Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
Daniel ReijsbergenUniversity of Edinburgh
DataMod, Vienna, 8 July 2016
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Preamble
Global emergence of Bike-SharingSystems (BSSs) has led to awealth of open data.
Data allows for parameterisationof models of bicycle movementand availability.
This talk: model for the locationsof stations in a BSS.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
1 Motivation, Context & Data
2 Characterisation of Existing BSSs
3 Simulation Model for Station Locations
4 Comparison
5 Conclusions
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
1 Motivation, Context & Data
2 Characterisation of Existing BSSs
3 Simulation Model for Station Locations
4 Comparison
5 Conclusions
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Bike-Sharing Systems
Bike-sharing systems: rapidly growing phenomenon.
There are currently (July 2016) 1,070 operational bike-sharingsystems worldwide — over 100 new systems each year since 2010.1
Biggest system thatshares usage data:
Paris, with 1,218stations.
Vienna has 121 sta-tions.
1 www.bikesharingmap.com
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Optimisation Approaches
Many systems publish station data online to allow users to plantrips → great for researchers.
Spatial analysis of BSSs tends to fall into one of two categories:2
1 Analysis of existing BSSs: e.g., to explore spatio-temporalusage patterns.
2 Location-allocation modelling to investigate ‘optimal’ stationlocations in a new BSS.
A typical optimality criterion involves the coverage of demandlocations.
2 Ying Zhang, Mark Zuidgeest, Mark Brussel, Richard Sliuzas & Martin vanMaarseveen (2013): Spatial location-allocation modeling of bike sharingsystems: a literature search.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Identifying demand locations (I).
There are several ways in which to determine demand locations.
First: datasets created by plannersor scientists.3
Demographic information.
Manually identified points ofinterests; e.g, “majoremployment concentrations,shopping destinations, universitycampuses, recreational areas,and tourist attractions”.
3 Sakari Jappinen, Tuuli Toivonen & Maria Salonen (2013): Modelling thepotential effect of shared bicycles on public transport travel times in GreaterHelsinki: An open data approach.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Identifying demand locations (II).
Second: user-generated datasets.
Foursquare.
Yelp.
Google Places.
Taxi datasets.4
OpenStreetMap.
...
4 Junming Liu, Qiao Li, Meng Qu, Weiwei Chen, Jingyuan Yang, Xiong Hui,Hao Zhong & Yanjie Fu: Station Site Optimization in Bike Sharing Systems.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
A Simulation Methodology
We propose a simulation methodology for BSS station locations.
We only use non-proprietary, easily accessible code and data.
Code is available upon request, will be released at a laterstage.
Easy comparison with over 350 existing BSSs available via theAPI available on http://citybik.es.
Can also easily be executed for cities without an existing BSS.
We discuss several baseline approaches, followed by an approachthat uses easily accessible geographic data from OpenStreetMap.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
1 Motivation, Context & Data
2 Characterisation of Existing BSSs
3 Simulation Model for Station Locations
4 Comparison
5 Conclusions
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
BSS Characteristics
BSS station configurations can be characterised in many ways,e.g.:4
Large vs. small.
Dense vs. sparse.
Homogeneous vs inhomogeneous.
Circular vs. strip-like.
4 Oliver O’Brien, James Cheshire & Michael Batty (2014): Mining bicyclesharing data for generating insights into sustainable transport systems.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
BSS Examples
Example 1: London. Large, dense.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
BSS Examples
Example 2: Brussels. Large, sparse, circular.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
BSS Examples
Example 3: Nice. Inhomogeneous, strip-like.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
BSS Examples
Example 4: New York. Large, inhomogeneous.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
1 Motivation, Context & Data
2 Characterisation of Existing BSSs
3 Simulation Model for Station Locations
4 Comparison
5 Conclusions
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Target Area Identification
Two initial steps done before BSS generation.
First: identifying the ‘target’ areas of a city.
Second: remove areas obviously unsuited for station placement:e.g., water, parks, farmland.
Done using pre-generated OpenStreetMap map tiles.
If BSS exists: we refine the target area further by drawing a circlewith a fixed radius around each station, and determine the convexhull.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Station Locations: New York
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Station Locations: New York
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Station Locations: New York
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Station Locations: New York
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Regular Grid
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Regular Grid, Little Noise
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Regular Grid, More Noise
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Regular Grid, Much Noise
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Regular Grid, Very Much Noise
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Poisson
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Ginibre point process
Let (~z1, . . . , ~zN) =((x1, y1), (x2, y2), . . . , (xN , yN))be the set of station locations.Let | · | denote the Euclideannorm. The (standard) Ginibrepoint process can be definediteratively, using the followingprobability density at iterationk :
f (~zk |~zk−1 . . . ~z1) =1
k!
1
πe−|~zk |
2︸ ︷︷ ︸bivariate Gaussian term
k−1∏j=1
|~zk − ~zj |2︸ ︷︷ ︸repulsion
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Ginibre
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Rating-based methods
Other methodology: assign to each pixel (x , y) a rating ρxy . Thensample according to the Poisson point process, but reject thesample with probability
ρxymaxu,v ρuv
.
Scoring approach: obtain list of all shops/amenities in the widertarget area from the OpenStreetMap database. Then we increasethe rating ρxy
by 5 if the pixel (x , y) is within 50 meters of the shop/amenity,by 2 if the pixel (x , y) is within 200 meters of theshop/amenity,by 1 if the pixel (x , y) is within 1 kilometer of theshop/amenity,
Also includes a repulsion term, similar to the Ginibre point process.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Ratings
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - Ratings
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
1 Motivation, Context & Data
2 Characterisation of Existing BSSs
3 Simulation Model for Station Locations
4 Comparison
5 Conclusions
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
New York - ComparisonReal system Regular (Noisy) Poisson
Regular Ginibre Rating-Weighted
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Brussels - ComparisonReal system Regular (Noisy) Poisson
Regular Ginibre Rating-Weighted
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
London - ComparisonReal system Regular (Noisy) Poisson
Regular Ginibre Rating-Weighted
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Vienna - ComparisonReal system Regular (Noisy) Poisson
Regular Ginibre Rating-Weighted
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Vienna - Ratings
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Distances Between Real BSS and Simulated
City Reg. Grid Poisson Ginibre Rated
Barcelona 17.55 18.98 ± .84 24.51 ± .08 12.29 ± .47
Brussels 5.88 7.08 ± .33 6.92 ± .06 5.38 ± .24
Dublin 9.51 11.07 ± 1.68 15.69 ± .27 9.06 ± .43
Glasgow 4.54 5.71 ± .75 4.43 ± .16 3.23 ± .24
London 14.39 15.02 ± .39 24.57 ± .03 9.59 ± .25
Melbourne 3.69 4.57 ± .33 4.82 ± .08 3.67 ± .29
New York 14.39 14.83 ± .54 20.00 ± .04 5.44 ± .15
Nice 11.75 13.04 ± .49 18.63 ± .07 6.55 ± .29
Paris 19.03 19.42 ± .54 25.69 ± .04 13.05 ± .33
Pisa 3.03 3.59 ± .45 4.18 ± .20 2.93 ± .38
Tel Aviv 7.71 8.26 ± .42 10.67 ± .06 5.43 ± .22
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
EdinburghNo Real system Regular (Noisy) Poisson
Regular Ginibre Rating-Weighted
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Probabilistic Modelling of Station Locations inBike-Sharing Systems
1 Motivation, Context & Data
2 Characterisation of Existing BSSs
3 Simulation Model for Station Locations
4 Comparison
5 Conclusions
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Conclusions
Modelling of BSS station locations can help planners evaluate theirsystem, and researchers evaluate their analysis methods.
Data-driven methods are the most realistic.
Generally and easily applicable to cities with or without existingBSSs.
Future work: comparison with deterministic optimisation methods.
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Motivation, Context & Data Characterisation of Existing BSSs Simulation Model for Station Locations Comparison Conclusions
Thank you for your attention.
This research has been conducted as part of the EU projectQUANTICOL. See www.quanticol.eu for more information.
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