Dykes, J.

Slingsby, A.

Wood, J.

English

City Research Online

Slingsby, A., Wood, J. & Dykes, J. (2011). Visualizing Bicycle Hire Model Distributions. Paper presented at the GeoViz workshop, Mar 2011, Hamburg, Germany.City Research OnlineOriginal citation: Slingsby, A., Wood, J. & Dykes, J. (2011). Visualizing Bicycle Hire Model Distributions. Paper presented at the GeoViz workshop, Mar 2011, Hamburg, Germany.Permanent City Research Online URL: http://openaccess.city.ac.uk/562/ Copyright & reuseCity  University  London has developed City  Research Online  so that  its  users  may access the research outputs of City University London's staff. Copyright © and Moral Rights for this paper are retained by the individual author(s) and/ or other copyright holders. Users may download and/ or print one  copy  of  any  article(s)  in  City  Research  Online  to  facilitate  their  private  study  or  for  non-commercial research. Users may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain. All material in City Research Online is checked for eligibility for copyright before being made available in the live archive. URLs from City Research Online may be freely distributed and linked to from other web pages. Versions of researchThe version in City Research Online may differ from the final published version. Users are advised to check the Permanent City Research Online URL above for the status of the paper.EnquiriesIf you have any enquiries about any aspect of City Research Online, or if you wish to make contact  with the author(s) of this paper, please email the team at publications@city.ac.uk.Visualizing Bicycle Hire Model DistributionsJo Wood, Aidan Slingsby and Jason DykesgiCentre (http://gicentre.org),School of Informatics, City University London0                 1                  2               3kmFig. 1. Location of bicycle hire docking stations in central London. Each station is represented by a rectangle sized by the number ofbicycles that may be docked and coloured by their full/normal/empty status.Index Terms—visualization, bicycle, OD map, origin destination, treemap, simulation.1 INTRODUCTIONIn July 2010, Transport for London launched the Barclays Cycle HireScheme [4] allowing members of the public to hire a bicycle from anyof around 350 docking stations located in central London. Users areencouraged to take short journeys between docking stations by mak-ing hire of a bicycle free (after membership fees) if returned to anydocking station within 30 minutes. Docking stations are located suchthan each is never more than 300m from another station (see Figure 1).A total of about 5000 bicycles are used by the scheme at any momentin time with approximately 8100 docking station places available. Thescheme is currently only available to registered users who sign up viathe web, although it is planned to open the scheme to casual users ona pay-per-use basis in early 2011. It is anticipated that when fully im-plemented, 400 docking stations and 6000 bicycles will be available.In common with similar schemes in other cities (e.g. Dublin, Paris,Melbourne), the challenge for implementers of the scheme is to ensurethat there are both sufficient bicycles and sufficient spaces for dockingavailable at each station. This can be achieved by supplementing thenatural flow of bicycles by users with additional tranportation of bikesfrom full stations to empty ones. This can be regarded as an exampleof fleet management [1]. This paper considers how geovisualization• Jo Wood: jwo@soi.city.ac.uk• Aidan Slingsby: sbbb717@soi.city.ac.uk• Jason Dykes: jad7@soi.city.ac.ukManuscript received 5 November 2010; accepted 10 December 2010.can be used to support fleet management through monitoring the ge-ographic distribution of bikes in the scheme and the integration withsimulation modelling of bike journeys.A number of web sites and services exist to support users of thescheme in locating docking stations and assessing bicycle availability.Those that represent the geography of stations tend to map dockingstations using point-based symbolisation at their geographic location(e.g. [3], [2]). While this allows precise location to be identified,it leaves little graphical space for supplementary information withoutadding extra interaction. We use an alternative approach of mappingdocking stations as a one-level spatial treemap [7] (see Figure 2) inwhich each node is sized equally and contains graphical symbolisationthat reflects station status and history. This information is updated inreal-time as users dock and remove bicycles from each station.2 DOCKING STATION CHARACTERISATIONThe primary purpose of the geovisualization here is to characterise andpredict the usability, and hence the need for intervention, for each sta-tion. Prediction relies on both a spatial and temporal characterizationof trends in station usage. Temporal trends can be assessed by show-ing number of docked bicycles over a previous period (e.g. last 24hours) as a timeline in each station. By maintaining their approximategeographic distribution, spatial trends such as movements from coreto periphery over time can be identified (see Figure 3).Temporal trends can be identified by examining the time lines di-rectly, or by statistical comparison with trends over similar weekdayor weekend periods. Thus stations that are filling or emptying moreFig. 2. Spatial treemap of docking stations showing number of dockedbicycles (height of blue rectangles) and three-level status (dark brownborder: full; mid-brown border: normal; light brown border: empty. Solidpale brown rectangles represent planned but non-operational dockingstations. The river Thames is shown as a dark blue line for added spatialcontext (compare with Figure 1).Fig. 3. Zoomed view of docking stations showing modelled movementthat results in movement from the core (bottom left) to the periphery(top right). Each graph shows number of docked bikes (vertical axis)over time (horizontal axis).quickly than expected can by symbolised. This is particularly usefulwhen exploring the effect of external events such as weather changesor problems with public transport on cyclists’ behaviours.Docking station usability is affected by the probability of a stationhaving no spaces available in which to dock a hired bicycle or thechances of there being no bicycle available to hire from the station.While a station need only have one space or one bicycle to make it us-able for an individual, low numbers of bicycles or spaces increase thechances of the station becoming unusable before it can be restockedor managed. We therefore model usability be measuring small num-bers of bicycles/spaces below a cut-off value K, which we set to 4bicycles/spaces in our modelling. Weighting decreases exponentiallytowards 0 as shown in Equation 1.usability =n∑i=11− b2K2bc+b 2K2c−b c2Kn(1)where b is the number of bicycles at a docking station of capacityc and n is the number of times station status is measured. This givesa scaled value between 0 (never usable) and 1 (always usable) whichwe can symbolise to investigate the need for fleet intervention and itsspatial structure.3 MODELLING BICYCLE JOURNEYSTo gain further insight into predicting usage trends we model bicyclejourneys between stations and assess their effect on docking stationlevels. Using a random walk model and a probabilistic model of bicy-cle docking and removal, we can simulate hire and journey behaviour.By weighting journey direction as a function of location, we can sim-ulate the tidal commuting flow that dominates observed patterns ofusage. We visualize journeys using the origin-destination map or ODMap [8]. This is simply a reordering of the cells of an OD matrix [5][6] into a two-level treemap. Thus each large cell in the treemap rep-resenting a docking station shows all the destination docking stationsof journeys that started in that originating station. (see Figure 4). Thisprovides a scalable means to visualize many origin-destionation pat-terns over time and compare modelled output with observed journeys.Fig. 4. Zoomed view of OD map showing destinations of bikes (bluerectangles) that were removed from selected destinations (large rectan-gles). Using a random walk model of bike movement there is consider-able variation in origin-desintation patterns. For example St John St andRosebury shows very local movement to Harwick St, while Hardwick Stshows destinations over a much wider region.4 CONCLUSIONInitial results of the random-walk models visualized as OD maps sug-gest that the precise geographic placement of stations can produce sig-nificant heterogeneity in journey patterns. This provides a useful toolto assess the effect of introducing new stations into the scheme. Vi-sualisation of station fullness, and hence usability, over time showswhere the greatest pressure points are at which times of day. Thisprovides a useful tool for fleet management and the redistribution ofbicycles across London. We are currently working on developing boththe modelling and visualization tools to allow operational managementof bicycle transport vehicles in real-time.REFERENCES[1] T. Crainic and G. Laporte, editors. Fleet Management and Logistics.Springer, 1998.[2] O. O’Brien. London Boris Bikes Map. http://www.oobrien.com/vis/bikes/,2010.[3] Transport for London. Barclays Cycle Hire Map.https://web.barclayscyclehire.tfl.gov.uk/maps, 2010.[4] Transport for London. Barclays Cycle Hire Scheme.www.tfl.gov.uk/barclayscyclehire, 2010.[5] A. Voorhees. A general theory of traffic movement. Proceedings of theInstitute of Traffic Engineers, pages 46–56, 1955.[6] L. Wilkinson. Permuting a matrix to a simple pattern. Proceedings ofthe Statistical Computing Section of the American Statistical Association,pages 409–412, 1979.[7] J. Wood and J. Dykes. Spatially ordered treemaps. IEEE Transactions onVisualization and Computer Graphics, 14(6):1348–1355, 2008.[8] J. Wood, J. Dykes, and A. Slingsby. Visualization of origins, destinationsand flows with OD maps. Cartographic Journal, 47(2):117–129, 2010.

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