Shortterm passenger flow forecast of urban rail transitbased on different time granularities(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2020年3期
- Page:
- 75-83
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Shortterm passenger flow forecast of urban rail transitbased on different time granularities
- Author(s):
- MA Chaoqun; LI Peikun; ZHU Caihua; LU Wenbo; TIAN Tian
- （College of Transportation Engineering, Changan University, Xian 710064, Shaanxi, China）
- Keywords:
- traffic engineering; urban rail transit; shortterm passenger flow forecast; ARIMA model; time granularity; similarity measure
- PACS:
- -
- DOI:
- -
- Abstract:
- In order to explore the relationships between forecast accuracy and time granularity of passenger flow in urban rail transit, based on the inbound passenger flow data of automatic fare collection (AFC) system for 50 consecutive days in Xian Metro, the effective time of metro operation was divided into different time granularities, such as 5 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours and 1 day. The similarity of passenger flow time series under different time granularities was measured by Pearson coefficient method. The autoregressive integrated moving average (ARIMA) model was used to fit and predict the total network entry under different time granularities. Taking Pearson coefficient equal to 0.95 as the threshold of time granularity selection for shortterm passenger flow forecast, ARIMA model was finally used for shortterm passenger flow forecast under three time granularities of 30 minutes, 60 minutes and 1 day. The forecast results of ARIMA model were compared with those of autoregressive (AR) model, support vector regression (SVR) and BP neural network model. The results show that the time granularity correlation coefficient change presents a single peak form, and the average relative error of ARIMA model for 30 minutes, 60 minutes and 1 day time granularities are 4.12%, 3.54% and 4.97% respectively. The forecast results of the four models show that the average forecast accuracy of the ARIMA model is the highest. The four methods have the same change tendency for the forecast error at different time granularities, that is, the average forecast error decreases according to the three time granularities of 1 day, 30 minutes and 60 minutes. Therefore, the extreme selection of time granularity will not directly improve the effect of shortterm passenger flow forecast, the optimized time series model has high accuracy in the analysis and shortterm prediction of passenger flow data of Xian Metro stations，the research results can provide technical support for the optimization of the operation organization of urban rail transit. 7 tabs, 10 figs, 24 refs.
Last Update: 2020-06-03