Incentivizing Users for Balancing Bike Sharing SystemsAdish Singla

ETH Zurichadish.singla@inf.ethz.ch

Marco SantoniElectricFeel Mobility Systems

marco@electricfeel.com

Gabor BartokETH Zurich

bartok@inf.ethz.ch

Pratik MukerjiElectricFeel Mobility Systems

mukerji@electricfeel.com

Moritz MeenenElectricFeel Mobility Systems

meenen@electricfeel.com

Andreas KrauseETH Zurich

krausea@ethz.ch

Abstract

Bike sharing systems have been recently adopted by agrowing number of cities as a new means of transporta-tion offering citizens a flexible, fast and green alterna-tive for mobility. Users can pick up or drop off the bi-cycles at a station of their choice without prior notice ortime planning. This increased flexibility comes with thechallenge of unpredictable and fluctuating demand aswell as irregular flow patterns of the bikes. As a result,these systems can incur imbalance problems such as theunavailability of bikes or parking docks at stations. Inthis light, operators deploy fleets of vehicles which re-distribute the bikes in order to guarantee a desirable ser-vice level. Can we engage the users themselves to solvethe imbalance problem in bike sharing systems?In this paper, we address this question and present acrowdsourcing mechanism that incentivizes the users inthe bike repositioning process by providing them withalternate choices to pick or return bikes in exchangefor monetary incentives. We design the complete archi-tecture of the incentives system which employs opti-mal pricing policies using the approach of regret mini-mization in online learning. We investigate the incentivecompatibility of our mechanism and extensively evalu-ate it through simulations based on data collected viaa survey study. Finally, we deployed the proposed sys-tem through a smartphone app among users of a largescale bike sharing system operated by a public transportcompany, and we provide results from this experimen-tal deployment. To our knowledge, this is the first dy-namic incentives system for bikes re-distribution everdeployed in a real-world bike sharing system.

IntroductionA Bike Sharing System (henceforth BSS) is a new con-cept of public transportation offering citizens a flexible andgreen alternative as well as complementing the slow andcrowded transportation in urban areas. This mobility trendhad an exponential growth over the last years, and, as of June2014, over 700 cities actively operate automated bike shar-ing systems deploying an estimate of 800,000 bikes world-wide (Meddin and DeMaio 2014). BSSs increase the user’scommuting flexibility by allowing her to pick up or drop off

Copyright © 2015, Association for the Advancement of ArtificialIntelligence (www.aaai.org). All rights reserved.

a bike at any station and let her decide the duration of thetrip without any prior planning or reservation. Indeed, thisflexibility is the key factor for the success of BSSs.

Balancing problem. This flexibility also poses a num-ber of new challenges for the BSS operators. The demand isoften unpredictable, asymmetric and fluctuating throughoutthe day. Other factors such as altitude differences, weatherconditions or events in the city can cause irregular or asym-metric rental demands and flow of bikes. As the number ofavailable bikes and parking spots at any bike station in thesystem is limited, satisfying the forthcoming demand withsuch limited resources is a major challenge and recurrentproblem for BSS operators. A BSS user would experiencean unsatisfactory quality of service when attempting to rentor drop off a bike at an empty or full station respectively.Meeting the demand for bikes and free docks requires anextensive logistic work and poses major operational coststo the operators. Thus, fleets of vehicles (or trucks) are de-ployed to re-distribute bikes among stations to balance thesystem out (Buttner, Mlasowsky, and Birkholz 2011).

Incentivizing users. Apart from the large operationalcosts, deploying trucks for the re-distribution goes againstthe green concept of BSS. Can we engage the users ofsuch systems and provide them incentives to contribute re-balancing the system? Termed as crowdphysics by Sadilek,Krumm, and Horvitz (2013), this class of crowdsourcingsystems requires users to sequence or synchronize physicalactions in time and space, and can lead to building self-sustainable systems. The goal of building such a systempresents several challenges including: i) the system’s largescale in terms of user base as well as number of stations, ii)the fluctuating demand over time that needs to be taken intoaccount while determining problematic stations, iii) the un-known personal costs of the users and their strategic behav-ior potentially aiming at maximizing their profit, iv) the bud-get constraints of how much can be spent on incentives andv) a user-friendly interface to make such a system appeal-ing (e.g., through a smartphone app). Building such a self-sustainable and crowd-powered bike sharing system is thekey idea we explore in this paper. While we focus on BSSs,similar problems arise in other domains (e.g., car sharing orrerouting users on over-booked flights) and our methodol-ogy is applicable to these systems as well.

- User Interface- Displays o!ers, receives response

C1 Rental Stations

- Distribution of costs, walking distance- Types of trips and response to incentives

- Capacity and current status- Events of user picking or dropping a bike

C5 Smartphone App Deployed

C3 User Model

C4 Incentives Deployment Schema- Choosing target stations to be o!ered- Learning user behavior and dynamic pricing mechanism- APIs to communicate with App and BSS

C2 BSS Infrastructure- Tracking status of rental stations- User accounts and payments- Tra"c forecaster- Noti#cations about user picking or dropping a bike

Figure 1: Overview of the system, our contributions are shown in the dotted box Figure 2: Spatial dist. of accepted offers

Overview of our systemIn this paper, we design the complete architecture of an op-erating incentives system for engaging users in the bike-repositioning effort (see Figure 1). In collaboration with acompany operating large scale bike sharing systems, we de-ployed our system on a real-world BSS in a city of Europe1.In Figure 1, the components C1 Rental Stations and C2 BSSInfrastructure correspond to the rental stations, the hardwareand software infrastructure of the public bike sharing sys-tem. These components involve tracking all the user activ-ities such as picking or dropping the bikes, managing useraccounts and the payment system.

The main components that we designed and built includeC3 User Model, C4 Incentives Deployment Schema (IDS)and C5 Smartphone App. The IDS is the central componentin the overall design that handles the user’s requests throughC5 Smartphone App. The request includes input such as theuser’s target station and type (i.e., bike pick up or drop off).The IDS communicates with the BSS Infrastructure to eval-uate the current and predicted status of the stations, and thendecides whether to offer incentives to the user by request-ing a change in her pickup or drop-off location. In order tomaximize the efficiency under given budget constraints, wedesign dynamic pricing mechanisms using the approach ofregret minimization in online learning that can learn overtime about the optimal pricing policies. We consider theusers as strategic agents who may untruthfully report infor-mation about their personal cost and location to maximizetheir profit. We model the full rental process of the user andher reaction to incentives in the component C3 User Model.In summary, our main contributions are as follows:• We design the complete architecture of an incentive sys-

tem with the goal of enabling self-sustainable, crowd-powered and greener bike sharing systems.

• We build an incentive deployment schema that interactswith the users and BSS infrastructure, decides about of-fering incentives, and learns from past interactions.

• We validate our assumptions by surveying BSS users, anddemonstrate the effectiveness of our approach via a de-tailed BSS simulator. We report on our experience de-ploying our system via a smartphone app integrated witha real-world BSS.

Related WorkTruck-based repositioning in BSS. The problem of asym-metric flow, imbalance and repositing of the vehicles has

1Bike sharing operator MVGmeinRad in Mainz, Germany.

been studied in generic vehicle sharing systerms that couldinvolve cars or bikes (Cepolina and Farina 2012; Kek et al.2009; Clemente et al. 2013; Nair and Miller-Hooks 2011;Correia and Antunes 2012). Regarding BSS’s balancingproblem, a substantial literature (Rainer-Harbach et al. 2013;Pfrommer et al. 2014; Raviv and Kolka 2013; Chemla, Meu-nier, and Wolfler Calvo 2013; Schuijbroek, Hampshire, andvan Hoeve 2013; George and Xia 2011) introduced bikerepositioning policies based on trucks. The user dissatisfac-tion function introduced by Raviv and Kolka (2013) mea-sures the performance of a BSS station and provides thequality of a repositioning. Nair et al. (2013) provides a de-tailed quantitative analysis of the repositioning strategies forVelib’, the BSS in Paris.

Crowd-based repositioning in BSS. Recent litera-ture has investigated crowd-based BSS balancing policies(Fricker and Gast 2012; Waserhole, Jost, and Brauner 2013;Pfrommer et al. 2014; Chemla, Meunier, and Pradeau 2013;GoDCgo 2011) to study the potential of influencing BSSusers by offering incentives that could improve balancingof the system. In Paris, Velib’ runs a static incentive schemathat offers users 15 extra free minutes each time they re-turn a bike to an elevated station (Velib’ 2014). However, incontrast, our proposed mechanism is dynamic and can ef-ficiently take into account the current state of the systemto decide where and what incentives to offer. Pfrommer etal. (2014) also introduced a dynamic pricing mechanism us-ing model-based predictive control principles. Our pricingmechanism, in contrast, is based on an efficient and prov-ably near-optimal online learning framework. Furthermore,we build an operating end-to-end system integrated with areal-world BSS for deployment, and we investigate the in-centive compatibility of our approach.

The ModelWe now formalize the problem addressed in this paper.

BSS. There are m stations in a city denoted by the setS = {s1, s2, . . . , sm}. We denote the number of bikes avail-able at time t at station s as v(s, t). Along the lines of Pfrom-mer et al. (2014), we split every day into 12 slices h ∈ Hof two hours each, where a time of the day t is mappedto the corresponding time-slice using h(t). The demand ofrentals from station si to station sj within time-slice h ismodeled as a discrete random variable z which represents acount of the number of trips with probability density func-tion τ(z|si, sj , h). We denote z(h) to represent the estimateof the total number of trips within time-slice h. The BSSinfrastructure has a proprietary demand forecaster that can

predict the future demand of incoming and outgoing trafficat the stations and is made available to us through their APIs.See (Come and Oukhellou 2012; Kaltenbrunner et al. 2010;Borgnat et al. 2011; Froehlich, Neumann, and Oliver 2009;Han, Come, and Oukhellou 2014) for possible approaches.

Quality of service. The goal of a bike repositioning pol-icy is to prevent customers’ dissatisfaction about not find-ing an available bike or a parking slot. Waserhole, Jost, andBrauner; Chemla, Meunier, and Pradeau; Fricker and Gast(2013; 2013; 2012) have proposed metrics aiming at max-imizing the number of trips or minimizing the number ofproblematic stations. However, these metrics fail to capturethe dissatisfaction experienced by BSS users. We adopt themeasure of service level proposed in Pfrommer et al. (2014):

Service level =Potential customers− No-service events

Potential customerswhere the number of no-service events corresponds to thecustomers who could not rent a bike at an empty station orfailed to return their bike at a full station.

Truck-based re-positioning policies. The BSS operatorallocates a fixed daily budget B that can be used by thetruck-based policy. The BSS company where we deployedour incentives system uses its proprietary re-positioning pol-icy. See (Chemla, Meunier, and Pradeau 2013; Raviv andKolka 2013) for possible approaches.

User model. Consider a user (or customer) u at locationlux interested in an action x ∈ {pick, return}, i.e., pick upor drop off a bike. We assume that, by default, the user wouldconsider the nearest station to her location to perform the ac-tion, given by sux ← argmins∈S d(l

ux , s), where d measures

the distance between two locations. The incentives systemencourages users to shift their pickup or drop-off stationsto contribute balancing the BSS. For simplicity, we assumethat the user u is willing to walk to another station up to amaximum distance γu, and, within this radius, her cost forthe additional effort is constant given by cu. She is not will-ing to walk for distance more than γu despite of any offer byour incentive system. We validate these assumptions througha survey study with real BSS customers. In our model, thecosts of the users are i.i.d. sampled from an underlying un-known distribution f . We let F : [cmin, cmax] → [0, 1]denote the cumulative distribution function (CDF) of costsor “cost curve”, unknown to the system.

Suppose a user u at location lux , going to station sux, isoffered an alternate station s∗x for a payment of p∗. We modelher reaction to this incentive as an indicator function:

1uaccept(l

ux , s∗x, p∗) =

{0 if γu < d(lux , s

∗x) or p∗ < cu

1 otherwise

which takes value 0 or 1 when the user rejects or acceptsthe offer, respectively. We remark that the information aboutuser cost cu and her true location lux is private to the user andmay vary among users. We consider the users as strategicagents who may untruthfully report their private informationabout the location lux or private cost cu to maximize theirprofit. For simplicity of the mechanism design, we use γ torepresent the maximum distance for the whole population,and we obtain its estimate through a user survey.

Procedure 1: Incentives Deployment Schema (IDS)1 Input: user u; location lux ; x ∈ {pick, return};2 Output: Offer: station s∗x; price p∗;3 Parameters: radius γ;

begin4 if HASINCENTIVESPENDING(u) then5 return null;6 candidate stations C ← S;7 default target sux ← argmins∈S d(l

ux , s);

8 foreach s ∈ C do9 s.status← PROBLEMATIC(s);

10 if d(lux , s) > γ then11 C = C \ {s};12 if x = return & s.status 6= EMPTY then13 C = C \ {s};14 if x = pick & s.status 6= FULL then15 C = C \ {s};16 if sux ∈ C OR C = ∅ then17 return null;18 s∗x ← argmins∈C d(l

ux , s);

19 p∗ ← PRICINGMECHANISM();20 Output: s∗x, p∗;

Incentivizing users for repositioning. We study anincentive system as an alternative to truck-based re-distribution in BSSs. We propose a dynamic incentivesschema that computes incentives each time a user makes anew request. The BSS operator regularly allocates a bud-get B(h) for time batch h, and the goal of the incentivesschema is to exploit the budget efficiently. Therefore, if auser u makes a request at time t for action type x, a dy-namic algorithm is responsible to select the alternate stations∗x and compute the value of the incentive p∗ based on thefollowing inputs: i) reported location of user lux , ii) currentstatus of the system: v(s, t) ∀s ∈ S, iii) near-future demandτ(z|si, sj , h) ∀si, sj ∈ S, iv) budget available, and v) infor-mation about historical interactions with users.

Our goal is to design a system that is i) budget-feasible,i.e., operates under strict budget constraints, ii) efficient interms of improvement in quality of service for a given bud-get, iii) incentive-compatible (truthful), i.e., it is in best inter-est of users to reveal true information, and iv) can deal withunknown user costs and learn pricing policies over time.

The Incentives SystemWe now present the various components of our incentivessystem and discuss its properties.

Incentives Deployment SchemaProcedure 1 illustrates the Incentives Deployment Schema(IDS) that handles the users’ requests forwarded by the de-ployed smartphone app. For each request, the IDS gets as in-put the user identifier u, the action type x ∈ {pick, return}and the user’s reported location lux . Based on the user inputand the current status of the system, IDS computes the offerthat consists of the alternative target station s∗x and the of-fered price p∗. Initially, the IDS ensures that the user does

not have any pending incentive, i.e., an incentive offer thatshe recently accepted but did not accomplish yet. Then itcreates a set of candidate stations that are within the max-imum walking distance γ from lux and are “problematic”,i.e. full or empty. The problematic status of the stations isbased on input from the APIs of the BSS infrastructure thatprovides the current status v(s, t) as well as the forecast offuture traffic. The IDS also takes into account the pendingincentives associated with each station while updating thestatus that additionally accounts for future traffic.

Once the IDS has filtered the candidate stations, it selectsas target station s∗x the one closest to reported location lux .Then, it sends a request to the pricing mechanism to obtainthe price p∗ to be offered. This offer is displayed to the useron the smartphone app. If the user accepts, the IDS registersthis incentive offer as pending until she accomplishes theassigned task or it expires after a prefixed amount of time.

Pricing Mechanism DBP-UCBThe main challenges in designing an optimal pricing mech-anism are due to the limited and dynamically changing bud-get (B) as well as pool size of users to make offers to (N )and the unknown “cost curve” F . Additionally, the mecha-nism has to compute the offers in an online setting with onlylimited interaction with the users in terms of the informa-tion that can be received from them for learning. First, let usconsider an offline setting where the cost curve F is knownand {p0(= cmin), . . . , pi, . . . , pK(= cmax)} be the set ofavailable prices that the mechanism can offer. In this case,the optimal truthful mechanism denoted by OPT-FIX, is tooffer a fixed price pOPT (Singer 2010) given by:

pOPT = argmaxp

min

{F (p),

B

N · p

}s.t. p ∈ {p0, . . . , pK}

(1)Now, in case of unknown F , we can cast the problem as thatof online learning of the optimal price. Note that, F (p) sim-ply denotes the probability of getting an acceptance from theuser when offered price p. In particular, we are interestedin designing mechanisms based on the posted-price modelwhere the mechanism makes a price offer p and the usersimply says “yes” or “no” to the offer, instead of solicitingthe user’s bid. In a learning framework, the mechanism canmake different price offers over time to different users, anduse the binary feedback of acceptance to learn an estimate ofF (p). There is an inherent explore-exploit trade off here ofexploring (or learning) about different, potentially subopti-mal, prices and exploiting the estimates of F (p) to offer theprice that appears best. Multi-armed bandits (MAB) (Lai andRobbins 1985) provide a natural formalism for tackling thisproblem, considering the prices as actions (or arms) with un-known stochastic reward given by the function F (p). In theMAB framework, the performance of a mechanism is mea-sured in terms of its “regret” Rn, i.e., the cumulative lossof the mechanism w.r.t. the optimal fixed price pOPT aftern iterations. The goal is to design a “no-regret” mechanismthat learns to perform competitively, i.e., the average regretw.r.t. n goes to zero limn→∞ Rn/n = 0. However, note thatin contrast to standard MAB settings, here actions have dif-

Procedure 2: Pricing Mechanism DBP-UCB1 Input: start time t0; prices {p0, . . . , pi, . . . , pK};2 Output: Price pn at iteration n;3 Initialize:

• n = 0;h0 = h(t0); . First batch• B = B(hn);Bn = B; N = 2 · z(hn);• Nn

i = 0;Fni = 0; ∀i ∈ [0, . . . ,K] . Value estimates

foreach request by user at time t do4 if hn 6= h(t) then5 hn = h(t); . New time batch6 Bn = Bn +B(hn); B = Bn; . More budget7 N = 2 · z(hn); . Trips estimate from forecaster

8 Fni = Fn

i +√

2·ln(n)Nn

i; . Same as in BP-UCB

9 in = argmaxi min{F ti ,

BN ·pi

}s.t. pi ≤ Bn;

10 Output: Offer price pn = pin at iteration n;11 Feedback: Observe acceptance decision yn;12 Update Variables:

• Bn+1 = Bn − pn · yn; Fn+1in = Fn

in +(yn−Fn

in )

(Nnin

+1);

• Nn+1in = Nn

in + 1; hn+1 = hn; n = n+ 1;13 Wait for new user request;

ferent costs and there is a budget constraint.For static settings where the budget (B) and the number of

users (N ) is known and fixed in advance, Singla and Krause(2013) designed a no-regret mechanism BP-UCB that isbudget feasible, truthful and has fast convergence rates. Thisscenario does not correspond to the incentives system we areinterested in as, in this context, the pricing mechanism hasto operate continuously throughout time, is allocated newbudget in time batches and the number of users coming tothe system is dynamic. In this light, we design our mech-anism DBP-UCB (Dynamic Budgeted Procurement usingUpper Confidence Bounds), illustrated in Procedure 2, as adynamic variant of BP-UCB. The key idea we use is that thelearning process of the mechanism can be decoupled fromthe constraints on B and N . While the parameters B and Nchange dynamically at the onset of each time batch, it carriesover the estimates of F (p) across batches, ensuring DBP-UCB maintains the convergence properties of BP-UCB.

At a high level, the mechanism DBP-UCB operates asfollows. At the onset of each new time batch h, the mecha-nism is provided with an additional budgetB(h) by the BSSoperator. Furthermore, the number of participants N for abatch is approximated by the expected number of trips z(h)taking place in the corresponding batch h estimated by theBSS forecaster. In particular, the expected number of rentalsis multiplied by two as the user may interact with the incen-tives system both to pick and return a bike. The mechanismsequentially interacts with users in discrete steps denoted byn. Let Bn be the budget at iteration n, Nn

i be the numberof times pi price has been offered so far, and Fn

i be the cur-rent estimate of the cost curve for prices ∀i ∈ [0, . . .K]. Themechanism also maintains upper confidence bounds on Fn

i

denoted as Fni , representing the optimistic estimates of F at

What is the maximum additional distance you are willing to walk?

Choose one of the options:

€

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Figure 3: Survey study with customers of a real-world BSS1 in a city of Europe where we deployed our incentive system

iteration n, and used to compute the optimal price pn to of-fer. The mechanism receives the binary feedback yn whichis then used to update the estimates for price pn.

Truthfulness of Incentives SystemAs the system users may act strategically, it is importantto analyze the truthfulness of the IDS to understand thereal-world performance of the proposed system. Each userhas a private cost cu and location lux and we now discuss thetruthfulness of our system w.r.t. these two dimensions ofthe users’ private information. The mechanism DBP-UCBis based on the posted-price model, where it is in the user’sbest interest to truthfully accept the offer whenever theoffered price is higher than the private cost cu. Furthermore,given the large scale of the system, the interaction witha single user does not significantly affect the learningbehavior of the mechanism, thus making it behave truthfullyin real-world BSSs.

Let us now analyze the truthfulness of IDS w.r.t the lo-cation information. One crucial aspect here is that no in-centives are offered when the user’s default station sux is al-ready among the candidate stations. This notably improvesthe efficiency (by over 50% in our simulations) of the IDSby avoiding to pay out incentives for stations that the userwould have taken anyways. We discuss the implications ofthis on the truthfulness of IDS below. If the system has noway to infer sux, the user may indeed act strategically by mis-reporting to obtain an incentive that would have not beenoffered otherwise. In the real deployment of our system, in-stead of allowing the user to declare her current location (lux),it is directly retrieved from the deployed mobile-app whichprovides localization features. Based on this location infor-mation, the user’s default pickup station can directly be in-ferred. This technical solution does not directly apply to thebike drop-off scenario where users explicitly declare theirtarget location. However the drop-off location could poten-tially be inferred based on user’s historical trips, making thesystem incentive-compatible in real-world deployments.

Experimental EvaluationIn this section, we carry out extensive experiments to under-stand the practical performance of our system.

Datasets and Experimental SetupFirst, we describe our datasets and the BSS simulator.

Historical BSS dataset. For our simulations, we useda historical dataset from Boston’s Hubway, made publicly

available by the Boston Metropolitan Area Planning Coun-cil2. The Hubway dataset contains data collected between28th July, 2011 and 1st October, 2012 with rich informationabout 95 stations, 694 bicycles, 552, 030 rentals, and snap-shots of the status of the BSS at regular intervals.

Survey study. We did a survey study among the cus-tomers of a real-world BSS1 in a city of Europe where wealso deployed our system. Our goal is to validate the re-alism of our model, as well as to obtain realistic statisticsabout the distribution of users’ personal costs cu and of theirmaximum walking range γu. The participants were askedgeneric questions about their rental behaviors (e.g., purposeand duration of the trips) and then we introduced them tothe proposed incentives system asking various questions toelicit their preferences about private cost and walking dis-tance. Figure 3(a) illustrates a survey question, and the dis-tributions obtained from the survey for γu and cu are shownin Figures 3(b) and 3(c) respectively. Note that the surveyalso contained an additional choice of “unwilling to walk”or “unwilling to participate at any cost” that accounted forroughly 20% of the response for both questions.

Simulator. We built a complete simulator of a real BSSbased on Hubway’s historical data as well as the survey datafrom customers. Each simulation starts by taking a snapshotof the status of the BSS, i.e., the number of bikes at eachstation retrieved from the historical dataset and runs the sim-ulation for a total of 30 days. The BSS simulator generatesusers’ trip events by sampling from the distribution learntfrom the historical data. Lastly, the simulator associates eachrental to a user with cost cu and γu sampled from the dis-tributions obtained from the survey. For the TRUCKS, weused the myopic greedy policy as defined by Chemla, Meu-nier, and Pradeau (2013). Using the idea of dynamic (duringrush hours) and static (during off hours) repositioning fromRaviv and Kolka (2013), our policies operate in time from12:00 p.m. and 3:00 p.m, and then from mid-night to morn-ing. We assume that the trucks entail a fixed cost of 50 eper hour to the system and the policy allocates the numberof hours based on the total budget.

ResultsWe now discuss the findings from our experiments.

Varying budget. We compare the performance of differ-ent policies by varying the allocated budget. We envisionthat the operators would deploy the incentives system in par-

2http://hubwaydatachallenge.org/.

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(c) Varying participationFigure 4: Simulation results based on historical data of Hubway BSS2 and survey data from customers of real-world BSS1

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ers  

Hour  of  the  day  

Distribu(on  of  accepted  offers  over  the  day  

(c) Dist. of accepted offers over dayFigure 5: Results from deployment of our system through smartphone app on a real-world BSS1 in beta-test phase for 30 days

allel to existing TRUCKS repositioning rather than as stan-dalone policy. To take this into consideration, we comparethe policies when deployed in addition to an already run-ning TRUCKS policy allocated with 150 e, (i.e., equivalentof 3 hours of trucks that would run between 12:00 p.m. and3:00 p.m). Apart from comparing to TRUCKS, we also com-pare our proposed system with IDS running different base-line pricing mechanisms instead of DBP-UCB: i) MINI-MUM mechanism selects the minimum price available cmin;ii) MEAN mechanism offers the mean price obtained fromthe user’s cost distribution. Our results in Figure 4(a) indi-cate that the proposed IDS with DBP-UCB pricing mech-anism compares favorably w.r.t. TRUCKS, as well as otherbaseline pricing mechanisms. The performance of DBP-UCB almost matches OPT-FIX, as it converges very fast tothe optimal price in less than a day of simulation time. Notethat allocating more budget B to TRUCKS is simply equiva-lent to running policy with total of B + 150 e budget.

Budget tradeoff between IDS and TRUCKS. This ex-periment aims to understand how to trade off budget in-vestment between TRUCKS and the incentives system. Fig-ure 4(b) illustrates that the best service levels are achievedwhen the two repositioning services are run in complementto each other, and shows the high potential of adding our in-centives system to the existing BSS infrastructure. In fact,these two policies have complementary effects. While thetrucks tackle the imbalances at a macro level by moving thebicycles from a part of the city to the other, the effect ofthe incentives system is rather dynamic and at a micro level,incorporating the traffic flow and fluctuating demands.

Varying user participation. The previous experimentsassume that all the BSS customers participate in the incen-tives system. However, this assumption may not hold in re-ality, especially in the testing phase of the system. With thesame setup as used in Figure 4(a), we vary the participation

rate of users between 0% and 100%, with budget fixed at300 e. Figure 4(c) illustrates that the proposed system cansurpass standalone TRUCKS policy with 20% participation.

DeploymentWe deployed our incentives system on a real-world BSS in abeta-test phase for a period of 30 days in a city of Europe, incollaboration with a large scale bike sharing company1. Wedesigned a smartphone app as an interface between the usersand the incentives system. The system itself was integratedwith the BSS infrastructure through their APIs. The partici-pants were offered monetary incentives for the bike pickupscenario, with payments directly transferred to the customeraccounts. Given the small pool of participants in the testingphase limiting the process of learning, we adopted a simplepricing mechanism based on OPT-FIX. Using the approxi-mate users’ cost distribution, denoted as F , obtained throughthe survey study, we computed the approximation of the op-timal price pOPT using Equation 1 by replacing F with F .We now present the findings from this deployment.

Participation. Figure 5(a) represents the histogram of thenumber of incentives obtained by each participant. The re-sult shows that most participants collected five or less incen-tives each while few particularly active users collected morethan 50 incentives each.

Reaction to incentives. In total, the acceptance rate of theincentive offers over all participants was about 60%. Here,the rejection of an offer corresponds to the case of a userrejecting an incentive to pick up a bike at an offered targetstation and starting a rental somewhere else. Furthermore, aswe recorded the information about the user’s location whenan offer is made, we can compute the distance that the userswere required to walk for a given offer. Figure 5(b) showsthe probability of an offer being accepted as decreasing withrespect to the distance to be walked and matches closely withthe survey data in Figure 3(b).

Time and space distribution. Figure 5(c) shows that in-centives were not collected equally throughout the day, butmost incentives were collected during the afternoon and atlate morning. In Figure 2, the diameter of each station isproportional to the number of incentives collected by usersat that station. The map shows that the majority of incentiveswere paid out at stations in the city center.

ConclusionsThere is much potential in intelligent and self-sustainablesystems that incentivize and empower their users to activelyengage in the system processes. In this paper, we presentedthe architecture of an incentives system to engage BSS usersin the bike repositioning process. We worked in collabo-ration with a bike sharing company, designed and built aworking system integrated with the BSS infrastructure. Weinvestigated the incentive compatibility of our system, andalso designed a dynamic pricing mechanism DBP-UCB tooptimize the efficiency of the incentives system. We evalu-ated the proposed system through extensive simulations us-ing historical and user survey data, and deployed it througha smartphone app for a period of 30 days.Acknowledgments. We’d like to thank bike sharing operatorMVGmeinRad for promoting the surveys and trial to their cus-tomers, and ElectricFeel Mobility Systems for providing access totheir software platform and infrastructure for running the trial. Thisresearch is supported in part by SNSF grant 200021 137971 andNano-Tera.ch program as part of the Opensense II project.

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