Probabilistic Modelling of Station Locations in Bike-Sharing … · Bike-sharing systems: rapidly...

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

UED/INF/LFCS/dreijsbe2016 1/43


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.




1 Motivation, Context & Data

2 Characterisation of Existing BSSs

3 Simulation Model for Station Locations

4 Comparison

5 Conclusions







4 Comparison

5 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


www.bikesharingmap.com


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.



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.



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.



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.


http://citybik.es






4 Comparison

5 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.



BSS Examples

Example 1: London. Large, dense.



BSS Examples

Example 2: Brussels. Large, sparse, circular.



BSS Examples

Example 3: Nice. Inhomogeneous, strip-like.



BSS Examples

Example 4: New York. Large, inhomogeneous.







4 Comparison

5 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.



Station Locations: New York



New York - Regular Grid



New York - Regular Grid, Little Noise



New York - Regular Grid, More Noise



New York - Regular Grid, Much Noise



New York - Regular Grid, Very Much Noise



New York - Poisson



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



New York - Ginibre



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.



New York - Ratings







4 Comparison

5 Conclusions



New York - ComparisonReal system Regular (Noisy) Poisson

Regular Ginibre Rating-Weighted



Brussels - ComparisonReal system Regular (Noisy) Poisson




London - ComparisonReal system Regular (Noisy) Poisson




Vienna - ComparisonReal system Regular (Noisy) Poisson




Vienna - Ratings



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



EdinburghNo Real system Regular (Noisy) Poisson








4 Comparison

5 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.



Thank you for your attention.

This research has been conducted as part of the EU projectQUANTICOL. See www.quanticol.eu for more information.


www.quanticol.eu

Probabilistic Modelling of Station Locations in Bike-Sharing … · Bike-sharing systems: rapidly...

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