The added-value of demand forecasting in bike sharing systems:
A general framework and selected case studies
S. Ataça,∗ , N. Obrenovićb , and M. Bierlairea
a École Polytechnique Fédérale de Lausanne, Transport and Mobility Laboratory, Lausanne,

Switzerland
selin.atac@epfl.ch, michel.bierlaire@epfl.chb University of Novi Sad, BioSense Institute, Novi Sad, Serbia
nikola.obrenovic@biosense.rs∗ Corresponding author
Extended abstract submitted for presentation at the 11th Triennial Symposium onTransportation Analysis conference (TRISTAN XI)
June 19-25, 2022, Mauritius IslandApril 4, 2022
Keywords: Bike-sharing systems; mixed integer linear programming; value of demand forecasting; discrete-event simulation

1INTRODUCTION

Transportation is becoming more and more essential in the developing world. According toPachauri et al. (2014), it is observed that transportation is responsible for 14% of the greenhouse gas emissions in 2014. When the end-use sectors are also considered, it accounts for thesecond-largest shares of U.S. greenhouse gas emissions by 28.7% in 2019 (EPA, 2021). With theincreasing environmental concerns, the idea of shared mobility, such as ride-hailing, ride-sharing,and vehicle sharing, has emerged in recent years.
Our previous work examines the components of vehicle sharing systems (VSSs) and thechallenges faced in terms of decision level, actors, and layers (Ataç et al., 2021c). The reader cankindly refer to this work for the terminology in VSSs. Although the operational level challenges
demand forecasting and rebalancing operations optimization are widely studied, we observe that
there is a lack of investigating the relationship between them and the value of demand forecasting.
Here, we regard this value through a quantification of the improvement of rebalancing operationsin presence of demand forecast. Therefore, we explore the value of demand forecasting in bikesharing systems (BSSs) in this work. This work considers a one-way station-based BSS whererebalancing operations are conducted when the system is low in operation, i.e., static rebalancing.
An early version of this work (Ataç et al., 2021a) presents an optimization-simulation frameworkfor BSSs and shows results on synthetic data. In this work, we incorporate a clustering moduleto solve larger instances and present results on selected real-life case studies.

2METHODOLOGY

Several methods are used in the literature to forecast trip demand and bike count behavior inBSSs. These include regression (Ashqar et al., 2019), Markov chain (Schuijbroek et al., 2017),
and behavioral models (Faghih-Imani et al., 2017). In addition to the historical trip records,
methods also use historical weather, socio-economic characteristics of the users, air quality, etc.
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S. Ataç, N. Obrenović, and M. Bierlaire

2The more information is available for forecasting, the more precise the models are. However,collecting and analyzing such information is not always trivial. Therefore, knowing the value of
demand forecasting is essential. It allows the decision maker know the upper limit for the budget
allowance of demand forecasting operations.

Figure 1 – The framework for VSS simulation and rebalancing optimization
Figure 1 illustrates our framework that involves two main modules, namely simulation andoptimization. The optimization module further splits into two parts, i.e., clustering and rebalancing. The input parameters are passed to the simulation module and a full day is simulated.
Then, the final configuration of the simulated day, the desired initial configuration for the following day, and precomputed clustering information are given to the optimization module that
calculates the best routing for the rebalancing trucks. The number of lost trip demand and thecost of rebalancing operations are recorded as performance measures and stored in the database.
The dashed arrow represents the iterative process of the framework.
We consider two extreme cases: the first, unknown demand case, assumes that the trip
demand information is not available to the operator. Therefore, the vehicles are rebalanced
to the same initial configuration every day. The second, known demand case, assumes perfectknowledge about the future demand. In other words, the operator knows where and when atrip demand will occur. The initial configuration of the next day is determined according to thisinformation. This helps us to assess the maximum budget allocation to forecast the trip demand.

2.1

Simulation

A discrete event simulator that simulates the trip demand of one full day of one-way stationbased BSS with capacitated stations is built for this framework (kindly see the details in Ataçet al. (2021a)). The number, location, and capacity of stations, time of the trip request, locationof origin and destination, and distance between stations are passed to the simulator. Then, thefinal bike configuration is obtained from the simulator, that is passed to the optimization module.

2.2

Clustering and rebalancing operations optimization

Generally, rebalancing operations can be modeled as mixed integer linear programming (Dell’Amicoet al., 2014). Although the rebalancing operations optimization can be optimally solved for small
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size instances, the complexity of the models exponentially increases with the problem size. To
overcome that, station clustering is one of the approaches to split the problem into sub problems(Lahoorpoor et al., 2019). In Ataç et al. (2021b), we test different clustering approaches thatcan be applied for rebalancing operations optimization. As a result, we use the agglomerative
hierarchical clustering according to station locations in this work.
We utilize and modify the rebalancing operations optimization model, named F1, fromDell’Amico et al. (2014). Furthermore, the model is solved only for the subset of stations thathave non zero demand (Ataç et al., 2021b). The final configuration, that is received from thesimulator, and the desired initial configuration are used to calculate the demand of a station.
Along with this information, the number of stations, distance between stations, number andcapacity of the trucks are utilized in the rebalancing operations optimization.

3NUMERICAL EXPERIMENTS

3.1

Data sets

This work applies the introduced framework to four case studies: nextbike Sarajevo, nextbike
Berlin, Divvy Chicago, and Citi Bike New York. These case studies operate with 21, 298, 681,
and 1361 stations, respectively. The data for the first two are obtained from their system inreal time and then preprocessed to obtain O-D trip information. The data for the last two arepublicly available. The O-D trip request time and location and destination location information
are taken from the real data, and simulated using the developed simulator to see whether thistrip is feasible, given the initial configuration of the system.

3.2Results

Figure 2 – Unknown vs known case
In Figure 2, we present results for 14 consecutive days. The horizontal axis shows the days
where the vertical axis shows the ratio of lost and total trip demand. As expected, unknown
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demand case produces more lost demand than the known case. On the other hand, it is worth tonote that the perfect demand knowledge does not guarantee satisfying all the trip demand. Thiscan be due to the station capacity, the number of bikes available, etc. Thanks to the extended
framework, we observe that the benefit of demand forecasting in larger instances, i.e., Chicago
and New York, is clearer and more significant than in smaller ones, i.e., Sarajevo and Berlin.
This indicates that smaller systems do not necessarily require precise demand forecasting.
Having analyzed the information passed from the optimization module to the database, wealso notice that the rebalancing operations cost,that is obtained by solving the rebalancing
operations optimization model, does not significantly change from one case to the other. Thisshows that the use of demand forecasting does not affect the rebalancing cost as the routes tend
to be similar to each other.

4

CONCLUSION AND FUTURE WORK

The proposed framework is able to assess the need of precise demand forecasting in different
BSSs. It also assists the decision maker by informing them on the upper limit of the budget fordemand forecasting. This way, the decision maker is informed about the trade-off.
The numerical experiments presented so far show results for the two extreme cases. The key
finding is that the demand forecasting might not significantly decrease demand loss nor improverebalancing operations in smaller BSSs. Therefore, the operators of such systems should assess
the potential benefits of demand forecasting before investing into creation of the needed models.
As it is not possible to know the true demand in advance (unless the system is reservation
based), one future research direction may include looking into different levels of knowledge intrip demand by deteriorating this information. Furthermore, this framework can form a base totest different values of input parameters to see their effects on the system and to support thedecision maker in both tactical and operational level decisions. Finally, another future research
direction might be looking into the effect of clustering on results.

References
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