A Functional Data Analysis Approach to Traffic Volume Forecasting
| Autor: | Wasim Irshad Kayani, Isaac Michael Wagner-Muns, Ivan G. Guardiola, V. A. Samaranayke |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
050210 logistics & transportation
Engineering Series (mathematics) Mean squared error business.industry Mechanical Engineering 05 social sciences Functional data analysis 02 engineering and technology Solid modeling computer.software_genre Computer Science Applications Data set 0502 economics and business Automotive Engineering Statistics Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Autoregressive integrated moving average Data mining Time series business computer |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems. 19:878-888 |
| ISSN: | 1558-0016 1524-9050 |
| DOI: | 10.1109/tits.2017.2706143 |
| Popis: | Traffic volume forecasts are used by many transportation analysis and management systems to better characterize and react to fluctuating traffic patterns. Most current forecasting methods do not take advantage of the underlying functional characteristics of the time series to make predictions. This paper presents a methodology that uses functional principal components analysis to create high-quality online traffic volume forecasts. The methodology is validated with a data set of 1755 days of 15 min aggregated traffic volume time series. Compared with 365 randomly selected days, the functional forecasts are found to outperform traditional seasonal autoregressive integrated moving average-based methods in both count deviation and root mean squared error. In addition, through the functional data analysis approach the full exploitation of the continuous nature of the data can be achieved. |
| Databáze: | OpenAIRE |
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