes when the independent entity changes.
  • CLD example • Simple example Eggs Chicken + + etc. Time Population Reinforcing feedback loop +
  • CLD example 2 • Simple example 2 Eggs Chicken + + + # Road crossing + - etc. Time Population Balancing feedback loop -
  • CLD transport example • “Congestion relief” by new road infrastructure Need for new highways Highways being built Number of Highways Number of traffic jams Attractiveness of driving on highways + + + + + - + - Source: Roberts, N.; et. al., Introduction to Computer simulation: The System Dynamics Approach. ed.; Addison-Wesley Publishing Company: London Amsterdam Don Mills Ontario Sydney, 1983
  • Stocks and flows Stock inflow outflow    t t tStockdssoutflowsInflowtStock 0 )()()()( 0
  • Chickens birthsdeaths eggs + + road crossings + + - Chickens 1,000 500 0 0 2 4 6 8 10 Time (Month) Chickens : with crossings Chicken and eggs model Note : 𝑑𝑒𝑎𝑡ℎ𝑠(𝑡) = 𝑟𝑜𝑎𝑑 𝑐𝑟𝑜𝑠𝑠𝑖𝑛𝑔𝑠(𝑡)2 1000
  • Population births deaths birth rate death rate Population 800 400 0 0 20 40 60 80 100 Time (Month) Rabbit Population : Current Simple population model 𝒑𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 = 𝒃𝒊𝒓𝒕𝒉𝒔 − 𝒅𝒆𝒂𝒕𝒉𝒔 𝒅𝒆𝒂𝒕𝒉𝒔 = 𝒑𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 ∗ 𝒅𝒆𝒂𝒕𝒉 𝒓𝒂𝒕𝒆 𝒃𝒊𝒓𝒕𝒉𝒔 = 𝒑𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 ∗ 𝒃𝒊𝒓𝒕𝒉 𝒓𝒂𝒕𝒆 Population Young births aging young average time in young birth rate Population Middle Population Old aging middle aging old average time in middle average time in old initial pop infant initial pop middle initial pop old
  • Fox Population fox food availability fox food requirements average fox life fox consumption of rabbits fox birth rate initial fox population fox mortality lookup fox births fox deaths Rabbit Population rabbit births rabbit crowding carrying capacity average rabbit liferabbit birth rate initial rabbit population effect of crowding on deaths lookup fox rabbit consumption lookup rabbit deaths Rabbit Population 4,000 2,000 0 0 10 20 30 40 50 Time (Year) Rabbit Rabbit Population : Current Fox Population 200 100 0 0 10 20 30 40 50 Time (Year) Fox Fox Population : Current
  • Susceptible Population Infected Population infections rate of potential infectious contacts rate that people contact other people Fraction of population infected total population Contacts between infected and unaffected fraction infected from contact initial infectedinitial susceptible Susceptible Population 1 M 750,000 500,000 250,000 0 0 10 20 30 40 50 Time (day) Person Susceptible Population : Current Infected Population 1 M 500,000 0 0 10 20 30 40 50 Time (day) Person Infected Population Simple epidemic model
  • Example – uptake of Electric Vehicles
  • Extended - Struben and Sterman (2008) • Consideration of three types of car: conventional vehicle (CV), Plug-in Hybrid (PIHV), and Battery Electric (BEV), • inclusion of choice model coefficients from a UK-based SP study (Batley et al, 2004), • inclusion of a price-volume effect • calibration to match the “business as usual” projection by BERR (2008) • testing a failing market case where we remove high profile marketing, • inclusion of a “revenue preserving” tax designed to replace any loss in revenues from fuel duty, • estimation of CO2 emissions Source: Shepherd, S.P., Bonsall, P.W., and Harrison G. (2012) Factors affecting future demand for electric vehicles : a model based study. Transport Policy, (20) March 2012, pp 62-74. DOI :10.1016/j.tranpol.2011.12.006
  • Struben and Sterman (2008) Take up of AFV
  • Calibrated to BERR 2030
  • Sensitivity to word of mouth Word of mouth between CV drivers is crucial for success – as was marketing
  • Example CM/failing regime vs BAU market shareEV 0.4 0.3 0.2 0.1 0 0 4 8 12 16 20 24 28 32 36 40 Time (Year) marketshare EV[PIHV]:BAUbase marketshare EV[PIHV]:BAUfailing marketshare EV[BEV]:BAUbase marketshare EV[BEV]:BAUfailing Willingnessto considerEV 1 0.75 0.5 0.25 0 0 4 8 12 16 20 24 28 32 36 40 Time(Year) WillingnesstoconsiderEV:BAUbase WillingnesstoconsiderEV:BAUfailing Willingness to consider collapses when high profile marketing is removed in year 10
  • Tipping point analysis Change required by year 10 to maintain marketing threshold and hence a successful marketing regime: • a 6.8% increase in CV operating costs • a 10.6% decrease in PIHV operating costs • a 66% decrease in BEV operating costs • 160 mile range for BEV • 130mph max speed for BEV; or • fuel availability increasing from 40% to 55% for BEV • Subsidies were seen to be crucial in the failing/CM case – but at a cost!
  • Control panel to vary scenarios Installed base EV 10 M 5 M 0 4 4 4 4 3 3 3 3 2 2 2 2 2 1 1 1 1 1 0 6 12 18 24 30 36 Time (Year) Installed base EV[PIHV] : BEV-range-300-20 1 1 Installed base EV[PIHV] : Low case 2 2 Installed base EV[BEV] : BEV-range-300-20 3 Installed base EV[BEV] : Low case 4 4 sales EV 1 M 500,000 0 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 1 0 8 16 24 32 40 Time (Year) sales EV[PIHV] : BEV-range-300-20 1 1 1 sales EV[PIHV] : Low case 2 2 2 2 sales EV[BEV] : BEV-range-300-20 3 3 sales EV[BEV] : Low case 4 4 4 subsidy duration 1 3010 subsidy BEV 0 10,0000 Initial fuel availability BEV 0 105 Initial operating cost BEV 1 2012 Initial range BEV 0 50.8 Initial emission rating BEV 0 105 BEV Attributes pence/mile miles/100 0-10 with 10 poor 0-10 with 10=100% Initial max speed BEV 1 209mph/10 Short Term Sales 600,000 300,000 0 4 4 4 4 3 3 3 3 2 2 2 2 1 1 1 1 1 0 4 8 12 16 20 Year sales EV[PIHV] : Low case 1 1 1 sales EV[BEV] : Low case 2 2 2 2 sales EV[PIHV] : BEV-range-300-20 3 3 sales EV[BEV] : BEV-range-300-20 4 4 SW Price Volume ON 0 11 Market Shares 2010-2050 0.4 0.2 0 4 4 4 4 3 3 3 3 3 2 2 2 2 2 1 1 1 1 1 0 8 16 24 32 40 Year market share EV[PIHV] : BEV-range-300-20 1 1 market share EV[BEV] : BEV-range-300-20 2 "Ricardo Low % PIHV" : BEV-range-300-20 3 "Ricardo Low % BEV" : BEV-range-300-20 4 4 final range BEV 0 43 Time final range BEV 1 4020 range BEV 4 0 2 2 21 1 1 1 0 12 24 36 Time (Year) range BEV : BEV-range-300-20 1 range BEV : Low case 2 Price BEV 20 10 2 2 2 1 1 1 0 14 28 Time (Year) Price BEV : BEV-range-300-20 Price BEV : Low case 2 final fuel availability BEV 1 105 Time final fuel availability BEV 1 4040 fuel availability BEV 6 4 2 2 21 1 1 1 0 12 24 36 Time (Year) fuel availability BEV : BEV-range-300-20 fuel availability BEV : Low case final operating cost BEV 0 2012 Time final operating cost BEV 1 4040 final max speed BEV 6 129 Time final max speed BEV 1 4040 final emission rating BEV 0 105 Time final emission rating BEV 1 4040 Initial operating cost PIHV 10 2017pence/mile final operating cost PIHV 5 2017 Time final operating cost PIHV 1 4040 Initial operating cost CV 10 2522 final operating cost CV 5 3022 Time final operating cost CV 1 4040 subsidy PIHV 0 10,0000 initial budget 100 M 1 B500 M budget limited 0 10 PIHV and CV Operating costs
  • Some of the conclusions • BAU assumptions are crucial! • Word of mouth assumptions can have a larger impact • Subsidies have no real impact in BAU but are crucial in a failing market – but expensive! (required for 6 years minimum – could cost in excess of £500m depending on other factors) • If EVs take off then we see significant loss of fuel duty = £10bn p.a. 2050 in most optimistic case. • Revenue preserver per vehicle could range between £300- £650 p.a. by 2050. • A further 9% reduction in emissions from CV gives similar results in terms of CO2 at much lower cost to government.
  • Some other examples • Over 50 journal papers since 1994 • Shepherd, S.P. (2014) A review of system dynamics models applied in transportation. Transportmetrica B: Transport Dynamics, 2014. http://dx.doi.org/10.1080/21680566.2014.916236 • Examples cover 6 main areas – airports and airlines, strategic polic/regional models, supply chain management with transport, highway construction/maintenance, uptake of AFVs and miscellaneous.
  • EU White paper challenge • Halve the use of ‘conventionally fuelled’ cars in urban transport by 2030; phase them out in cities by 2050;
  • Future challenges
  • Behaviour change
  • Growth and business cycles
  • Uncertainty Source adapted from Zurek, M. and T. Henrichs (2007): Linking scenarios across geographical scales in international environmental assessments. Technological Forecasting and Social Change.
  • Technology or behaviour change?
  • C-ROADS at COP-15 • Scoreboard went viral • Real-time analysis picked up by media, negotiators • US State Dept used as common platform, picked up by other delegations “This capability, had it been available to me when we negotiated Kyoto, would have yielded a different outcome.” Tim Wirth, President, UN Foundation, former Senator
  • Summary • SD has been applied widely in transport problems • It has the advantage of being transparent (with client involvement in building CLDs) • Small models can show underlying structure and dynamics of the problem – providing new insights • Can deal with cycles, resource limits, lagged responses, softer variables • Easy to introduce scenario and sensitivity analysis • Can deal naturally with cohorts (population or fleet) • Can bring in more systems and learn from structures in other fields
  • Summary 2 • Provides a holistic approach to modelling • Not suited to traditional network assignment problems • Future applications - competition dynamics, freight and the development of ports, sensitivity of systems and transport demand to changing external factors related to demographics and the economy; • modelling behavioural change whether this is at the user level of some higher level stakeholder • modelling the decision making process and game playing to inform
  • And finally • “System dynamics helps us expand the boundaries of our mental models so that we become aware of and take responsibility for the feedbacks created by our decisions”, Sterman (2002).
  • Thank you for listening S.P.Shepherd@its.leeds.ac.uk