01042533

Component

https://doi.org/10.3141/2024-03

http://scholar.google.com...mith&publication_year=2007

The specification of time intervals for data collection is a fundamental determinant of the nature and utility of the resulting traffic condition data streams. In the context of short-term traffic flow forecasting, the establishment of the data collection time interval should play a key role in determining the corresponding appropriate forecasting approach. The data collection time interval provides the forecasting horizon for one-step-ahead forecasting. Nevertheless, the need for more rigorous understanding of the effects of data collection time interval specification within the context of short-term traffic flow forecasting is not well recognized. By contrast, it has been common practice in previous research to select the data collection time interval and forecasting approach without explicit consideration of time interval effects or systematic evaluation of available forecasting methods. A stochastic seasonal autoregressive integrated moving average plus generalized autoregressive conditional heteroscedasticity (SARIMA+GARCH) structure proposed in previous work holds promise in providing accurate point forecasts and reasonable forecasting confidence intervals. In this paper, a spectrum of data collection time intervals is tested with an online forecasting algorithm developed based on the SARIMA+GARCH structure to determine the applicable data collection time intervals for this structure. In this test, both the forecast accuracy and the validity of the forecasting confidence intervals are investigated. This work serves as an important step toward establishing a short-term traffic condition forecasting framework that identifies appropriate forecasting approaches for candidate data collection time intervals based on the functional requirements of specific applications.

01088321

English

pp 18-26

2007

Transportation Research Record: Journal of the Transportation Research Board

9780309104517

Print

Figures (6) ; References (13) ; Tables (2)

Accuracy; Algorithms; Data collection; Highway operations; Stochastic processes; Time intervals; Traffic data; Traffic flow; Traffic forecasting

ARMA (Auto-Regressive Moving Average model)

Generalized autoregressive conditional heteroscedasticity; Short term forecasts

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Feb 8 2007 6:42PM

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