Optimizing the level of service quality of a bike-sharing system

Álvarez-Valdés, Ramón; Belenguer, José Manuel; Benavent, Enrique; Bermúdez, José D.; Muñoz, Facundo; Vercher, Enriqueta; Verdejo, Francisco

doi:10.1016/j.omega.2015.09.007

Cited by 171 publications

(92 citation statements)

References 23 publications

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“…Bike rebalancing problem is one of the main problems in operating the bike sharing systems [10,12]. The aim of rebalancing is to specify the number of bikes loading/unloading at each station and find the optimal truck routes subject to various constraints [7,9,13].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Their computational time efficiently solves networks with up to 50 vertices and extends exponentially with the larger networks. With different objective, Alvarez-Valdes et al [12] optimized the level of service of bike sharing systems by forecasting the unsatisfied demand at each station and proposing an algorithm upon the estimated measures. Their tested procedure on a relatively small network demonstrates to be an extremely fast heuristic algorithm.…”

Section: Static Rebalancing Problemmentioning

confidence: 99%

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Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

et al. 2019

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Bike sharing systems, as one of the complementary modes for public transit networks, are designed to help travelers in traversing the first/last mile of their trips. Different factors such as accessibility, availability, and fares influence these systems. The availability of bikes at certain times and locations is studied under rebalancing problem. The paper proposes a bottom-up cluster-based model to solve the static rebalancing problem in bike sharing systems. First, the spatial and temporal patterns of bike sharing trips in the network are investigated. Second, a similarity measure based on the trips between stations is defined to discover groups of correlated stations, using a hierarchical agglomerative clustering method. Third, two levels for rebalancing are assumed as intra-clusters and inter-clusters with the aim of keeping the balance of the network at the beginning of days. The intra-cluster level keeps the balance of bike distribution inside each cluster, and the inter-cluster level connects different clusters in order to keep the balance between the clusters. Finally, rebalancing tours are optimized according to the positive or negative balance at both levels of the intra-clusters and inter-clusters using a single objective genetic algorithm. The rebalancing problem is modeled as an optimization problem, which aims to minimize the tour length. The proposed model is implemented in one week of bike sharing trip data set in Chicago, USA. Outcomes of the model are validated for two subsequent weekdays. Analyses show that the proposed model can reduce the length of the rebalancing tour by 30%.Bike sharing systems are one of the effective and environmentally friendly solutions to the first/last mile problem; they notably reduce motorized trips, air pollution, fuel consumption and needs for parking spaces [1][2][3][4][5][6][7]. Since the last decade, these systems have become one of the main complementary modes of public transit systems in many urban areas. Different factors such as accessibility to stations, availability of bikes and vacant docks, and fares influence the effectiveness of these systems, among which the availability of bikes during the day is one of the most challenging issues in the designing, operating, and management of these services.To a large extent, bike sharing systems are used for one-way trips, and such a trend leads to inappropriate bike distribution in time and space [7,8]. As a result, unmet demand and consequently user dissatisfaction occur in the system. To minimize the demand dissatisfaction (i.e., maximize usage and revenue), the redistribution of bikes over stations is required. This redistribution, also called rebalancing, is implemented by employing vehicles (usually trucks) to collect surplus bikes from overloaded stations and deliver to bike deficient stations [9]. The efficiency of rebalancing operations is important to the authorities to ensure that the number of bikes and vacant docks at each station are periodically restored to predefined target values [8].Rebalancing...

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Section: Literature Reviewmentioning

confidence: 99%

Section: Static Rebalancing Problemmentioning

confidence: 99%

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

et al. 2019

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“…As Spain is one of the countries with the highest number of bike-sharing systems, there is growing interest in their research [e.g., [31][32][33][34][35][36]] and there is an initiative to publish data and information about bike-sharing systems [37], but there is still room for further investigation in the topics of the present paper.…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of Smart Configuration in Pedelec-Sharing: Evidence from a Panel Survey in Madrid

Munkácsy

Monzón

2017

Journal of Advanced Transportation

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Despite the recently increasing research interest, this is one of the first studies employing a panel sample of users and nonusers to understand the bike-sharing phenomenon ( = 205). On the basis of a novel surveying technique, a case study on the clients of the state-of-the-art bike-sharing scheme of Madrid (Spain) is presented. BiciMAD is a system of the latest generation, namely, multimodal demand responsive bike-sharing: a fleet of electric pedal-assisted bicycles (pedelecs) with an advanced technology and unique smart service configuration to tackle challenges that may hinder the promotion of cycling and bike-sharing in the city. A statistical test has verified that there is a moderate association between previous intention and actual use of bike-sharing (Cramer's V = 0.25) and both barriers and motivators of further use have been identified. Indicators on mobility patterns show that although drawing primarily from other sustainable modes of transport, bike-sharing has increased mobility (total number and distance of trips) and especially active travel but decreased the perceived travel time.

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“…The proposed taxi dispatch solution aims to serve as many ride requests as possible while minimizing the total reposition cost. We model an urban transportation system of interest as a flow network, which is a useful approach introduced to manage mobility-on-demand platforms [32,38,39]. In particular, we model the transportation system as a grid-world consisting of homogeneous regular hexagonal cells as in [18,27,40].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization

Kim

Huh

et al. 2020

IEEE Access

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In this paper, we discuss a large-scale fleet management problem in a multi-objective setting. We aim to seek a receding horizon taxi dispatch solution that serves as many ride requests as possible while minimizing the cost of relocating vehicles. To obtain the desired solution, we first convert the multi-objective taxi dispatch problem into a network flow problem, which can be solved using the classical minimum cost maximum flow (MCMF) algorithm. We show that a solution obtained using the MCMF algorithm is integer-valued; thus, it does not require any additional rounding procedure that may introduce undesirable numerical errors. Furthermore, we prove the time-greedy property of the proposed solution, which justifies the use of receding horizon optimization. For computational efficiency, we propose a linear programming method to obtain an optimal solution in near real time. The results of our simulation studies using the real-world data for the metropolitan area of Seoul, South Korea indicate that the performance of the proposed predictive method is almost as good as that of the oracle that foresees the future.

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Optimizing the level of service quality of a bike-sharing system

Cited by 171 publications

References 23 publications

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Spatial Cluster-Based Model for Static Rebalancing Bike Sharing Problem

Impacts of Smart Configuration in Pedelec-Sharing: Evidence from a Panel Survey in Madrid

Multi-Objective Predictive Taxi Dispatch via Network Flow Optimization

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