Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Zhou, Wei; Wang, Wei; Zhao, De

doi:10.3390/s20123555

Cited by 22 publications

(10 citation statements)

References 51 publications

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“…In order to further improve the accuracy and efficiency of passenger flow forecasting, researchers combine other methods with ensemble tree model. A new model combining singular spectrum analysis with AdaBoost-weighted extreme learning machine is proposed [25]. Lin and Tian introduced a hybrid model combining Random Forest and LSTM to predict subway passenger flow [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost

Zou

Lin

et al. 2022

Wireless Communications and Mobile Computing

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Bus passenger flow prediction is a critical component of advanced transportation information system for public traffic management, control, and dispatch. With the development of artificial intelligence, many previous studies attempted to apply machine learning models to extract comprehensive correlations from transit networks to improve passenger flow prediction accuracy, given that the variety and volume of traffic data have been easily obtained. The passenger flow on a station is highly affected by various factors such as the previous time step, peak hours or nonpeak hours, and extracting the key features from the data is essential for a passenger flow prediction model. Although the neural networks, k -nearest neighbor, and some deep learning models have been adopted to mine the temporal correlations of the passenger flow data, the lack of interpretability of the influenced variables is still a big problem. Classical tree-based models can mine the correlations between variables and rank the importance of each variable. In this study, we presented a method to extract passenger flow of different routes on the station and implemented a XGBoost model to find the contributions of variables to the prediction of passenger flow. Comparing to benchmark models, the proposed model can reach state-of-the-art prediction accuracy and computational efficiency on the real-world dataset. Moreover, the XGBoost model can interpret the predicted results. It can be seen that period is the most important variable for the passenger flow prediction, and so the management of buses during peak hours should be improved.

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Section: Literature Reviewmentioning

confidence: 99%

Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost

Zou

Lin

et al. 2022

Wireless Communications and Mobile Computing

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“…In comparison, Luo et al divided them into three categories: linear approach, nonlinear approach, and hybrid approach [ 11 ]. Zhou et al divided them into four categories: parametric models, non-parametric models, hybrid models, and deep learning models [ 12 ]. Liu et al divided them into traditional classical algorithms, regressive models, machine learning-based models, and hybrid models [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al divided them into traditional classical algorithms, regressive models, machine learning-based models, and hybrid models [ 13 ]. In this research, based on the classification method of Zhou et al [ 12 ], passenger prediction models are separated into the following four categories: parametric models, non-parametric models, deep learning methods, and hybrid models.…”

Section: Related Workmentioning

confidence: 99%

“…The principle of time series decomposition methods is to decompose a complicated time series into multiple frequency components and predict them separately. Then, the predicted results are summed as the outcomes [ 12 ]. Commonly used time series decomposition methods include: empirical mode decomposition (EMD) [ 9 , 29 ], wavelet decomposition (WD) [ 30 , 31 ], singular spectrum analysis (SSA) [ 32 , 33 ], STL [ 34 , 35 ], etc.…”

Section: Related Workmentioning

confidence: 99%

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An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

Jiao

Huang

Song

et al. 2021

Sensors

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The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people’s travel and public transport companies’ management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction.

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“…Tourist flow is an important indicator of the development level of tourism industry, an important part of tourism planning by national or regional tourism authorities, an effective guarantee to improve the quality of tourism products, and an important basis for the development of tourism resources and the construction of reception facilities such as hotels. Accurate prediction of tourist flow is related to the successful operation of an international and regional tourism project, which will directly affect the scientific decision making of the tourism project and is an important part of urban tourism development planning [6,7].…”

Section: Introductionmentioning

confidence: 99%

Design of a Cultural Tourism Passenger Flow Prediction Model in the Yangtze River Delta Based on Regression Analysis

2021

Scientific Programming

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Cultural tourism has gained much attention in the last decade and has promoted the preservation of a variety of tangible and intangible assets of culture. In order to accurately predict the cultural tourism passenger flow in the Yangtze River Delta and improve its economic benefits, this paper designs the prediction model of cultural tourism passenger flow in the Yangtze River Delta based on regression analysis. Taking the competitiveness of passenger flow as the core, this paper selects 28 indexes from four aspects of cultural tourism brand resources, cultural tourism support and protection, and urban tourism market income to build the evaluation index system of influencing factors of passenger flow. The principal component analysis method is used to simplify many related factors into a few uncorrelated factors to eliminate the multicollinearity caused by too many dependent variables; on this basis, the principal component regression model is constructed, and the determination coefficient is used to test the model fitting. Taking 15 cultural tourism cities in the Yangtze River Delta as the research object, the results show that the designed model has a good fitting degree, and the average error is only 0.41%, which can meet the needs of the prediction of cultural tourism passenger flow in the Yangtze River Delta. After the application of the prediction model, the foreign exchange earning amount of each cultural tourism city can be increased by more than 12%. The study has revealed good results.

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Passenger Flow Forecasting in Metro Transfer Station Based on the Combination of Singular Spectrum Analysis and AdaBoost-Weighted Extreme Learning Machine

Cited by 22 publications

References 51 publications

Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost

Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost

An Improved STL-LSTM Model for Daily Bus Passenger Flow Prediction during the COVID-19 Pandemic

Design of a Cultural Tourism Passenger Flow Prediction Model in the Yangtze River Delta Based on Regression Analysis

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