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

نویسندگان

چکیده

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term flow needs to consider many factors, the results of existing models for subway forecasting are often unsatisfactory. Along this line, we propose parallel architecture, called seasonal nonlinear least squares support vector machine (SN-LSSVM), extract periodicity nonlinearity characteristics flow. Various models, including auto-regressive integrated moving average, long memory network, machine, employed evaluating performance proposed architecture. Moreover, first applied method Tiyu Xilu station which is most crowded Guangzhou metro. The indicate that model effectively make all-weather year-round predictions, thus contributing management station.

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ژورنال

عنوان ژورنال: Promet-traffic & Transportation

سال: 2021

ISSN: ['1848-4069', '0353-5320']

DOI: https://doi.org/10.7307/ptt.v33i2.3561