Cycle Hire Scheme Report2

Sheffield City
Council - Bike
Sharing
Scheme
Proposal
Jack Eades
GIS Msc
Andrew Jones & Keith McKoy
By

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CONTENTS
Introduction.......................................................................................................................................3
Selecting Hub Locations.................................................................................................................3
Hub Quantity..............................................................................................................................10
London....................................................................................................................................10
Sheffield .................................................................................................................................11
Trip Origin ....................................................................................................................................4
London Cycle Scheme Methodology..................................................................................4
Criticisms of this Study..........................................................................................................4
Sheffield Data..........................................................................................................................5
Areas of Potential Growth.....................................................................................................7
Trip Destinations .........................................................................................................................8
Route attractors .......................................................................................................................8
Facility Attractors ...................................................................................................................8
Limitations/Criticisms............................................................................................................8
Multi-Criteria Site Selection......................................................................................................9
Trip Origin Wards Assigned Values ...................................................................................9
Creating Suitability Surface for Trip Destinations............................................................9
Quantify Results ........................................................................................................................10
Limitations/Criticisms ..............................................................................................................11
Trip Origin data.....................................................................................................................11
Weighting and Distances.................................................Error! Bookmark not defined.
Existing Public Transport....................................................................................................12
Redistribution of Bikes .................................................................................................................13
Adjusting Network for Bicycle Speeds......................................................................................13
Methodology..............................................................................................................................13
Incorporating Slope ..............................................................................................................13

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Bike Speeds............................................................................................................................13
Analysis.......................................................................................................................................14
Test and Adjust Hub Locations ..........................................................................................14
Temporal Analysis and Usage Data ..................................................................................15
Conclusions.....................................................................................................................................17
References.......................................................................................................................................18
AnnexA ..........................................................................................................................................20

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INTRODUCTION
It has been proposed to introduce a bicycle sharing scheme to Sheffield in order to ease
traffic congestion and for positive health and environmental effects. This document will
give an outline to the proposed overall planning and maintenance of the scheme. In part
this will draw experience from the planning of similar projects,most notably the Barclays
cycle hire scheme in Central London (Commonly called “Boris Bikes”)
The basic premise for bicycle sharing schemes are that a number of bike storage facilities
or “hubs” are placed strategically around the city. Those wishing to use a bike can simply
borrow one, dropping it off at anotherHub when they near their destination.Payment can
either be based on time or distance,in London it is based on time with payment taken
easily via Oyster or bank card (Transport for London, 2014).
Cycling schemes worldwide have met with varying degrees of success.Boris Bikes have
entered the public perception as a success (The Guardian Newspaper, 2011), however are
used roughly half as much1, and to a far greater cost to the tax-payer, than their French
counterpart (Peck, 2013).
The raw numbers however make it appear popular still2; with approximately 600,000
users in London a month on average (Barth, 2012). During the Olympics 47,104 rentals
were made in a single day (Baker, 31 July 2014). They appears to be a growing trend in
cycle use, in part due to an interest in healthier lifestyles, a renewed interest in cycling as
a sport, and as a more efficient method of travel in congested city centres (The Times,
2013). Between 2001 and 2011 Sheffield saw a 2.2% increase in those commuting by
bicycle, the largest increase in the whole country (Office of National Statistics, 2013).
SELECTING HUB LOCATIONS
Bicycle Hubs should be situated in order to make them available to the maximum number
of people willing to use such a scheme, whilst keeping costs to a minimum. Careful
consideration has to be given using both GIS, and the theory behind existing transport
models.
It was assessed that Sheffield has two distinct area types,both requiring different analysis
to determine suitable areas;
 City Centre-Business district, seen as “Trip Destinations”
 Outer Ring. Residential, seen as “Trip Origins”
1 A possible key reason for this is missing from the source:British climate
2 As an indicator of its enduring popularity and use, extensions have been announced to
the London scheme (London Evening Standard, 2013), whilst similar projects have
opened in otherUK cities including: Bath, Northampton, Stirling and Glasgow (Next
Bike , 2014).

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TRIP ORIGIN
Trip origins data was created by analyzing the census data for all Sheffield wards. “Trip
Origins” themselves being the assumed demand for bicycles within a certain ward. This
is based on the assumption that certain types of people already cycle regularly, or would
be willing to if the facilities were put in place.
LONDON CYCLE SCHEME METHODOLOGY
London hubs were chosen by using by using “k-means clustering” on certain elements of
the census data to give seven classifications of people, broken up into postcodes.This
was then combined with data regarding people’s attitudes to cycling, gained from the
London Travel Demand Survey and a survey on Londoners attitude to cycling (Transport
For London, 2010). This can be shown in figure 1. From these results they could then
determine every area of London’s likelihood of using the scheme.
Figure 1.-Population “K-Means” Demographics used for Boris Bike Placement
CRITICISMS OF THIS STUDY
This method gives distinct boundaries to the data, there is no sliding scale. For instance; a
postcode could be 49% people classed as “Urban Living”, 51% “Comfortable maturity”.
This postcode would be given a very low score when in actuality there should be a fairly
high demand for bicycles.

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Assumptions appearto have been made in the data such as “ethnic background may
present a barrier to cycling” etc3..
SHEFFIELD DATA
As such,it was decided the study would be based purely on raw statistics in order not to
lose data. Whilst census data is available, there is no travel demand survey, or attitude to
cycling survey available for Sheffield. With a completely different geography and
demographics to London it should be treated very differently. The data used to estimate
cycle demand was:
 Population Economically Active
 Full Time Students
 Population with no car
 Population that walk to work
 Population that cycle to work
High levels of each indicate a possible high demand, results can be seen below broken
down by ward level.
3 Whilst there may be correlation between ethnicity and propensity to cycle, this does not
indicate a dependency ofeither variable on the other. For example, both race and
propensity to cycle are more likely to be dependent on geographical location. It would be
just as (in)correct to deduce that different ethnicities are choosing where to live in
London on the basis that few local people cycling there; statistical correlation does not
equal causation (Boston University Medical Campus, n.d)

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Diagram 1. Cycling propensity variables by ward (Service, 2011)
Diagram 2. Cycling Propensity by Ward Amalgamated Total Scores
From this it was assessed that the wards to focus on are (in order of likely demand)
0
10
20
30
40
50
60
70
80
90
100
Arbourthorne
Beauchief and Greenhill
Beighton
Birley
Broomhill
Burngreave
Central
Crookes
Darnall
Dore and Totley
East Ecclesfield
Ecclesall
Firth Park
Fulwood
Gleadless Valley
Graves Park
Hillsborough
Manor Castle
Mosborough
Nether Edge
Richmond
Shiregreen and Brightside
Southey
Stannington
Stocksbridge and Upper Don
Walkley
West Ecclesfield
Woodhouse
Sheffield Cycling Demand Variables
No cars, percent Level 4 Quals and above percent
Economically Active-percent Full time Student Percent
Bike to Walk Walk to Work Per
0
50
100
150
200
250
300
350
400
450

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1. Central
2. Broomhill
3. Nether Edge
4. Crookes
5. Walkley
6. Fullwood
7. Ecclesall
8. Manor Castle
AREAS OF POTENTIAL GROWTH
Areas of potential growth can be also be highlighted. Firstly, as was expected there was a
strong correlation between the percentage of car ownership and the percentage of people
that walk/cycle to work. Areas with low car ownership generally have high levels of
people that walk/cycle. The areas we were interested in however was wards with low
levels of car ownership but also low levels of walkers/cyclists. As employment is fairly
consistent across Sheffield it can only be assumed that in these wards there is high usage
of public transport.With a supply of cycling facilities these people could be persuaded to
change their modal choice of transport.
Secondly, walking is generally a more popular choice than cycling; in areas where this
disparity is largest it could possibly be lessened with more cycling facilities.
Diagram 3. Method of travel to work.Wards of potential growth highlighted Low level of
cars and low level of walking/cycling and Disparity between walking and cycling
Further investigation of these results revealed that those highlighted in green (low level
of car ownership/low level of commuting by bike/walk) were situated a few miles from
the city centre and on tram routes.Despite being roughly the same distance from the town
centre as Ecclesall (which does not have a tramline but does have a cycle lane), the
0
10
20
30
40
50
60
70
80
90
100
Arbourthorne
Beauchiefand…
Beighton
Birley
Broomhill
Burngreave
Central
Crookes
Darnall
DoreandTotley
EastEcclesfield
Ecclesall
FirthPark
Fulwood
GleadlessValley
GravesPark
Hillsborough
ManorCastle
Mosborough
NetherEdge
Richmond
Shiregreenand…
Southey
Stannington
Stocksbridgeand…
Walkley
WestEcclesfield
Woodhouse
Work Method of Travel
No cars, percent Bike to Walk Walk to Work Per

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walking/cycling to lack of car ratio is much lower. This would appear to signify an area
of potential growth among the non-car owners.
In contrast those wards highlighted in red (High levels of walking compared to cycling)
are all situated in or very close to the city centre. It has been assessed that the close
proximity leads to a bike being deemed too much hassle over such short distances. A well
run scheme could offer excellent potential for cycle growth.
TRIP DESTINATIONS
Trip destinations can be created by simply using data from “trip attractors”.These are the
features that create the need for any type of trip and are a commonly used aid in
predicting cycling traffic (Changshan,2010).
ROUTE ATTRACTORS
 Cycle Lanes
 Arterial Routes
FACILITY ATTRACTORS
 University
 Train Stations
 Tram Stops
 Workplaces
 Halls of Residence
 Gyms
 Supermarkets
 Bus stops
 Parks
It has been assessed that the two universities and central rail station are the busiest.Trams
and bus stops were chosen to try and integrate the existing transport networks.
LIMITATIONS/CRITICISMS
Further secondary analysis could have been performed to improve the validity of our
results.This was not done due to time restraints and lack of data.
BLOS Index This is a method of grading routes by their perceived comfort, and therefore
propensity to cycle. It takes into account factors such as: crime, pavement widths, vehicle
speeds,road surface type, traffic volume etc. (B.Landis.et.all, 1997). The importance of
this is highlighted by a similar study done for Milwaukee USA (Changshan,2010) giving
negative values - “trip reductors” (crime, traffic) a higher weighting in the final study
than “trip attractors”.
Cycling Propensity In all other studies this report is based on: London (Transport For
London, 2010), Milwaukee (Changshan, 2010) and Wuhan,China (Zang, 2011), an

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element of propensity has always been incorporated into the planning, usually through
the form of a cycling-attitude related survey.
MULTI-CRITERIA SITE SELECTION
The data created for both “Trip Origins” and “Trip Destinations” could then be joined
togetherto create a continuous rastersurface, giving values on a scale of most to least
suitable areas for the docking stations (Lisec, 2009).
TRIP ORIGIN WARDS ASSIGNED VALUES
Wards were applied generic values on a scale from 1(least suitable) to 100 (most suitable)
based on the results shown above (Graph 2).
 Central-100
 Broomhill-80
 Nether Edge-75
 Crookes-70
 Walkley-70
 Fullwood-60
 Ecclesall-50
 Manor Castle-50
 Firth Park-45
 Manor Castle-40
 Southey - 35
 Arbouthorne -35
CREATINGSUITABILITY SURFACE FOR TRIP DESTINATIONS
Euclidean distance was run from all trip destination features,with a distance of 200m
decided on. The reason for this distance being so large, was that due to the number of
features, mainly bus and tram stops,a large distance would lead to overlapping service
areas. This could then lead to potential values higher than 200 between features. By
placing hubs at these locations, multiple “trip attractors” could be covered. Values were
then standardised to give scores between 0-100 using the formula:
=(“LayerName”/”HighestValue”)*100
This output was inverted to give high values closest to the feature, low values furthest
away:
=(100-((“LayerName”/100)*1000))

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Figure 9.-Bus stop surface. Normalised and scores inverted.
Finally all eleven “trip destination” surfaces,and all twelve “trip origin” ward surfaces
could be added togetherusing the raster calculator.
QUANTIFY RESULTS
This raster surface could not be simply taken at face value, for instance many very high
values were in the centre of roads or other places unsuitable for hubs. Instead they were
manually digitised using the surface as a guide, and 50k mapping. Areas with very high
values were given large hubs to cover the higher demand.
HUB QUANTITY
The London scheme was looked at to give a rough indicator of how many docking
stations would be needed.
LONDON
 Total area covered - 100 Km²
 Bikes - 10,000
 Docking stations -700
Sheffield has a total area of 368km² (Sheffield City Council, 2014). However, it has been
assessed that only the area within 2 km of the train station need to be covered. Other
factors limiting the demand, and therefore number of docking stations needed (by
comparison) are:
 Smaller Population

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 Far less “Trip attractors”
 Undulating terrain
 Worse climate, less suitable for cycling.
 Less of target demographic
SHEFFIELD
Therefore it has been estimated that this scheme will incorporate:
 Total area covered – 19.7km²
 Bikes – 1,000
 Docking Stations - 50
Final results can be seen below and at Annex1.
Diagram 5. Final Hub Locations
LIMITATIONS/CRITICISMS
TRIP ORIGIN DATA
As previously mentioned, crime data should have been included. Also,the Census
Cycling to Work report (2011) offers an excellent summary on UK cycling habits.Age

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and profession data should have also been included, a breakdown from the report, can be
seen below, and would have proved valuable to the study.
Diagram 4.Percentage of workers cycling or walking by industry (Office of National
Statistics, 2013)
Diagram 5. Bicycle commuters by age (Office of National Statistics, 2013)
EXISTING PUBLIC TRANSPORT
Whereas some studies of this nature look to incorporate existing public transport
networks (Zang, 2011), it could be argued that placing hubs next to bus stops will cause
potential users to catch a conveniently-timed bus instead.

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REDISTRIBUTION OF BIKES
A management systemhad to be planned in order to redistribute bicycles from the Hubs.
It would appear likely that,traffic dependent,over time some hubs would become full
whilst others empty. In order to alleviate this, a van could redistribute the bikes, a task
can be made easier by using network analysis and the “route solver” tool. After the start
and end point (cycle depot)is inputted,the quickest, most efficient route can be
calculated between all hubs.Factors such as the capacity of the van, timings to be kept to,
even which side of the road to pull up on to can also be attributed for analysis.
Figure 6. Example of most effective route between hubs with directions
ADJUSTING NETWORK FOR BICYCLE SPEEDS
METHODOLOGY
A network for Sheffield attributed for vehicle speeds and restrictions has been supplied,
however with some small adjustments this can be changed to reflect bicycle speeds.
INCORPORATINGSLOPE
The undulating terrain of Sheffield will significantly affect bike speeds compared to
motor vehicles. Slope has already been attributed into each road feature, underthe
heading “From-To” and “To-From” with either a minus or plus score.
BIKE SPEEDS
Doubling the cost attributes as shown below give a rough indicator of bike times
compared to cars in a busy city. More complicated restrictions can be added such as
larger resistance in the network at certain times of day to simulate rush hour etc (ESRI,
n.d).

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Fig.8-A simple method of editing the cost attributes to give more realistic bike speeds
This network can then be uploaded to an interactive web portal. This can then be used not
only for showing hub locations but also empty hubs and route directions. The “new
route” tool could be used to calculate the quickest route between hubs.
POST ANALYSIS
TEST AND ADJUST HUB LOCATIONS
Conducting analysis on the network could have proved useful for validating hub spacing.
The “service area” tool could have been run from all hubs with the network altered to
give walking speeds.Rings in 5 minute intervals for instance would then indicate any
gaps too large, or small between hubs.Alternatively it could indicate any important areas
lacking coverage.
Anothertool purpose built for this is the “location-allocation” functionality (ESRI, n.d).
This could be used to calculate any redundant facilities by examining the journey time
between facilities (Hub) and demand points (Trip attractors) The ideal time measurement
between facilities has to be entered, for the Wuhan study this was given as 5 minutes
(Zang, 2011).

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Fig.9 An example of how “location-allocation” finds 9 redundant fire stations out of a
set of 16. 3 Minute response times are needed (ESRI, n.d).
TEMPORAL ANALYSIS AND USAGE DATA
When a trip is made on a Boris Bike, the number of the start and end point docking
stations,and the time are recorded. These are then made available on the internet for
further analysis,particularly with regard to creating transport phone apps.
Figure 11.-Boris Bike Trip Data (Greater London Authority, 2014)
This data can then be used for keeping track of full and empty hubs.Secondly temporal
analysis can be performed, investigating patterns and usage at various times of day. An
example of this created for London can be seen here: London Hub Spaces. (J.Cheshire,
2014)

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In addition the data can be fed into the network, although the exact routes taken are not
know, the start and end points are. Calculating the shortest route between them can
indicate the post probable route used,from this the frequency of trips in the area can be
visualized.
Fig.11-Boris bike usage over a working day, troughs indicate rush hour times
Fig.12-Estimated routes and volume of Boris Bikes on a single day (J.Cheshire, 2014)

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CONCLUSIONS
Many spatial analysis methods could have been used for identifying hub locations, the
one used in the study is only one and is probably far from perfect. The network tool and
extension play a valuable part in this analysis.
Unlike automobile traffic which has to be modelled at great length, cycle hire schemes
capture trip data perfectly through the credit/oystercard system. Not only that,but it is
available as open-source on the internet (Greater London Authority, 2014). This offers
excellent scope for further analysis, and when combined with the relatively flexible
nature of a cycle hire scheme (compared to a rail/tube network for instance) the
opportunity to improving existing facilities is greater than almost any other form of
transport.Apart from a planning perspective, the network can also be used to plan
journeys and improve the systemupon completion of the hire scheme.

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REFERENCES
B.Landis.et.all, 1997. Real Time Human Perceptions: Towards a Bicycle Level of
Service. Journal of Transportation Research Board, Volume 1578, pp. 119-126.
Baker, L., 31 July 2014. Cycling Weekly. [Online]
Available at: http://www.cyclingweekly.co.uk/news/latest-news/boris-bikes-set-break-
record-131903
[Accessed 27 December 2014].
Barth, S., 2012. Boris Bike Hires Hit New Record. [Online]
Available at: http://road.cc/content/news/62991-boris-bike-hires-hit-new-record-
londoners-and-tourists-use-them-beat-olympic
Boston University Medical Campus, n.d. Hypothesis Testing. [Online]
Available at: http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/BS704_HypothesisTest-
Means-Proportions/BS704_HypothesisTest-Means-Proportions_print.html
Changshan,G. R. &. W., 2010. Bicycle facility planning using GIS and multi-criteria
decision analysis.Applied Geography, Volume 30, pp. 282-293.
ESRI, n.d. Arc Resources-Types of Network Analysis Layers. [Online]
Available at:
http://resources.arcgis.com/en/help/main/10.1/index.html#//004700000032000000
Greater London Authority, 2014. London Datastore. [Online]
Available at: http://data.london.gov.uk/dataset/number-bicycle-hires
J.Cheshire, O. &., 2014. Mapping London. [Online]
Available at: http://mappinglondon.co.uk/2014/cycle-hire-journeys-the-central-london-
grid/
Lisec, S. D. &. A., 2009. Multi-attribute Decision Analysis in GIS: Weighted
LinearCombination and Ordered Weighted Averaging. Informatica, Volume 33, pp. 459-
474.
London Evening Standard, 2013. Boris Bike Scheme Makes Tracks South and West.
[Online]
Available at: http://www.standard.co.uk/news/london/boris-bike-scheme-makes-tracks-
south-and-west-as-it-grows-50-9002311.html
Next Bike , 2014. Next Bike Locations.[Online]
Available at: http://www.nextbike.co.uk/en/
[Accessed 27 Dec 2014].

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Office of National Statistics, 2013. Method of Travel to Work. [Online]
Available at: http://www.ons.gov.uk/ons/rel/census/2011-census-analysis/method-of-
travel-to-work-in-england-and-wales/art-method-of-travel-to-work.html#tab-Commuting-
by-public-transport
Peck, C., 2013. LondonsCycle Hire Least Used and Most Expensive in Europe. [Online]
Available at: http://www.ctc.org.uk/news/londons-cycle-hire-least-used-and-most-
expensive-in-europe
Service, U. D., 2011. Infuse - 2011 Census Data. [Online]
Available at: http://infuse2011.mimas.ac.uk/InFuseWiz.aspx?cookie=openaccess
Sheffield City Council, 2014. Sheffield City Council. [Online]
Available at: https://www.sheffield.gov.uk/your-city-council/sheffield-
profile/introduction.html
The Guardian Newspaper, 2011. Guardian Poll - Have Boris Bikes been a success?.
[Online]
Available at: http://www.theguardian.com/commentisfree/poll/2011/jul/29/boris-bikes-
success
The Times, 2013. Cities Fit For Cycle. [Online]
Available at: http://www.thetimes.co.uk/tto/public/cyclesafety/article3706006.ece
[Accessed 27 Decemeber 2014].
Transport For London, 2010. Analysisof Cycling Potential, London: Transport For
London.
Transport for London, 2014. TFL-Transport For London. [Online]
Available at: https://www.tfl.gov.uk/modes/cycling/barclays-cycle-hire/what-you-pay
Zang, Y., 2011. Reviewing Performance of Bicycle Sharing System in Wuhan, China,
Enshcede: University of Twente.

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