Passenger Flow Prediction in Bus Transportation System using ARIMA Models with Big Data
| Autor: | Yinna Ye, Feng Xue, Li Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
010308 nuclear & particles physics
business.industry Computer science media_common.quotation_subject Big data Real-time computing Volume (computing) 02 engineering and technology Payment 01 natural sciences Flow (mathematics) 0103 physical sciences 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Autoregressive–moving-average model Autoregressive integrated moving average Smart card Time series business media_common |
| Zdroj: | CyberC |
| DOI: | 10.1109/cyberc.2019.00081 |
| Popis: | The objective of this research is to predict the daily bus passenger flow volume in a given bus line and compare the prediction performances in the case using whole weekday data against the case using weekday-only data. Based on the real data collected from the bus IC card payment devices in Jiaozuo City, we firstly obtained time series plots on the daily passenger volume and then proposed ARIMA models to do the prediction. The results show that the the operation of including weekend data is necessary to improve the prediction performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|