Modelling Cycling: Potential Cycling & Potential Benefits. James Woodcock 1, Alvaro Ullrich 1, Robin Lovelace 2

Transcription

1 Modelling Cycling: Potential Cycling & Potential Benefits James Woodcock 1, Alvaro Ullrich 1, Robin Lovelace 2 1 CEDAR MRC Epidemiology Unit, 2 University of Leeds

2 Summary of talk James Introducing CEDAR Introducing DfT National Propensity to Cycle Tool Robin PhD Spatial Micro Simulation Subsequent projects Alvaro Cambridge(shire) project

3 CEDAR, MRC Epidemiology Unit

4 Woodcock J, Tainio M, Cheshire J, O Brien O, Goodman A. Health effects of the London bicycle sharing system: health impact modelling study. BMJ 2014;348

5 Associations between exposure to takeaway food outlets, takeaway food consumption, and body weight in Cambridgeshire, UK: population based, cross sectional study BMJ 2014; 348 doi: /bmj.g146 Burgoine T, Forouhi, Griffin, Wareham, Monsivais

6 DfT: Provision of Research Programme into Cycling: Propensity to Cycle Tool Stage 1: Jan 2015 until June 2015 Prototype model Stage 2?: June ? National Propensity to Cycle Tool with health & carbon

7 Stage 1 Evidence Review Interventions Which people, which trips Impact on inequalities Statistical analysis Who cycles & for which trips: England & Netherlands? Estimates need for creating Propensity to Cycle model

8 Stage 1 Modelling Health & Carbon benefits of switching trips to cycling: Two models/ two approaches: London & England Prototype model for three cities Scoping Report: How to build a National Propensity to Cycle model

9 Why a Propensity to Cycle Tool? Where to prioritise cycling investment? City by city Street by street Potential in terms of Cycling Health Carbon Inequalities Consider separately factors relating to Characteristics of trips Characteristics of people

10 All Cycling Trips are not the Same? Which trips are cycled? Who cycles?

11 Cumulative % of total distance (solid lines) / % of distance by car (dashed lines) Carbon: Cumulative % of Distance by Trip Length London SW Rural 0 Distance (miles)

12 Distance Decay Odds Cycling < to < to < to < to < to < to < to < to < to15.5 to <15.5 <20.5 Female age Female age 60+ Male age Male age 60+

13 Change in DALYs Health Trade-offs of Cycling: Central London 500 Males Females Harms to males Benefits to males Harms to females Benefits to females Age group Age group

14 Definitions Current level of cycling (CLC) number who regularly cycle work or leisure OR rate (trips/ week) Potential level of cycling (PLC) expected rate of cycling in an area or between origin-destination pairs (under certain assumptions). PLC is affected by Overall level of OR shift to cycling in the wider area Trip distances (see distance decay, below), Socio-demographics (and its influence on distance decay) Transport network (e.g. circuity and cycle infrastructure) Hilliness

15 Definitions Extra cycling potential (ECP) the number of additional trips or cyclists that would be expected in a given scenario. Distance decay relates distance of a trip to the probability (or odds) of it being made by a specific mode (e.g. by bicycle) with respect to explanatory variables such as the person's socio-demographic group and the hilliness. Circuity is the actual length of a trip along the transport network compared with the straight-line (Euclidean) distance.

16 Spatial Microsimulation Generating individual level data (usually at a small area level) starting from aggregate data Robin integrating with individual level dataset (usually national or regional) Alvaro hypothetical individual dataset- not real data

17 Modelling cycling uptake at individual, local and national levels Robin Lovelace (University of Leeds) Presented at the University of Cambridge 18 th February 2015

18 Research interests

19 Current research: Twitter to calibrate SIM

20 Scenarios of cycling: national

21 Spatial Microsimulation Two definitions of spatial microsimulation A method for combining individual-level data with aggregate-level data An approach to policy evaluation and analysis Generating spatial microdata Deterministic method (IPF) Probabilistic (combinatorial optimisation) Uses of spatial microdata Input into ABMs Analysis of sub-regional issues Basis for 'what if' scenarios

22 Applications: 1 - Smoking rate Tomintz et al (2008). The geography of smoking in Leeds: estimating individual smoking rates and the implications for the location of stop smoking services. Area, 40(3), Retrieved from oi/ /j x/full

23 2. Health Behaviours Lovelace, R. (2014). Introducing spatial microsimulation with R: a practical. National Centre for Research Methods, 08(14). Retrieved from

24 Spatial microsimulation with FMF The Flexible Modelling Framework is a free and open source Java program. It can be downloaded from

25 Spatial microsimulation with R

26 What is spatial microsimulation?

27 Generating spatial microdata Subtitle Algorithm assigns weight to each individual Original implementation in pure R Now use faster ipfp package (C)

28 Where do people travel?

29 Input: work-time population

30 Input: MSOA flow data Breakdown of flow by destination MSOA and mode of travel - published 25th July 2014

31 Assignment to travel network Next stage: allocate flows to roads/paths New software available to do this Google/CycleStreets API PG Routing ggmap/igraph/r Evaluation of local policies

32 'What if' scenarios A 'snapshot' scenario of a future state 'What-if peoples willingness to cycle doubled for every trip distance?' What if people cycled further?' 'What if male-female differences in cycling reduce?' What if new cyclists have different needs than existing cyclists?

33 Future work Lovelace, R., Ballas, D., & Watson, M. (2014). A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels. Journal of Transport Geography, 34(0),

34 Cambridgeshire: Commuting Microsimulation Alvaro Ullrich CEDAR, MRC Epi Unit Institute of Public Health

35 Cambridge model: Objectives Goal: accurate picture of city commuting trips (residents + inflow) 1st attempt to use microsim Sources: Census aggregates 2011 IPF method (deterministic) (incursions on probabilities) Tools: R Data analysis SQL Databases-ArcGIS

36 Cambridge model: Overview Census 2011 public data 4 constraints (.csv) + [Probability allocation] IPF (deterministic) 4 populations combined ind.csv Categories combined (+filtering) Translate to Map Flows (by MSOA) [Synthetic Population] [Route allocation ABM Analysis]

37 Cambridge model: Spatial level of detail 13 MSOAs (~5,000 people /each) Population weighted centroids MSOA centroids apart ~1km vs. LSOA centroids <500 m 69 LSOAs (~1,000 people /each) ACCURACY LIMIT

38 Cambridge model: choosing the variables What variables? [Age]- [Gender]- [Mode]-. at LSOA/MSOA level 1 constraint, 1 var Mode categ. (11) Flows by MSOA Cambr. MSOA (13). BUT: correlated, i.e. crosstabbed!! [Age ~ Mode] - [Mode~Distance] - [Gender~Mode] 1 crosstab var Mode-Age categ. (11x 6) Cambr. MSOA (13)

39 Availability of variables Challenge: getting crosstab + MSOA/LSOA ( more info, less detail ) Assumption: corr. hold at MSOA/LSOA level Allocate individuals by MSOA (multinomial distr.) The Lego-IKEA problem: IPF finds best correlation (Math) IRL: Multiple solutions

40 Census Flows (added end 2014) Flows [age]-[gender]-[mode], MSOA to MSOA Distance variable: Euclidean, added using ArcGIS (exact) Flows by MSOA Option: Route length adjustment (LSOA) Population weighted centroids (.shp)

41 Target Flows. Linked populations City level flows: interflow - outflow- inflow - other although 4 Census populations: Cambridge CC Population (as per Census) # 1. interflow I. Live UK, work Cambridge 85K II. Live Cambridge, work UK 50K 2. outflow III. Live Cambridge, work Cambridge 35 K 4. other IV. Live Cambridge, work Other cat. 10 K 3. inflow

42 Processing populations Census dataset: flow data = 1 origin, 1 destination Get final combined populations (SQL language + ddbb): translate Total Working in city (I. LA_WC + IV. Other): ~94K Residents working (II. LC_WC + IV. LC_WOth): ~60K Daily Residents Outflow (II.+IV 1,2 III): ~17K Daily Inflow (Total WCity): ~51K

43 Results: Synthetic Population Results: 1 data file. Next: clustering using Mach.Learning. Next: better mapping & Visualisation Check vs. real data: CC cordon data, transport aggregates SP file Natural groups Data Protection: How real is a Synthetic Population?

44 Results: some examples Barnwell > Addenbrooke s trips (mode) Mode distribution by MSOA (core vs periphery)

45 Thanks for listening! Any questions? Contacts: slides: robinlovelace.net

46 ACKNOWLEDGEMENT This work was undertaken by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence. Funding from Cancer Research UK, the British Heart Foundation, the Economic and Social Research Council, the Medical Research Council, the National Institute for Health Research, and the Wellcome Trust, under the auspices of the UK Clinical Research Collaboration, is gratefully acknowledged.