A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering

Dong, Jian; Chen, Bin; He, Lingnan; Ai, Chuan; Zhang, Fang; Guo, Danhuai; Qiu, Xiaogang

doi:10.3390/ijgi8080345

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…Li et al applied ordinary least squares (OLS) regression and geographically weighted regression (GWR) models to explore how the built environment and social-demographic characteristics infuence bike-sharing utilization [22]. Dong et al proposed DestiFlow based on points of interest (POIs) clustering to predict the demand for dockless bike-sharing [23]. Yan et al investigated the travel distance distributions of dockless bike-sharing near metro stations to provide the basis for the service area of dockless bike-sharing [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

Yang

Shao

Zhu

et al. 2023

Journal of Advanced Transportation

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To help related operators to allocate and dispatch the number of bike-sharing and provide good guidance for setting up electronic fences, this paper proposes a spatiotemporal graph convolution network prediction model (SGCNPM) with multiple factors to enhance the accuracy of predicting the demand for bike-sharing. First, we consider time, built environment, and weather. We use a multigraph convolution network (GCN) to model the built environment, utilize a long short-term memory (LSTM) network to extract temporal features, and utilize a fully connected network (FCN) to model weather influence. We construct SGCNPM which can effectively fuse GCN, LSTM, and FCN, thus creating a prediction method considering the influence of multiple factors. The results of the real case in Tianjin, China, show that the proposed model can perform well in improving prediction accuracy. Also, we analyze the influence of factors on model prediction results in different periods.

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Section: Literature Reviewmentioning

confidence: 99%

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

Yang

Shao

Zhu

et al. 2023

Journal of Advanced Transportation

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“…The number of individuals in the artificial society is

. Based on the work of Dong et al ( Dong et al, 2019 ; Edmunds et al, 1997 ; Mossong et al, 2008 ), the parameters are set as

, and the average degree of this network is 13.4. There are 172 subdistricts, and the final contact number in this city is 24,463,470.…”

Section: Appendixmentioning

confidence: 99%

Evaluating the mitigation strategies of COVID-19 by the application of the CO2 emission data through high-resolution agent-based computational experiments

Chen

Zhu

et al. 2022

Environmental Research

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The negative consequences, such as healthy and environmental issues, brought by rapid urbanization and interactive human activities result in increasing social uncertainties, unreliable predictions, and poor management decisions. For instance, the Coronavirus Disease (COVID-19) occurred in 2019 has been plaguing many countries. Aiming at controlling the spread of COVID-19, countries around the world have adopted various mitigation and suppression strategies. However, how to comprehensively eva luate different mitigation strategies remains unexplored. To this end, based on the Artificial societies, Computational experiments, Parallel execution (ACP) approach, we proposed a system model, which clarifies the process to collect the necessary data and conduct large-scale computational experiments to evaluate the effectiveness of different mitigation strategies. Specifically, we established an artificial society of Wuhan city through geo-environment modeling, population modeling, contact behavior modeling, disease spread modeling and mitigation strategy modeling. Moreover, we established an evaluation model in terms of the control effects and economic costs of the mitigation strategy. With respect to the control effects, it is directly reflected by indicators such as the cumulative number of diseases and deaths, while the relationship between mitigation strategies and economic costs is built based on the emission. Finally, large-scale simulation experiments are conducted to evaluate the mitigation strategies of six countries. The results reveal that the more strict mitigation strategies achieve better control effects and less economic costs.

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“…[Ke et al, 2017] and partitioned the urban into I × J grid areas to forecast short-term demand. [Dong et al, 2019] proposed the points of interest-based (POI-based) clustering to find stations. Nevertheless, all of the above approaches only considered the geographical information, and ignored the information of historical trip records, i.e., start and end locations.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery

Wang

Zhang

et al. 2021

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

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Influenced by the era of the sharing economy and mobile payment, Dockless Bike-Sharing System (Dockless BSS) is expanding in many major cities. The mobility of users constantly leads to supply and demand imbalance, which seriously affects the total profit and customer satisfaction. In this paper, we propose the Spatio-Temporal Mixed Integer Program (STMIP) with Flow-graphed Community Discovery (FCD) approach to rebalancing the system. Different from existing studies that ignore the route of trucks and adopt a centralized rebalancing, our approach considers the spatio-temporal information of trucks and discovers station communities for truck-based rebalancing. First, we propose the FCD algorithm to detect station communities. Significantly, rebalancing communities decomposes the centralized system into a distributed multi-communities system. Then, by considering the routing and velocity of trucks, we design the STMIP model with the objective of maximizing total profit, to find a repositioning policy for each station community. We design a simulator built on real-world data from DiDi Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, profit, and complexity compared with the state-of-the-art approach.

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A Spatio-Temporal Flow Model of Urban Dockless Shared Bikes Based on Points of Interest Clustering

Cited by 13 publications

References 38 publications

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

Short-Term Forecasting of Dockless Bike-Sharing Demand with the Built Environment and Weather

Evaluating the mitigation strategies of COVID-19 by the application of the CO2 emission data through high-resolution agent-based computational experiments

Dynamic Rebalancing Dockless Bike-Sharing System based on Station Community Discovery

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