Machine learning based method for forecasting short-term passenger flow in urban rail stations
Urban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit syst...
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| Format: | Article |
| Language: | English |
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Science Press (China Science Publishing & Media Ltd.)
2022-09-01
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| Series: | Shenzhen Daxue xuebao. Ligong ban |
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| Online Access: | https://journal.szu.edu.cn/en/#/digest?ArticleID=2465 |
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| _version_ | 1849770708636270592 |
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| author | HU Mingwei SHI Xiaolong WU Wenlin HE Guoqing |
| author_facet | HU Mingwei SHI Xiaolong WU Wenlin HE Guoqing |
| author_sort | HU Mingwei |
| collection | DOAJ |
| description | Urban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. Finally, we conduct the passenger flow forecasting experiments on the Hangzhou subway dataset based on the five prediction-models mentioned above. Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. The experimental results show that, the multi-feature machine learning model can effectively forecast urban rail transit short-term passenger flow which is difficult for the traditional time series model. However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations. |
| format | Article |
| id | doaj-art-79bb60f8cd564b658247bfa36ceec9dd |
| institution | DOAJ |
| issn | 1000-2618 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Science Press (China Science Publishing & Media Ltd.) |
| record_format | Article |
| series | Shenzhen Daxue xuebao. Ligong ban |
| spelling | doaj-art-79bb60f8cd564b658247bfa36ceec9dd2025-08-20T03:02:55ZengScience Press (China Science Publishing & Media Ltd.)Shenzhen Daxue xuebao. Ligong ban1000-26182022-09-0139559359910.3724/SP.J.1249.2022.055931000-2618（2022）05-0593-07Machine learning based method for forecasting short-term passenger flow in urban rail stationsHU MingweiSHI XiaolongWU WenlinHE GuoqingUrban rail transit features much strength, such as large capacity, safety and environment-friendliness, and it becomes a preferred choice for most passengers. It also plays a prominent part in solving urban traffic problems. In order to improve the operation efficiency of the urban rail transit system and achieve the goal of smart operations, this paper applies machine learning algorithms and completes the feature engineering of urban rail transit passenger flow data in terms of time, space and external factors. Based on passenger flow characteristics as collected, we build the short-term passenger flow forecast models, which include light gradient boosting machine (LightGBM) model, long short-term memory (LSTM) model, and LightGBM-LSTM fusion model. Besides, we construct the autoregressive integrated moving average (ARIMA) model and extreme gradient boosting (XGBoost) model for experiment comparison. Finally, we conduct the passenger flow forecasting experiments on the Hangzhou subway dataset based on the five prediction-models mentioned above. Then, five types of subway stations are selected (residential type, occupation type, residential-occupation type, business type, and transportation hubs type) and three accuracy evaluation indicators are chosen (mean absolute error, root mean square error and mean absolute percentage error) to evaluate the prediction accuracy of the five prediction models. The experimental results show that, the multi-feature machine learning model can effectively forecast urban rail transit short-term passenger flow which is difficult for the traditional time series model. However, the single model has poor adaptability to different types of subway stations. Compared with a single model, LightGBM-LSTM model equipped with merits of multiple models, fulfills a better function in forecasting and a better adaptability to different types of urban rail transit stations.https://journal.szu.edu.cn/en/#/digest?ArticleID=2465traffic and transportation engineeringurban rail transitshort-term passenger flow predictiondata processingfeature engineeringmachine learning |
| spellingShingle | HU Mingwei SHI Xiaolong WU Wenlin HE Guoqing Machine learning based method for forecasting short-term passenger flow in urban rail stations Shenzhen Daxue xuebao. Ligong ban traffic and transportation engineering urban rail transit short-term passenger flow prediction data processing feature engineering machine learning |
| title | Machine learning based method for forecasting short-term passenger flow in urban rail stations |
| title_full | Machine learning based method for forecasting short-term passenger flow in urban rail stations |
| title_fullStr | Machine learning based method for forecasting short-term passenger flow in urban rail stations |
| title_full_unstemmed | Machine learning based method for forecasting short-term passenger flow in urban rail stations |
| title_short | Machine learning based method for forecasting short-term passenger flow in urban rail stations |
| title_sort | machine learning based method for forecasting short term passenger flow in urban rail stations |
| topic | traffic and transportation engineering urban rail transit short-term passenger flow prediction data processing feature engineering machine learning |
| url | https://journal.szu.edu.cn/en/#/digest?ArticleID=2465 |
| work_keys_str_mv | AT humingwei machinelearningbasedmethodforforecastingshorttermpassengerflowinurbanrailstations AT shixiaolong machinelearningbasedmethodforforecastingshorttermpassengerflowinurbanrailstations AT wuwenlin machinelearningbasedmethodforforecastingshorttermpassengerflowinurbanrailstations AT heguoqing machinelearningbasedmethodforforecastingshorttermpassengerflowinurbanrailstations |