Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

Huang, Xin; Wang, Yimin; Lin, Peiqun; Yu, Heng; Luo, Yue

doi:10.7307/ptt.v33i2.3561

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Due to the nonlinear relationship between the metro ridership and its influencing factors [ 32 ], as one indicator of relationship characteristics, the peak deviation is difficult to be explained using a simple linear statistical model. Nonlinear methods, e.g., artificial neural network, support vector machine, and the like, can be a better choice.…”

Section: Methodsmentioning

confidence: 99%

“…The LSSVM and its improved models [ 41 , 42 ] have been used for traffic flow prediction [ 43 , 44 ], real-time traffic information extraction [ 45 ], importance evaluation of nodes in complex networks [ 46 ], regional risk prediction [ 47 ], and so on. Specially, in the field of metro system, the LSSVM has been applied to predict the time change law of passenger flows [ 32 , 36 , 48 ]. Since the peak deviation for metro stations belongs to a more detailed scope of the station-level metro ridership studies and its related work is still in the initial stage, the LSSVM method is considered to be applied in this study.…”

Section: Methodsmentioning

confidence: 99%

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Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

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The station-level ridership during the peak hour is one of the key indicators for the design of station size and relevant facilities. However, with the operation of metro system, it cannot be ignored that, in many cities, the station peak and the city peak may not be simultaneously occurred. As the current ridership forecasting methods use the city peak as the point of reference, stations with wide differences of ridership in between would experience disorders due to serious underestimates of passenger demand during the actual peak. Accordingly, this study fully considers the phenomenon that the metro station peak is not identical to the city peak and focuses on the concept of the peak deviation coefficient (PDC), the ratio of the station peak ridership to the city peak ridership. It investigates how metro ridership determinants affects the PDC using the least square support vector machine (LSSVM) model. A land-use function complementarity index is employed as one of the independent variables, which is newly proposed in this study that describes the relationship of the commute land use around an individual station with that along the whole network. This method can help to resolve the ridership amplification indicator for a fine-grained station-level forecasting. The results for Xi’an metro indicate that the LSSVM is an effective method to scrutinize the nonlinear effects of e.g., land use attributes, on the temporal distribution features of the metro ridership. Compared to the ratio of commute land use measured for individual stations, the land-use function complementarity index can better explain and predict the severity of peak deviation phenomenon, controlling other independent variables in the model.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

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“…The current research on passenger flow prediction for rail transit mainly focuses on short-term passenger flow prediction for completed lines or stations [2][3][4], as well as the prediction and distribution of OD (Origin-Destination) travel routes for passengers [5][6][7][8]. When planning and constructing a new rail transit line or station, due to the lack of historical passenger flow data, the influence factors of passenger flow at this time become the indicators for the future long-term evaluation of the line or station.…”

Section: Introductionmentioning

confidence: 99%

Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network

Du,

Yang,

Yin

et al. 2024

Applied Sciences

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When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations.

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“…In recent years, many studies of the metro systems, from both mathematical and statistical perspectives, have emerged in the literature. Most of them focus on passenger forecasting [3][4][5][6] and human mobility patterns [2,[7][8][9][10][11]. For instance, ref [9] proposed a k-medoids clustering analysis approach to analyze subway stations in Nanjing (China) and compared the results obtained with previous studies; ref [10] developed a new method to mine metro commuting mobility patterns using massive smart card data in Chongqing (China); ref [11] examined changes in travel behavior based on yearly activity profiles using 3 years of longitudinal smart card data; ref [3] proposed a hybrid EMD-BPN forecasting approach that combines empirical mode decomposition (EMD) and back-propagation neural networks (BPN) to predict the short-term passenger flow in metro systems; and finally, ref [6] proposed a new approach called the seasonal and nonlinear least squares support vector machine (SN-LSSVM) to extract the periodicity and non linearity characteristics of passenger flow.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition

et al. 2022

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In recent years, a growing number of large, densely populated cities have emerged, which need urban traffic planning and therefore knowledge of mobility patterns. Knowledge of space-time distribution of passengers in cities is necessary for effective urban traffic planning and restructuring, especially in large cities. In this paper, the inbound ridership in the Barcelona metro is modelled into a three-way tensor so that each element contains the number of passenger in the ith station at the jth time on the kth day. Tucker3 decomposition is used to discover spatial clusters, temporal patterns, and the relationships between them. The results indicate that travel patterns differ between weekdays and weekends; in addition, rush and off-peak hours of each day have been identified, and a classification of stations has been obtained.

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Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

Cited by 6 publications

References 20 publications

Deviation of peak hours for metro stations based on least square support vector machine

Deviation of peak hours for metro stations based on least square support vector machine

Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network

An Analysis of Travel Patterns in Barcelona Metro Using Tucker3 Decomposition

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