A prediction method of missing vehicle position information based on least square support vector machine

Du, Peng; Ma, Xiaoqi; Wang, Zhuanping; Mo, Yuanfu; Peng, Peng

doi:10.1016/j.susoc.2021.03.003

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…But this method runs slowly when dealing with a large sample [ 38 ]. The improved algorithm, the least-square support vector machine (LSSVM), can fit the data in segments, which allows to reduce the calculation dimensions for data fitting, and save the calculation time and increase the fitting accuracy simultaneously as a result [ 39 , 40 ]. 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.…”

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 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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