Dynamic Resource Redistribution and Demand Estimation: An Application to Bike Sharing Systems

Mellou, Konstantina; Jaillet, Patrick

doi:10.2139/ssrn.3336416

Cited by 6 publications

(5 citation statements)

References 33 publications

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“…In order to overcome this issue, rebalancing processes seek to minimize three variables [ 14 ]: (i) the fraction of time the stations have no available bicycles or docks; (ii) the number of rebalancing operations; and (iii) the travelled distance. Considering these three objectives, rebalancing can be formalized as a dynamic resource redistribution based on demand estimation in the field of BSSs [ 15 ]. Under this perspective, scientific activity focuses on designing rebalancing algorithms, which commonly include three phases [ 16 ]: (i) clustering; (ii) prediction; (iii) and intra-cluster and inter-cluster routing.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Díaz

Pozo

González

et al. 2020

Sensors

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Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.

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Section: Related Workmentioning

confidence: 99%

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Díaz

Pozo

González

et al. 2020

Sensors

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“…Jia et al proposed a Modified Artificial Bee Colony (MABC) in order to find the optimal routes and desired parked bikes of stations [23]. Meanwhile, hybrid algorithms have also been put forward, such as ant-colony optimization and Constraint Programming (CP) [36] as well as Large Neighborhood Search (LNS) and CP [28] by Di Gaspero et al It is worth mentioning that for dynamic cases, a series of work applied a rolling horizon approach to decrease the scale of the problem, e.g, [27] by Shui et al, [33] by Zhang et al and [37] by Mellou et al…”

Section: Solving Algorithmsmentioning

confidence: 99%

Dynamic Rebalancing Optimization for Bike-Sharing System Using Priority-Based MOEA/D Algorithm

Zhang

et al. 2021

IEEE Access

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As an indispensable part of public transportation systems, the bike-sharing system (BSS) can improve road resource utilization and alleviate traffic congestion, significantly improving urban mobility. The disproportion between the demand and supply creates a giant gap for maintaining the smooth functioning of the system. To address the issue, this paper proposes a dynamic optimization rebalancing model for docked bike-sharing systems, which aims at minimizing the operation cost of rebalancing while maximizing the user satisfaction. The rebalancing demand is evaluated using both historical and predicted data so that a second time service for each station could be avoided within a rebalancing horizon. A time-window based satisfaction modeling is put forward to evaluate user satisfaction. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) under the rolling horizon strategy is adopted to solve the model. To improve the algorithmic performance, local search based on station priority is applied. Numerical experiments using real-world data were implemented to demonstrate the proposed model and the advantage of the improved algorithm. As the results indicate, the proposed algorithm outperforms the nondominated sorting genetic algorithm II (NSGA-II) and MOEA/D without prioritybased local search. Solutions with higher satisfaction profit can be discovered with the help of a local search heuristic based on station priority. The experiment also revealed that a slight increase of 7.02% (¥29.2) in the rebalancing cost could yield a significant growth of 1233.7% (¥288.7) in satisfaction profit.

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“…They also proved that there is an optimal inventory level. Mellou and Jaillet [17] proposed a novel mixed-integer programming formulation to solve the dynamic repositioning problem and provided a linear programming model to capture the bike flows from all trips. The authors of these studies computed the repositioning flows requiring a huge number of trucks, but in this paper we relax this requirement by setting the availability of parking regions to a certain level.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

Fan

2020

Processes

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The dockless bike sharing system (DBSS) has been globally adopted as a sustainable transportation system. Due to the robustness and tractability of the closed queuing network (CQN), it is a well-behaved method to model DBSSs. In this paper, we view DBSSs as CQNs and use the mean value analysis (MVA) algorithm to calculate a small size DBSS and the flow equivalent server (FES) algorithm to calculate the larger size DBSS. This is the first time that the FES algorithm is used to study the DBSS, by which the CQN can be divided into different subnetworks. A parking region and its downlink roads are viewed as a subnetwork, so the computation of CQN is reduced greatly. Based on the computation results of the two algorithms, we propose two optimization functions for determining the optimal fleet size and repositioning flow, respectively. At last, we provide numerical experiments to verify the two algorithms and illustrate the optimal fleet size and repositioning flow. This computation framework can also be used to analyze other on-demand transportation networks.

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Dynamic Resource Redistribution and Demand Estimation: An Application to Bike Sharing Systems

Cited by 6 publications

References 33 publications

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Dynamic Rebalancing Optimization for Bike-Sharing System Using Priority-Based MOEA/D Algorithm

Optimization Strategies for Dockless Bike Sharing Systems via two Algorithms of Closed Queuing Networks

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