An improved general variable neighborhood search for a static bike-sharing rebalancing problem considering the depot inventory

Ren, Yaping; Meng, Leilei; Zhao, Fu; Zhang, Chaoyong; Guo, Hongfei; Tian, Ying; Wen, Tong; Sutherland, John W.

doi:10.1016/j.eswa.2020.113752

Cited by 21 publications

(4 citation statements)

References 26 publications

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“…The interaction between pedestrians and e-bicycles in bicycle lanes or pedestrian areas can be a crucial challenge in the process of implementing e-bicycles owing to their high speed. Moreover, the operational costs of a bicycle and e-bicycle sharing system can significantly fluctuate depending on various factors, including population density, the size of the service area, and the location and capacity of stations [44], all of which are added challenging and determining factors in the daily use of cycling innovations.…”

Section: Discussionmentioning

confidence: 99%

Assessing Cycling Accessibility in Urban Areas through the Implementation of a New Cycling Scheme

Chondrogianni,

Stephanedes,

Fatourou

2023

Sustainability

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Cycling’s integration into the intricate facets of urban design, together with walking and public transportation, offers an effective solution to the mobility issues plaguing urban spaces, and is critical to the sustainability of modern cities. In this context, in this research urban cyclists’ needs and preferences are analyzed through questionnaires, and bicycle accessibility to urban areas is assessed using multicriteria analysis. The public’s familiarity with the integration of novel mobility solutions (e.g., e-bicycles) that support accessibility and inclusiveness is tested and analyzed by recording cyclists trajectories on bicycle routes. The European hub of Patras was selected as the case study for a pilot scheme in this analysis. Similar to many medium-sized European cities, several mobility obstacles, including urban topography, hinder bicycle accessibility in the city, especially between the Modern and Old City areas. The research findings indicate that, addressing these obstacles, electric bicycles can substantially increase bicycle accessibility in the city center. The public usage of electric bicycles is encouraged in the pilot study, and the results indicate that it can increase accessibility to urban areas while reducing restrictions related to age, physical condition, and disabilities. Providing citizens access to e-bicycles can increase the number of daily bicycle users, leading to positive impact in urban cohesion, resilience, and sustainability.

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Section: Discussionmentioning

confidence: 99%

Assessing Cycling Accessibility in Urban Areas through the Implementation of a New Cycling Scheme

Chondrogianni,

Stephanedes,

Fatourou

2023

Sustainability

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“…A static strategy relocates bikes at routine times or when both traffic and demand are low, e.g., during night time, whereas a dynamic one works once a station is going to be unavailable, i.e., full or empty station. Obviously, the static repositioning is hardly content with frequent rental and return demands; however, it can be modeled as optimization problems whose objective is to route trunks of finite capacity to meet station targets while minimizing the route length [ 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems

Lin

2022

Sensors

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Transport-sharing systems are eco-friendly and the most promising services in smart urban environments, where the booming Internet of things (IoT) technologies play an important role in the smart infrastructure. Due to the imbalanced bike distribution, bikes and stalls in the docking stations could be unavailable when needed, leading to bad customer experiences. We develop a dynamic repositioning strategy for the management of bikes in this paper, which supports dispatchers to keep stations in service. Two open datasets are examined, and the exploratory data analysis presents that there is a significant difference of travel patterns between working and non-working days, where the former has an excess demand at rush hours and the latter is usually at a low demand. To evaluate the effect when the demand outstrips a station’s capacity, we propose a non-linear scaling technique to transform demand patterns and perform the clustering analysis for each of five categories obtained from the sophisticated analysis of the dataset. Our repositioning strategy is developed according to the transformed demands. Compared with the previous work, numerical simulations reveal that our strategy has a better performance for high-demand stations, and thus can substantially reduce the repositioning cost, which brings benefit to bike-sharing operators for managing the city bike system.

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“…In contrast with other modes of transportation, the operation and planning of public BSS in metropolitan areas are hindered by unique challenges. First, the demand is unevenly distributed both geographically and temporally, with stations in residence and office areas being either frequently full or empty during peak hours, thus preventing local check-ins and check-outs and consequently hampering user trust [12,27]. Second, bike sharing demand is affected by significant externalities, including daily variations on the users' profile and endeavors impacting their preference towards a given mode of mobility [17].…”

Section: Introductionmentioning

confidence: 99%

“…Second, bike sharing demand is affected by significant externalities, including daily variations on the users' profile and endeavors impacting their preference towards a given mode of mobility [17]. Balancing initiatives, including the ongoing bike relocation or dynamic user incentives for taking specific routes along certain time windows can be placed to counter-act this effect [27]. Yet, their efficacy is largely dependent on the ability to model and forecast the demand of BSS stations.…”

Section: Introductionmentioning

confidence: 99%

Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context

Sardinha¹,

Finamore²,

Henriques³

2021

Preprint

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Bike sharing demand is increasing in large cities worldwide. The proper functioning of bike-sharing systems is, nevertheless, dependent on a balanced geographical distribution of bicycles throughout a day. In this context, understanding the spatiotemporal distribution of check-ins and check-outs is key for station balancing and bike relocation initiatives. Still, recent contributions from deep learning and distance-based predictors show limited success on forecasting bike sharing demand. This consistent observation is hypothesized to be driven by: i) the strong dependence between demand and the meteorological and situational context of stations; and ii) the absence of spatial awareness as most predictors are unable to model the effects of high-low station load on nearby stations.This work proposes a comprehensive set of new principles to incorporate both historical and prospective sources of spatial, meteorological, situational and calendrical context in predictive models of station demand. To this end, a new recurrent neural network layering composed by serial long-short term memory (LSTM) components is proposed with two major contributions: i) the feeding of multivariate time series masks produced from historical context data at the input layer, and ii) the time-dependent regularization of the forecasted time series using prospective context data. This work further assesses the impact of incorporating different sources of context, showing the relevance of the proposed principles for the community even though not all improvements from the contextaware predictors yield statistical significance.

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An improved general variable neighborhood search for a static bike-sharing rebalancing problem considering the depot inventory

Cited by 21 publications

References 26 publications

Assessing Cycling Accessibility in Urban Areas through the Implementation of a New Cycling Scheme

Assessing Cycling Accessibility in Urban Areas through the Implementation of a New Cycling Scheme

A Demand-Centric Repositioning Strategy for Bike-Sharing Systems

Context-aware demand prediction in bike sharing systems: incorporating spatial, meteorological and calendrical context

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