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URBAN FREEWAY TRAFFIC FLOW PREDICTION: APPLICATION OF SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND EXPONENTIAL SMOOTHING MODELS
| 题名： | URBAN FREEWAY TRAFFIC FLOW PREDICTION: APPLICATION OF SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE AND EXPONENTIAL SMOOTHING MODELS |
| 作者： | Williams-BM; Durvasula-PK; Brown-DE |
| 关键词： | Freeways; Urban areas; Traffic flow; Forecasting; Travel patterns; Seasons; Time series analysis; Urban freeways; Seasonal traffic pattern; Autoregressive integrated moving average models; Exponential smoothing models |
| 摘要： | The application of seasonal time series models to the single-interval traffic flow forecasting problem for urban freeways is addressed. Seasonal time series approaches have not been used in previous forecasting research. However, time series of traffic flow data are characterized by definite periodic cycles. Seasonal autoregressive integrated moving average (ARIMA) and Winters exponential smoothing models were developed and tested on data sets belonging to two sites: Telegraph Road and the Woodrow Wilson Bridge on the inner and outer loops of the Capital Beltway in northern Virginia. Data were 15-min flow rates and were the same as used in prior forecasting research by B. Smith. Direct comparisons with the Smith report findings were made and it was found that ARIMA (2, 0, 1)(0, 1, 1) at a lag of 96 (daily period) and ARIMA (1, 0, 1)(0, 1, 1) at a lag of 96 (daily period) were the best-fit models for the Telegraph Road and Wilson Bridge sites, respectively. Best-fit Winters exponential smoothing models were also developed for each site. The single-step forecasting results indicate that seasonal ARIMA models outperform the nearest-neighbor, neural network, and historical average models as reported by Smith. |
| 总页数： | Transportation Research Record.1998. pp 132-141 (FIGS: 5 Fig. TABS: 4 Tab. REFS: 11 Ref.) |
| 报告类型： | 科技报告 |
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