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Outline
To address the challenge of accurate fine-granularity passenger flow prediction and decision support in urban rail transit, the study analyzes spatiotemporal passenger flow patterns and influencing factors using automatic fare collection (AFC) data, and employs a support vector regression (SVR) model whose parameters are optimized by a particle swarm optimization (PSO) algorithm. Using October 2021 data from Beijing Metro Line 4 at a 15 min aggregation interval, weather, time period, and date type were used as input variables; model accuracy was evaluated via mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), and relative accuracy, together with comparative model experiments. The results demonstrate that the proposed model effectively captures the spatiotemporal characteristics of rail transit passenger flow and achieves high prediction accuracy, with a mean squared error of 0.0164 and a coefficient of determination of 0.93. Additionally, a positive correlation is observed between the volume of rail transit passenger flow and error values of different model predictions. The study enhances understanding of the distribution and evolution of urban rail transit passenger flows and supports optimization of operations organization, scheduling strategies, and network planning.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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