sdna | spatial design network analysis - modelling …...• modelling pedal cycle usage and flows...

• www.cardiff.ac.uk/sdna www.cardiff.ac.uk/sdna

Modelling pedal cycle usage and flows

with spatial network analysis

Dr Crispin Cooper

28th January 2015

• www.cardiff.ac.uk/sdna

Active Travel (Wales) Act 2013

The Welsh Government seeks to enable more people to walk, cycle and generally travel by more active methods, so that:

● more people can experience the health benefits of active travel;

● we reduce our greenhouse gas emissions;

● we help address poverty and disadvantage, and;

● we help our economy to grow by unlocking sustainable economic growth.

Research indicates that for many people, the biggest barrier to walking and cycling is concern for their safety

(From design guidance)


Active Travel (Wales) Act 2013

The Active Travel Act makes provision -

a) for approved maps of existing active travel routes and related facilities in a local authority’s area,

b) for approved integrated network maps of the new and improved active travel routes and related facilities needed to create integrated networks of active travel routes and related facilities in a local authority’s area,

c) requiring local authorities to have regard to integrated network maps in preparing transport policies and to ensure that there are new and improved active travel routes and related facilities,

d) requiring the Welsh Ministers to report on active travel in Wales,

e) requiring the Welsh Ministers and local authorities, in the performance of certain functions under the Highways Act 1980, to take reasonable steps to enhance the provision made for walkers and cyclists and to have regard to the needs of walkers and cyclists in the exercise of certain other functions, and

f) requiring the Welsh Ministers and local authorities to exercise their functions under the Act so as to promote active travel journeys and secure new and improved active travel routes and related facilities.


Need for modelling

• Much is known about schemes to get people cycling

• Much is known about the desirability of certain types of infrastructure, e.g. boxes, lanes, traffic free cycle routes

• Less is known about

– how to decide where to focus limited resources for improvement

– how to quantify the effect of these features on the decision to cycle

– How cyclists behave en masse on a city wide scale, i.e. transport modelling of cyclists, and hence how best to integrate new infrastructure with the existing network


Direct demand modelling

• 4-stage transport models (generation, distribution, mode choice, assignment)

– Very costly to calibrate (origin/destination balancing factors; home and intercept surveys)

– Typically too low resolution to capture built environment effects on cyclist and pedestrian behaviour e.g. cycle paths encourage cycling

– Fail to capture land use/accessibility feedback cycle or residential self-selection

• sDNA direct demand / sketch model

– Small number of parameters to estimate (no balancing factors just trip lengths)

– High resolution so can capture effect of built environment on mode choice

– Does account for land use/accessibility feedback


Types of modelling

Multivariate analysis Assignment modelling Micro-simulation Purpose Establish a statistical link

between network configuration and movement

Incorporate cycle movement into a traditional transport modelling and evaluation framework

Understand cycle comfort and safety at a detailed level

Role in the design process

Option generation and testing

Planning, feasibility, appraisal

Detailed planning and design

Spatial Scale Large urban area or neighbourhood wide

Large urban area or neighbourhood wide

Junction, individual station (interchange node), individual place

Method Calculation of the statistical relationship between activity density distribution and network Calculation of potential movement

Calculation of change in trip production / attraction Cycle route assignment model

Simulation of cycle movement and interaction Calculation of density measurements

Cost Low Medium High Information cost Low Medium High Level of operation Strategic Strategic to tactical Tactical


Challenges in measuring cycle flows

Department for Transport pedal cycle flows – annual average daily traffic (AADT)

– derived from a mixture of vehicle gates and manual sampling at 107 locations in Cardiff

• Vehicle gates may miss cycles

• Manual counts may not be on representative day

– only locations on roads carrying vehicle traffic are recorded

– weekdays March-October are ‘neutral days’ • may underestimate recreational pedal cycle traffic on weekends

– AADT is estimated by applying expansion factors based on type of road, day of year and type of vehicle

• takes account of national weather variations but discards regional

– in some cases roads are not sampled at all, but flows are estimated by applying a growth factor to previous year


Challenges in measuring cycle flows

• Cardiff Council’s data – Collected from 14 electronic cycle

counters on traffic free routes over a 3 month period.

– 15th counter records flows all years round

– Only daily flows for the recorded months (September-November 2014)

– Much better data but only 14 counts and only limited period


DfT and Council data

NOT NECESSARILY COMPARABLE


Merging data sets - submodel

AADT = AADT from Department for Transport

or

AADT = k [AADT estimated by me from Cardiff Council]




or


all on roads

all on traffic free cycle paths




or


all on roads

all on traffic free cycle paths

we can’t be certain (in this study) how much of this k arises from

difference in data collection vs how much arises because cyclists

like cycle paths even more than the route choice model allows for


How cyclists choose routes

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

0% 2% 4% 6% 8%

Pe

rce

ive

d k

m

Slope

Perceived effort for 1km

0

1

2

3

4

5

6

7

8

9

0 5 10 15 20 25 30 35

Pe

rce

ive

d k

m

Annual Average Daily Traffic (1000s)

Perceived effort for 1km

…and cost of 90 degree turn = 67 metres

Broach, Dill, Glieb (2012) Where do cyclists ride? A route choice model developed with revealed

preference GPS data. Transportation Research A, 46 (10)


3d model of city region based on OpenStreetMap + OS DTM

sDNA is compatible with GIS – easy to bring in other data


Model calibration

𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 × 𝑠𝑙𝑜𝑝𝑒𝑓𝑎𝑐𝑠 × 𝑡𝑟𝑎𝑓𝑓𝑖𝑐𝑓𝑎𝑐𝑡 + 𝑎𝑛𝑔𝑢𝑙𝑎𝑟𝑖𝑡𝑦 × 67.2

90 × 𝑎

Where

𝑠𝑙𝑜𝑝𝑒𝑓𝑎𝑐 =

1.000 𝑖𝑓 𝑠𝑙𝑜𝑝𝑒 < 2%1.371 𝑖𝑓 2% < 𝑠𝑙𝑜𝑝𝑒 < 4%2.203 𝑖𝑓 4% < 𝑠𝑙𝑜𝑝𝑒 < 6%4.239 𝑖𝑓 𝑠𝑙𝑜𝑝𝑒 > 6%

𝑡𝑟𝑎𝑓𝑓𝑖𝑐𝑓𝑎𝑐 = 0.84 𝑒𝐴𝐴𝐷𝑇1000

𝑎 = 0.2𝑠 = 1

𝑡 = 0.04𝑟𝑎𝑑𝑖𝑢𝑠 = 3𝑘𝑚


Model calibration


90 × 𝑎

Where




𝑎 = 0.2𝑠 = 1


Desire for direct routes far less than in Oregon • No block street structure • We have bends in places other than junctions

Effect of slope is the same

Effect of traffic is similar (0.05 in Oregon)


Model calibration


90 × 𝑎

Where




𝑎 = 0.2𝑠 = 1


Desire for direct routes far less than in Oregon • No block street structure • We have bends in places other than junctions

Effect of slope is the same

Effect of traffic is similar (0.05 in Oregon)

This formula is extensible to accommodate more infrastructure details when

mapped: on-road cycle lanes, designated routes, Level of Service etc


Correlation (R) with real flows 90%


Correlation (R) with real flows

• Not taking account of data source 72%

• Taking account of data source 78%


Uses

• Visual reference for integrated network planning

• Discover why links are/are not used if we expect otherwise

(“Is the model wrong or am I?”)

• HEAT model improvement

• Estimate mean trip distance from flows

• Estimate effect on mode choice (currently hard to predict for HEAT)

• Estimate reduction in risk to existing cyclists (not in HEAT but significant)


sDNA conflict model

• Identifies 75% of incident sites to within 30m (75% sensitivity)

• Identifies 73% of safe sites (73% specificity)

• This validates the model

• Accidents are sparse so some incident-free roads which the model

thinks dangerous may just be lucky

• Accident data low quality which limits model performance in this test

Uses

• Baseline risk model

• Identify priority roads

for improvement

Data: DfT 2005-2012


Note this is about fixed barriers (walls, railways, rivers) not vehicle traffic

This measure relates to work we have done on social cohesion:

Cooper, Fone, Chiaradia (2014) Measuring the impact of spatial network layout on community social cohesion: a cross-sectional study

International Journal of Health Geographics 2014, 13:11


sDNA summary

• Low cost, simple models of pedal cycle “decision to cycle” and flow, integrated with GIS, compatible with open data

– complement by ground surveys and cyclist consultation

• Useful for – integrated network planning

– estimation of trip lengths/alternatives for cost benefit analysis

– identifying hotspots for traffic risk mitigation

– identifying hotspots for building new links

• Future – improve and more extensively test “decision to cycle” model

– identify hotspots for increasing uptake of cycling

– more pedestrian models too (we already have some) based on distance and PERS

• Any questions?

• Pathways to uptake of cycle modelling in policy?

sdna | spatial design network analysis - modelling …...• modelling pedal cycle usage and flows...

Documents