modelling cycling...definitions • current level of cycling (clc) number who regularly cycle work...

Modelling Cycling:

Potential Cycling & Potential Benefits

James Woodcock1, Alvaro Ullrich1, Robin Lovelace2

1CEDAR MRC Epidemiology Unit, 2University of Leeds

Summary of talk

• James

• Introducing CEDAR

• Introducing DfT National Propensity to Cycle Tool

• Robin

• PhD Spatial Micro Simulation

• Subsequent projects

• Alvaro

• Cambridge(shire) project

CEDAR, MRC Epidemiology Unit

• 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

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:

http://dx.doi.org/10.1136

/bmj.g146 Burgoine T,

Forouhi, Griffin,

Wareham, Monsivais

DfT: Provision of Research Programme into Cycling: Propensity to Cycle Tool

• Stage 1: Jan 2015 until June 2015

• Prototype model

• Stage 2?: June 2015 - ?

• National Propensity to Cycle Tool with health & carbon

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

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”

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

All Cycling Trips are not the Same?

• Which trips are cycled?

• Who cycles?

Carbon: Cumulative % of Distance by Trip Length

0

0.1

0.2

0.3

0.4

0.5

0.6

0.70.2

5 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

Cu

mu

lati

ve %

of

tota

l d

ista

nce (

so

lid

li

nes) /

% o

f d

ista

nce b

y c

ar (

dash

ed

li

nes)

Distance (miles)

London

SW Rural

Distance Decay Odds Cycling

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

<0.5 0.5 to

<1.5

1.5 to

<2.5

2.5 to

<3.5

3.5 to

<4.5

4.5 to

<5.5

5.5 to

<6.5

6.5 to

<9.5

9.5 to

<12.5

12.5 to

<15.5

15.5 to

<20.5

Female age 16-59 Female age 60+ Male age 16-59 Male age 60+

Harms to males

Benefits to males

Harms to females

Benefits to females

Males Females

-2500

-2000

-1500

-1000

-500

0

500

Ch

an

ge

in

DA

LY

s

Age group Age group

15-2930-4445-5960-6970-79 80+ 15-2930-4445-5960-6970-79 80+

-3000

Health Trade-offs of Cycling: Central London

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

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.

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

Modelling cycling uptake at individual, local and national levels

Robin Lovelace (University of Leeds)

Presented at the University of Cambridge

18th February 2015

Research interests

Current research: Twitter to calibrate SIM

Scenarios of cycling: national

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

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), 341–353. Retrieved from

http://onlinelibrary.wiley.com/doi/10.1111/j.1475-

4762.2008.00837.x/full

2. Health Behaviours

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

http://eprints.ncrm.ac.uk/3348/

Spatial microsimulation with FMF

The Flexible Modelling Framework is a free and open source Java program. It can be downloaded from https://github.com/MassAtLeeds/FMF

Spatial microsimulation with R

What is ‘spatial microsimulation’?

Generating spatial microdataSubtitle

Algorithm assigns weight to each individual

Original implementation in pure R

Now use faster ipfppackage (C)

Where do people travel?

Input: work-time population

Input: MSOA flow data

• Breakdown of flow by destination MSOA and mode of travel - published 25th July 2014

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

'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?

Future work

Lovelace, R., Ballas, D., & Watson, M. (2014). A spatial microsimulation approach for the analysis of commuter patterns: fromindividual to regional levels. Journal of Transport Geography, 34(0), 282–296.

Cambridgeshire:

Commuting Microsimulation

Alvaro Ullrich

CEDAR, MRC Epi Unit

Institute of Public Health

• 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

Cambridge model: Objectives

Cambridge model: Overview

4 constraints(.csv)

Flows (by MSOA)

Census 2011

public data

ind.csv

IPF(deterministic)+

4 populations combined

Categories combined

(+filtering)

[Route allocation –

ABM –Analysis]

[Probability

allocation]

Translate to

Map…

[Synthetic Population]

Cambridge model: Spatial level of detail

• 13 MSOAs (~5,000 people /each)

• Population weighted centroids

MSOA centroids apart ~1km vs. LSOA centroids <500 m

ACCURACY LIMIT

• 69 LSOAs (~1,000 people /each)

Cambridge model: choosing the variables

Flows by MSOA

What variables? [Age]- [Gender]- [Mode]- …. at LSOA/MSOA level

…. BUT: correlated, i.e. crosstabbed !! [Age ~ Mode] - [Mode~Distance] - [Gender~Mode]

Mode categ. (11)

Cambr. MSOA (13)

‘1 constraint, 1 var’

‘1 crosstab var’

Mode-Age categ. (11x 6)

Cambr. MSOA (13)

• 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

Availability of variables

Census Flows (added end 2014)

Flows by MSOA

Flows [age]-[gender]-[mode], MSOA to MSOA

Distance variable: Euclidean, added using ArcGIS (exact)

Option: Route length adjustment (LSOA)

Population weighted centroids (.shp)

Target Flows. Linked populations

City level flows: interflow - outflow- inflow - other

… although 4 Census populations:

1. interflow

2. outflow

3. inflow

Cambridge CC

4. other

Population (as per Census) #

I. Live UK, work Cambridge 85K

II. Live Cambridge, work UK 50K

III. Live Cambridge, work Cambridge 35 K

IV. Live Cambridge, work Other cat. 10 K

Processing populations

Total Working in city (I. LA_WC + IV. Other): ~94K

Residents working (II. LC_WC + IV. LC_WOth): ~60K

Daily Residents Outflow (II.+IV1,2 – III): ~17K

Daily Inflow (Total – WCity): ~51K

Get final combined populations (SQL language + ddbb):

Census dataset: flow data = 1 origin, 1 destination

translate

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…

Natural groups SP file

Data Protection: ‘How real is a ‘Synthetic Population?’

Results: some examples

Barnwell > Addenbrooke’s trips (mode) Mode distribution by MSOA (core vs periphery)

Thanks for listening!

• Any questions?

• Contacts:

• jw745@cam.ac.uk

• au232@medschl.cam.ac.uk

• Contact: r.lovelace@leeds.ac.uk

• @robinlovelace

• slides: robinlovelace.net

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.