Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations

Yu, Liang; Feng, Tao; Li, Tie; Cheng, Long

doi:10.1007/s40864-022-00183-w

Cited by 12 publications

(7 citation statements)

References 32 publications

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“…the optimization results of the IAOA, the optimal k and α are [15,244] in CYSS, [19,206] in NNERS, [19] in NNRS, and [3,18] in JHSS.…”

Section: Vmd Parameter Optimization Resultsmentioning

confidence: 99%

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A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit

Wu,

He,

et al. 2023

Urban Rail Transit

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Short-term passenger flow prediction (STPFP) helps ease traffic congestion and optimize the allocation of rail transit resources. However, the nonlinear and nonstationary nature of passenger flow time series challenges STPFP. To address this issue, a hybrid model based on time series decomposition and reinforcement learning ensemble strategies is proposed. Firstly, the improved arithmetic optimization algorithm is constructed by adding sine chaotic mapping, a new dynamic boundary strategy, and adaptive T distribution mutations for optimizing variational mode decomposition (VMD) parameters. Then, the original passenger flow data containing nonlinear and nonstationary irregular changes of noise is decomposed into several intrinsic mode functions (IMFs) by using the optimized VMD technology, which reduces the time-varying complexity of passenger flow time series and improves predictability. Meanwhile, the IMFs are divided into different frequency series by fluctuation-based dispersion entropy, and diverse models are utilized to predict different frequency series. Finally, to avoid the cumulative error caused by the direct superposition of each IMF’s prediction result, reinforcement learning is adopted to ensemble the multiple models to acquire the multistep passenger flow prediction result. Experiments on four subway station passenger flow datasets proved that the prediction performance of the proposed method was better than all benchmark models. The excellent prediction effect of the proposed model has important guiding significance for evaluating the operation status of urban rail transit systems and improving the level of passenger service.

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“…the optimization results of the IAOA, the optimal k and α are [15,244] in CYSS, [19,206] in NNERS, [19] in NNRS, and [3,18] in JHSS.…”

Section: Vmd Parameter Optimization Resultsmentioning

confidence: 99%

“…Therefore, in earlier studies, time series models were often utilized for STPFP. These models include linear models [2,3], gray prediction [4], history average [5], and Kalman filter [6]. The premise of these methods is that the time series remains stationary.…”

Section: Introductionmentioning

confidence: 99%

A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit

Wu,

He,

et al. 2023

Urban Rail Transit

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“…Regression models are also helpful in quantitative variable forecasting [36]. These models can provide valuable insights into variables like time, weather, and local events that affect the demand for vehicles.…”

Section: Models In Shared Mobility Systemsmentioning

confidence: 99%

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

Kayisu,

Kambale,

Benarbia

et al. 2024

Symmetry

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In the near future, the incorporation of shared electric automated and connected mobility (SEACM) technologies will significantly transform the landscape of transportation into a sustainable and efficient mobility ecosystem. However, these technological advances raise complex scientific challenges. Problems related to safety, energy efficiency, and route optimization in dynamic urban environments are major issues to be resolved. In addition, the unavailability of realistic and various data of such systems makes their deployment, design, and performance evaluation very challenging. As a result, to avoid the constraints of real data collection, using generated artificial datasets is crucial for simulation to test and validate algorithms and models under various scenarios. These artificial datasets are used for the training of ML (Machine Learning) models, allowing researchers and operators to evaluate performance and predict system behavior under various conditions. To generate artificial datasets, numerous elements such as user behavior, vehicle dynamics, charging infrastructure, and environmental conditions must be considered. In all these elements, symmetry is a core concern; in some cases, asymmetry is more realistic; however, in others, reaching/maintaining as much symmetry as possible is a core requirement. This review paper provides a comprehensive literature survey of the most relevant techniques generating synthetic datasets in the literature, with a particular focus on the shared electric automated and connected mobility context. Furthermore, this paper also investigates central issues of these complex and dynamic systems regarding how artificial datasets could be used in the training of ML models to address the repositioning problem. Hereby, symmetry is undoubtedly a crucial consideration for ML models. In the case of datasets, it is imperative that they accurately emulate the symmetry or asymmetry observed in real-world scenarios to be effectively represented by the generated datasets. Then, this paper investigates the current challenges and limitations of synthetic datasets, such as the reliability of simulations to the real world, and the validation of generative models. Additionally, it explores how ML-based algorithms can be used to optimize vehicle routing, charging infrastructure usage, demand forecasting, and other important operational elements. In conclusion, this paper outlines a series of interesting new research avenues concerning the generation of artificial data for SEACM systems.

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“…Based on this, a hybrid prediction model was proposed, effectively addressing the imbalance between supply and demand of shared bicycles. This study has also driven the development of transportation systems [22]. The studies conducted by these scholars highlight that current research fails to consider multiple factors when examining residents' travel characteristics.…”

Section: Introductionmentioning

confidence: 99%

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

Cheng,

Yue

2024

Int J Comput Intell Syst

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In the era of big data, traditional analysis methods are insufficient to meet the growing demand for green and low-carbon travel orders in shared bicycle systems. To address this issue, a new order demand forecasting model, named the “convolutional neural network (CNN)”—“long short-term memory (LSTM)” model (CNN-LSTM), is proposed by integrating CNN and LSTM techniques. The research further validates the spatiotemporal prediction performance of this model. The experimental results demonstrate that LSTM exhibits favorable prediction performance in terms of time feature analysis, as evidenced by the overlapping of the true value (TV) and predicted value (PV) curves. Notably, LSTM achieves an impressively low mean squared error (MSE) value of 0.0063, which is significantly lower compared to CNN (0.0082) and XGBoost (0.0074). Upon incorporating date and weather characteristics, the predictive performance improves significantly, achieving an outstanding MSE value of 0.0043. However, when it comes to spatial feature analysis, the LSTM algorithm alone proves inadequate, obtaining a MSE value of 0.0084. Thus, by employing the CNN-LSTM combination model, a lower MSE value of 0.0066 is achieved, outperforming the comparison algorithms. Overall, the CNN-LSTM model exhibits strong predictive capabilities regarding the temporal and spatial requirements of shared bicycles. This model plays a key role in accurately forecasting order demands, facilitating urban transportation planning and management, as well as guiding the planning and location of non-motorized vehicle stops.

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Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations

Cited by 12 publications

References 32 publications

A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit

A Time Series Decomposition and Reinforcement Learning Ensemble Method for Short-Term Passenger Flow Prediction in Urban Rail Transit

A Comprehensive Literature Review on Artificial Dataset Generation for Repositioning Challenges in Shared Electric Automated and Connected Mobility

Construction of a Green and Low-Carbon Travel Order Prediction Model Based on Shared Bicycle Big Data

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