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

Zeng, Lu; Li, Zinuo; Yang, Jie; Xu, Xinyue

doi:10.3390/ijerph192416433

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…The analysis of both long-distance transport and urban bus transport was the focus of the research in [8][9][10][11]. A closer look was taken at national rail transport and its impact on customer service offerings [9,[12][13][14]. There was also the challenge of obtaining data on the evolution of the disease and associated information on which steps to take against the disease [15][16].…”

Section: Methodsmentioning

confidence: 99%

Impact of the COVID Crisis on Public Passenger Transport in Slovakia and Urban Transport in Žilina on a Selected Line

Majerčák

Kurenkov

2023

LOGI – Scientific Journal on Transport and Logistics

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The effects of the COVID-19-related crisis in society can be seen not only in long-distance passenger transport but also in the urban transportation services in the territory. During the pandemic, the travelling public tended to choose private motor vehicles, bicycles, or walking as their mode of transport to work or other activities. This fact was also the subject of our research based on the analysis of data on public passenger rail transport in the individual regions of the Slovak Republic and based on data analysis performed, also in urban transport in the city of Žilina. Mobility in the city was investigated and analysed based on detailed monitoring of the use of the means of transport on a specific trolleybus line No. 67, where a hybrid trolleybus Škoda 26TrA Solaris was deployed. It was found out that the impact of the pandemic and the adoption of restrictive measures to prevent the spread of the disease significantly affected this trolleybus line, where, depending on the extent of the measures taken, the occupancy of the trolleybus fluctuated by up to 60 % in specific months compared to the standard period of use of this means of transport.

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Section: Methodsmentioning

confidence: 99%

Impact of the COVID Crisis on Public Passenger Transport in Slovakia and Urban Transport in Žilina on a Selected Line

Majerčák

Kurenkov

2023

LOGI – Scientific Journal on Transport and Logistics

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“…However, traditional parametric and nonparametric models are impeded by protracted training durations and low responsiveness, rendering them ill-suited for managing large-scale data in practical applications [9]. Moreover, the extant literature on short-time passenger flow prediction predominantly concentrates on optimizing model structures and training algorithms, often overlooking the impact of multimedia data noise on prediction accuracy [10].…”

Section: Introductionmentioning

confidence: 99%

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

Cited by 9 publications

References 42 publications

Impact of the COVID Crisis on Public Passenger Transport in Slovakia and Urban Transport in Žilina on a Selected Line

Impact of the COVID Crisis on Public Passenger Transport in Slovakia and Urban Transport in Žilina on a Selected Line

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

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

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