Multi-objective optimal scheduling model for shared bikes based on spatiotemporal big data

Wang, Xiaoxia; Zheng, Shiqi; Wang, Luqi; Han, Shuang; Liu, Lin

doi:10.1016/j.jclepro.2023.138362

Cited by 6 publications

(2 citation statements)

References 40 publications

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“…Xv et al [ 28 ] and others combined chaos theory and an ant colony system to improve the ant colony algorithm to solve the process planning problem of the static dispatching of shared bike services. Wang et al [ 29 ] developed a new model for BRP using the NSGA-II algorithm. The model analyzed user travel patterns through order data, segmented operating areas into interconnected communities, and evaluated submarkets within these communities.…”

Section: Related Workmentioning

confidence: 99%

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

Ma,

Zheng,

Lin

et al. 2024

Biomimetics

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Public transportation scheduling aims to optimize the allocation of resources, enhance efficiency, and increase passenger satisfaction, all of which are crucial for building a sustainable urban transportation system. As a complement to public transportation, bike-sharing systems provide users with a solution for the last mile of travel, compensating for the lack of flexibility in public transportation and helping to improve its utilization rate. Due to the characteristics of shared bikes, including peak usage periods in the morning and evening and significant demand fluctuations across different areas, optimizing shared bike dispatch can better meet user needs, reduce vehicle vacancy rates, and increase operating revenue. To address this issue, this article proposes a comprehensive decision-making approach for spatiotemporal demand prediction and bike dispatch optimization. For demand prediction, we design a T-GCN (Temporal Graph Convolutional Network)-based bike demand prediction model. In terms of dispatch optimization, we consider factors such as dispatch capacity, distance restrictions, and dispatch costs, and design an optimization solution based on genetic algorithms. Finally, we validate the approach using shared bike operating data and show that the T-GCN can effectively predict the short-term demand for shared bikes. Meanwhile, the optimization model based on genetic algorithms provides a complete dispatch solution, verifying the model’s effectiveness. The shared bike dispatch approach proposed in this paper combines demand prediction with resource scheduling. This scheme can also be extended to other transportation scheduling problems with uncertain demand, such as store replenishment delivery and intercity inventory dispatch.

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Section: Related Workmentioning

confidence: 99%

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

Ma,

Zheng,

Lin

et al. 2024

Biomimetics

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“…W. Wu et al established a multi-period bicycle relocation model within an overall framework and derived a shortage formula coupling relocation decisions with mid-term demand [35]. X. Wang et al proposed a multi-objective optimization scheduling method combining massive spatiotemporal trajectory data of shared bicycles with user travel demands [36]. X. Wang et al studied the bike-sharing rebalancing problem based on variable demand, considering the impact of the number of bikes allocated by operators on user demand, aiming to maximize profits for PBS operators through route planning for transport vehicles and determining the target number of bicycles for redistribution at each station postoperation [37,38].…”

Section: Literature Reviewmentioning

confidence: 99%

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Liu,

Wu,

Tang

et al. 2024

Sustainability

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Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost.

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Investigating the influence of spatial characteristics on cycling volume: A multi-scale geographic weighted regression approach

Çiriş,

Akay,

Tümer

2024

Transportation Research Interdisciplinary Perspectives

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Multi-objective optimal scheduling model for shared bikes based on spatiotemporal big data

Cited by 6 publications

References 40 publications

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

A City Shared Bike Dispatch Approach Based on Temporal Graph Convolutional Network and Genetic Algorithm

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Investigating the influence of spatial characteristics on cycling volume: A multi-scale geographic weighted regression approach

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