CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

The railway is one of the most energy-efficient modes of transport, helping to enhance the environment and public health in cities and agglomerations. In this paper, the authors raise the issue of the construction of an underground railway route in Wrocław (Poland) to allow the organization of the suburban rail system in the agglomeration. There are many concepts for the construction of this route, but so far none has been realized. Therefore, it is important to design the route properly. Here, five options for this tunnel are considered and evaluated. To make such an evaluation, the authors construct a modified ant colony optimization algorithm (ACO). The “classic” algorithm considers the determination of the shortest route. The modification of the algorithm will allow a more accurate analysis of the issue, taking into account more parameters than just the length of the route. These are the location of traffic generators in the city center, the number of inhabitants neighboring the stations, and the number of tram or bus lines integrated with the railway. The presented method and exemplary case study should allow for the evaluation, introduction, or development of the city railway.

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“…The problem of accessibility to cities is analyzed in [ 11 ] and in [ 12 ]. Here, short-term passenger flow prediction is important [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section

Korzeń

Kruszyna

2023

IJERPH

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“…Zeng et al [16] predicted the passenger flow of Yangji Station of Guangzhou Metro based on the inbound and outbound AFC data from 1 July to 28 July 2019.…”

Section: Introductionmentioning

confidence: 99%

Short-time prediction model for cross-section passenger flow of trains in urban transit using GCN-AM-BiLSTM

Zuo

Zhao

Liu

et al. 2023

Preprint

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With the proliferation of passenger flow under the condition of network condition, the imbalanced temporal and spatial distribution of passenger flow occurs frequently, which brings enormous challenges to the operation of urban rail systems. Effectively predicting the short-time passenger flow of trains is an important prerequisite to optimize the transportation strategies, respond to the fluctuation of passenger flow and meet the real-time demand. Consequently, the GCN-AM-BiLSTM prediction model is proposed to extract the complex temporal and spatial characteristics of passenger flow. Firstly, the urban rail transit temporal diagram and spatial adjacency matrix are constructed to capture the global spatial characteristics using GCN. Secondly, the attention mechanism is introduced into the BiLSTM to construct the AM-BiLSTM module to extract and assign the importance of temporal characteristics from both the forward and backward dimensions. Finally, the characteristics are integrated based on the fusion network. The performance verification and analysis based on Chengdu Metro in China show that compared with several baseline models, our model achieves the best values in terms of MAE, RMSE and MAPE. The prediction efficiency can fully meet the timeliness requirements of the field, which has good application prospects.

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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion

Wan,

Cheng,

Yang

2024

IET Circuits, Devices & Systems

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Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.

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CEEMDAN-IPSO-LSTM: A Novel Model for Short-Term Passenger Flow Prediction in Urban Rail Transit Systems

Cited by 7 publications

References 42 publications

Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section

Modified Ant Colony Optimization as a Means for Evaluating the Variants of the City Railway Underground Section

Short-time prediction model for cross-section passenger flow of trains in urban transit using GCN-AM-BiLSTM

Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion

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