Daily Passenger Volume Prediction in the Bus Transportation System using ARIMAX Model with Big Data
| Autor: | Yinna Ye, Yingchen Su |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Series (mathematics) business.industry Computer science media_common.quotation_subject 05 social sciences Big data Real-time computing Volume (computing) 02 engineering and technology Solid modeling Payment Data modeling 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Smart card Time series business media_common |
| Zdroj: | CyberC |
| DOI: | 10.1109/cyberc49757.2020.00055 |
| Popis: | Based on the real data collected from the bus IC card payment database, firstly a time series of daily passenger volumes in a given bus line was obtained and then two kinds of time series models, ARMA with quadratic trend and ARIMAX, were proposed to do the prediction. The experiment results show that both models can make prediction effectively and especially ARIMAX model, which takes daily temperatures in to consideration, performs better in terms of prediction accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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