Jan 20, 2021 · This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction ...
Based on the dynamic historical passenger flow data at an urban rail station, this study proposes an ML-fusion strategy to enhance prediction accuracy, ...
The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing ...
Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction, and the obtained ...
Abstract: Short-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling.
The experimental data is based on Chongqing Rail Transit AFC data, meteorological data and air quality data, and the multi- source data is selected to be ...
Jul 18, 2024 · [18] proposed a multi-graph data approach, processing spatiotemporal features using multiple graph convolutional networks, and then extracting ...
Short‐Term Passenger Flow Forecast of Rail Transit Station Based on ...
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A short-term forecasting model for metro passenger flow based on the light gradient boosting machine (LightGBM) model is established.
Based on the Beijing Line 10 passenger flow, this study forecasts the passenger flow for the next few days by using the ARIMA model to uncover the travel rules ...
Oct 22, 2024 · The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test ...