Sheffield City Council - Bike Sharing Scheme Proposal

By

Jack EadesGIS Msc Andrew Jones & Keith McKoy

CONTENTS

Introduction..........................................................................................................................1

Selecting Hub Locations......................................................................................................1

Hub Quantity...................................................................................................................2

London.........................................................................................................................2

Sheffield.......................................................................................................................2

Trip Origin.......................................................................................................................2

London Cycle Scheme Methodology..........................................................................2

Criticisms of this Study...............................................................................................3

Sheffield Data..............................................................................................................4

Areas of Potential Growth...........................................................................................5

Trip Destinations.............................................................................................................6

Route attractors............................................................................................................6

Facility Attractors........................................................................................................6

Limitations/Criticisms.................................................................................................7

Multi-Criteria Site Selection............................................................................................7

Trip Origin Wards Assigned Values............................................................................7

Creating Suitability Surface for Trip Destinations......................................................8

Quantify Results..............................................................................................................8

Limitations/Criticisms.....................................................................................................9

Trip Origin data...........................................................................................................9

Weighting and Distances...........................................................................................10

Existing Public Transport..........................................................................................11

Redistribution of Bikes......................................................................................................11

Adjusting Network for Bicycle Speeds.............................................................................11

Methodology..................................................................................................................11

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Incorporating Slope...................................................................................................12

Bike Speeds...............................................................................................................12

Analysis.........................................................................................................................12

Test and Adjust Hub Locations.................................................................................12

Temporal Analysis and Usage Data..........................................................................13

Conclusions........................................................................................................................14

References..........................................................................................................................15

Annex A.............................................................................................................................17

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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 another Hub 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 other UK 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”.

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This postcode would be given a very low score when in actuality there should be a fairly high demand for bicycles.

Assumptions appear to 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 of either 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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ArbourthorneBeauchief and GreenhillBeighton

Birley

Broomhill

Burngreave

Central

Crookes

Darnall

Dore and Totley

East Ecclesfield

EcclesallFirth Park

FulwoodGleadless ValleyGraves ParkHillsborough

Manor Castle

Mosborough

Nether Edge

Richmond

Shiregreen and Brightside

Southey

Stannington

Stocksbridge and Upper Don

WalkleyWest Ecclesfield

Woodhouse

0

50

100

Sheffield Cycling Demand Variables

No cars, percent Level 4 Quals and above percent Economically Active-percentFull time Student Percent Bike to Walk Walk to Work Per

Diagram 1. Cycling propensity variables by ward (Service, 2011)

Arbourt

horne

Beighto

n

Broomhil

l

Centra

l

Darnall

East Eccl

esfiel

d

Firth Park

Gleadle

ss Vall

ey

Hillsbo

rough

Mosbo

rough

Richmon

d

Southe

y

Stocks

bridg

e and

Upp

er Don

West Eccl

esfiel

d0

100

200

300

400

Chart Title

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)

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1. Central2. Broomhill3. Nether Edge4. Crookes5. Walkley6. Fullwood7. Ecclesall8. 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.

Arbourt

horne

Beighto

n

Broomhil

l

Centra

l

Darnall

East Eccl

esfiel

d

Firth Park

Gleadle

ss Vall

ey

Hillsbo

rough

Mosbo

rough

Richmon

d

Southe

y

Stocks

bridg

e and

Upp

er Don

West Eccl

esfiel

d0

20406080

100

Work Method of Travel

No cars, percent Bike to Walk Walk to Work Per

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

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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 together to create a continuous raster surface, 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

CREATING SUITABILITY 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 together using 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 Annex 1.

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 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.

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Diagram 4.Percentage of workers cycling or walking by industry (Office of NationalStatistics, 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 system had 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.

INCORPORATING SLOPE

The undulating terrain of Sheffield will significantly affect bike speeds compared to motor vehicles. Slope has already been attributed into each road feature, under the 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.

Another tool 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/oyster card 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 system upon 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[Accessed 29 December 2014].

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[Accessed 29 December 2014].

Greater London Authority, 2014. London Datastore. [Online] Available at: http://data.london.gov.uk/dataset/number-bicycle-hires[Accessed 29 December 2014].

J.Cheshire, O. &., 2014. Mapping London. [Online] Available at: http://mappinglondon.co.uk/2014/cycle-hire-journeys-the-central-london-grid/[Accessed 29 December 2014].

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[Accessed 27 December 2014].

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[Accessed 27 December 2014].

Peck, C., 2013. Londons Cycle 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[Accessed 27 December 2014].

Sheffield City Council, 2014. Sheffield City Council. [Online] Available at: https://www.sheffield.gov.uk/your-city-council/sheffield-profile/introduction.html[Accessed 27 December 2014].

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. Analysis of 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[Accessed 27 December 2014].

Zang, Y., 2011. Reviewing Performance of Bicycle Sharing System in Wuhan, China, Enshcede: University of Twente.

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ANNEX A

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