Research progress on shortterm passenger flow forecastmodel of urban rail transit(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2022年1期
- Page:
- 79-96
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Research progress on shortterm passenger flow forecastmodel of urban rail transit
- Author(s):
- LEI Bin; ZHANG Yuan; HAO Yarui; JING Lizhu
- 1. School of Civil Engineering, Xian University of Architecture and Technology, Xian 710055, Shaanxi, China;〖JP〗2. Key Laboratory for Special Area Highway Engineering of the Ministry of Education,Changan University, Xian 710064, Shaanxi, China
- Keywords:
- traffic engineering; rail transit; passenger flow prediction model; neural network; shortterm passenger flow; passenger flow analysis; combination model; time granularity
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to comprehensively understand the current research progress of shortterm passenger flow prediction of urban rail transit, the research status of recent years were summarized, the research focus of shortterm passenger flow prediction of urban rail transit were summarized, and its classification at home and abroad were discussed. The existing research results from three aspects, such as passenger flow analysis, forecasting method, prediction methods under different conditions and time granularity selection were mainly summarized. The results show that in terms of passenger flow analysis, most of the travel mode chain, cluster analysis and other methods are used to quantitatively analyze the characteristics of passenger flow, but lack of comprehensive qualitative and quantitative analysis. In terms of prediction methods, statistical, nonlinear and neural network prediction models are mainly used. With the development of prediction methods, the three kinds of analytical models for shortterm passenger flow prediction are more perfect and the prediction accuracy is improving day by day. However, the defects of the model still need to be further studied. In terms of prediction method and time granularity selection, the prediction method selection of normal situation and sudden large passenger flow, as well as the time granularity selection of shortterm passenger flow prediction in working days and nonworking days, peak and flat peak periods are mainly studied, which is not comprehensive enough. Future research can be carried out from three perspectives: passenger flow analysis, forecasting method, forecasting method under different conditions and time granularity selection. Firstly, through big data, the passenger flow of urban rail transit is analyzed by combining qualitative and quantitative methods. Secondly, the passenger flow prediction models with different characteristics are combined to solve the problems existing in the single model and improve the calculation speed of the combined model on the basis of ensuring the accuracy. Finally, the prediction method and time granularity of line passenger flow and network passenger flow under different conditions such as holidays and emergencies are reasonably selected. Through further research on how to combine the results of the above three aspects, the shortterm passenger flow of urban rail transit can be more accurately predicted and the basis for reasonable traffic organization can be provided. 5 tabs, 10 figs, 73 refs.
Last Update: 2022-04-06