Modelling the energy savings of a 'Get Britain Cycling' scenario of modal shift

Presented at the RGS-IBG annual conference. Links to the DfT's National Travel Model (NTM) and the mismatch between official aspirations on cycling and NTM projections.

Transcript

1. Modelling the energy savings of a 'Get Britain Cycling' shift to cycling Robin Lovelace, University of Leeds 28th August, 2014 RGS-IBG, London Slides Available: robinlovelace.net
2. What I'm going to talk about 1) The Get Britain Cycling vision 2) Context + approach 3) Time series modelling 4) Method + next steps 5) Discussion + Conclusions
3. 1) Introduction – the vision The most important impacts of cycling may only emerge in the future. Yet most cycling policy evaluation, and cycle promotion focusses on benefits in the here and now. Considering energy impacts encourage long-term thinking. Credit: Oil Drum Article (2009) http://campfire.theoildrum.com/node/5976
4. What is Get Britain Cycling? • GBC = optimistic vision of a cycling nation • Scenario with quantitative targets • Overview of policies needed to get there • Produced for MPs by the APPCG, April 2013
5. • “the long-term ambition should be to increase cycle use to 10% of all journeys in 2025, and 25% by 2050” • Case studies of growth (e.g. Devon) • Funding: needs > £10 person/yr The GBC report “For technical reasons, computer modelling and forecasting has played little role in assessing the future potential of the volume of cycling” (Goodwin 2013)
6. Limitations of GBC • Lacks timeline beyond targets for 2025 and 2050 • No geographical disaggregation of cycling uptake • Nothing on who would be cycling where, replacing which modes and for what trip types • Basically, great overview, scant on detail • So the first stage was to create scenarios
7. 2) Research context + approach
8. Dynamic modelling of populations See Lovelace et al. (2011) • Borrowed equation from population ecology • 3 scenarios of growth in Sheffield • Wide boundary energy savings explored P: population r: max. Growth rate K: carrying capacity
9. Limitations of the paper • No geographic disaggregation • Little evidence on carrying capacity • Little evidence on growth rates • Scarce case studies in UK • Useful concepts • Now: better data
10. The Replacement Ratio • Critical to environmental impact of a bike trip is how many car trips it replaces • Varies over space, time and for different trips • Difficult to estimate
11. 3) Time series modelling
12. Things can be linear for a while... • Doubling in 8 yr • “Levelling off” • Proxy for number of cycle trips in London • From “sixty automatic cycle counters” • Adjusted to account for season
13. But we do not live in a linear world!
14. Linear and “doubling in 10 yr” models • Realistic? • Almost there for 2050 target • Way off 2025 target
15. The Parliamentary Question (PQ)
16. The “Official” model in context • Based on economic growth, car-focussed • Cyclist Touring Club lobbied DfT to rectify this • NTM's cycling projections now being updated by Philipp Thiessen – lobbying may have worked! Forec ast year Cycle trips - billion Cycle miles- billion Dista nce per trip 2010 1.2 2.9 2.4 2015 1.4 3.4 2.4 2020 1.3 3.2 2.5 2025 1.3 3 2.3 2030 1.3 3.1 2.4 2035 1.4 3.1 2.2 2040 1.4 3.1 2.2
17. Increasing distance of bicycle trips • Increased potential to replace car trips? • Or just “Wiggo effect”? – Leisure/utility trips? • Clashes with DfT model Source: https://www.gov.uk/government/publications/national-travel-survey-2012
18. Comparison of data and models
19. Distance-decay varies with mode and reason for trip... Source: My Thesis, based on Iacono et al. (2011)
20. The Geo-dimension Flows into Sheffield's 4 largest Employing Output Area zones
21. Distance decay functions – look at the overlaps
22. Method: next steps • Decide best functional form for distance decay • Adjust distance decay functions by reason for trip (e.g. Shopping, school, work) • Disaggregate the results geographically – Regional data in NTS – Smaller scale estimates from commute patterns? • Calculate CO2 emissions based on energy saving estimates • Implement dynamic model spatially -> associated energy savings in time + space
23. What I expect the results to look like
24. Discussion • Need to factor-in possibility for behaviour change (e.g. People cycling further, telecommuting) • The “Amazon effect” - people shopping less outside • Electric bicycles: elephant in the room • Impact of demographic shift -> spatial microsimulation model
25. 5) Conclusions • It is hard to imagine, let alone model, a future of very high cycling uptake • Logistic growth seems most plausible • The National Transport Model should be reconciled with trends and the GBC scenario • The energy savings of cycling depends primarily on what modes cycling replaces • Modelling process has 2 major benefits: – Making visions of the future more tangible – Identification of long-term needs