399 research outputs found
Incentives and Redistribution in Homogeneous Bike-Sharing Systems with Stations of Finite Capacity
Bike-sharing systems are becoming important for urban transportation. In such
systems, users arrive at a station, take a bike and use it for a while, then
return it to another station of their choice. Each station has a finite
capacity: it cannot host more bikes than its capacity. We propose a stochastic
model of an homogeneous bike-sharing system and study the effect of users
random choices on the number of problematic stations, i.e., stations that, at a
given time, have no bikes available or no available spots for bikes to be
returned to. We quantify the influence of the station capacities, and we
compute the fleet size that is optimal in terms of minimizing the proportion of
problematic stations. Even in a homogeneous city, the system exhibits a poor
performance: the minimal proportion of problematic stations is of the order of
(but not lower than) the inverse of the capacity. We show that simple
incentives, such as suggesting users to return to the least loaded station
among two stations, improve the situation by an exponential factor. We also
compute the rate at which bikes have to be redistributed by trucks to insure a
given quality of service. This rate is of the order of the inverse of the
station capacity. For all cases considered, the fleet size that corresponds to
the best performance is half of the total number of spots plus a few more, the
value of the few more can be computed in closed-form as a function of the
system parameters. It corresponds to the average number of bikes in
circulation
Incentives and redistribution in homogeneous bike-sharing systems with stations of finite capacity
International audienceBike-sharing systems are becoming important for urban transportation. In these systems, users arrive at a station, pick up a bike, use it for a while, and then return it to another station of their choice. Each station has a finite capacity: it cannot host more bikes than its capacity. We propose a stochastic model of an homogeneous bike-sharing system and study the effect of the randomness of user choices on the number of problematic stations, i.e., stations that, at a given time, have no bikes available or no available spots for bikes to be returned to. We quantify the influence of the station capacities, and we compute the fleet size that is optimal in terms of minimizing the proportion of problematic stations. Even in a homogeneous city, the system exhibits a poor performance: the minimal proportion of problematic stations is of the order of the inverse of the capacity. We show that simple incentives, such as suggesting users to return to the least loaded station among two stations, improve the situation by an exponential factor. We also compute the rate at which bikes have to be redistributed by trucks for a given quality of service. This rate is of the order of the inverse of the station capacity. For all cases considered, the fleet size that corre-sponds to the best performance is half of the total number of spots plus a few more, the value of the few more can be computed in closed-form as a function of the system parameters. It corresponds to the average number of bikes in circulation
Two-choice regulation in heterogeneous closed networks
A heterogeneous closed network with one-server queues with finite capacity
and one infinite-server queue is studied. A target application is bike-sharing
systems. Heterogeneity is taken into account through clusters whose queues have
the same parameters. Incentives to the customer to go to the least loaded
one-server queue among two chosen within a cluster are investigated. By
mean-field arguments, the limiting queue length stationary distribution as the
number of queues gets large is analytically tractable. Moreover, when all
customers follow incentives, the probability that a queue is empty or full is
approximated. Sizing the system to improve performance is reachable under this
policy.Comment: 19 pages, 4 figure
A Stochastic Model for Car-Sharing Systems
Vehicle-sharing systems are becoming important for urban transportation. In
these systems, users arrive at a station, pick up a vehicle, use it for a while
and then return it to another station of their choice. Depending on the type of
system, there might be a possibility to book vehicles before picking-up and/or
a parking space at the chosen arrival station. Each station has a finite
capacity and cannot host more vehicles and reserved parking spaces than its
capacity. We propose a stochastic model for an homogeneous car-sharing system
with possibility to reserve a parking space at the arrival station when
picking-up a car. We compute the performance of the system and the optimal
fleet size according to a specific metric. It differs from a similar model for
bike-sharing systems because of reservation that induces complexity, especially
when traffic increases
User-based redistribution in free-floating bike sharing systems
We investigate the problem of user-based redistribution for free-floating bike sharing systems (BSS). We present a stochastic model of the bike dynamics and we show that the spatial distribution of bikes is correlated. This is specific to free-floating systems and it results in a substantially reduced service level.
Offering incentives to users may stimulate them to change their behavior and usage pattern. We analyze drop-off incentives, derive an incentive methodology and study its potential. We show that by implementing a smart incentive system, the number of bikes for establishing a specific service level can be reduced significantly, even if only a minority of users participates. Under realistic behavioral assumptions, 30–50% reduction of bikes is achievable, which converts into substantial costs savings for the operator.
Our research was carried out in the context of the development of the new e-bike sharing system “smide” in Zurich, launched in 2017. The incentive approach has been implemented and tested in a field test
A dynamic approach to rebalancing bike-sharing systems
Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Bike sharing provides an environment-friendly way for traveling and is
booming all over the world. Yet, due to the high similarity of user travel
patterns, the bike imbalance problem constantly occurs, especially for dockless
bike sharing systems, causing significant impact on service quality and company
revenue. Thus, it has become a critical task for bike sharing systems to
resolve such imbalance efficiently. In this paper, we propose a novel deep
reinforcement learning framework for incentivizing users to rebalance such
systems. We model the problem as a Markov decision process and take both
spatial and temporal features into consideration. We develop a novel deep
reinforcement learning algorithm called Hierarchical Reinforcement Pricing
(HRP), which builds upon the Deep Deterministic Policy Gradient algorithm.
Different from existing methods that often ignore spatial information and rely
heavily on accurate prediction, HRP captures both spatial and temporal
dependencies using a divide-and-conquer structure with an embedded localized
module. We conduct extensive experiments to evaluate HRP, based on a dataset
from Mobike, a major Chinese dockless bike sharing company. Results show that
HRP performs close to the 24-timeslot look-ahead optimization, and outperforms
state-of-the-art methods in both service level and bike distribution. It also
transfers well when applied to unseen areas
Fluid and Diffusion Limits for Bike Sharing Systems
Bike sharing systems have rapidly developed around the world, and they are
served as a promising strategy to improve urban traffic congestion and to
decrease polluting gas emissions. So far performance analysis of bike sharing
systems always exists many difficulties and challenges under some more general
factors. In this paper, a more general large-scale bike sharing system is
discussed by means of heavy traffic approximation of multiclass closed queueing
networks with non-exponential factors. Based on this, the fluid scaled
equations and the diffusion scaled equations are established by means of the
numbers of bikes both at the stations and on the roads, respectively.
Furthermore, the scaling processes for the numbers of bikes both at the
stations and on the roads are proved to converge in distribution to a
semimartingale reflecting Brownian motion (SRBM) in a -dimensional box,
and also the fluid and diffusion limit theorems are obtained. Furthermore,
performance analysis of the bike sharing system is provided. Thus the results
and methodology of this paper provide new highlight in the study of more
general large-scale bike sharing systems.Comment: 34 pages, 1 figure
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