Traffic conditions often change substantially over a short time. To decrease the uncertainty caused by the changing traffic conditions, this study applied speed data as the model input and demonstrated a univariant approach for travel-time forecasting models with sequentially updated traffic data by combining grey and regression methods. The rolling grey model (RGM(1,1)), incorporating prediction grey model (IPGM(1,1)), and incorporating partial prediction grey model (IPPGM(1,1)) were applied to forecast the speed by using historical speed data. Based on the forecasted speed from the grey models, the regression method was used to develop a functional relationship between the actual historical travel time from vehicle detectors and actual historical bus travel times. Consequently, the forecasted bus travel time was obtained by applying the forecasted travel-time from the grey models as the independent variable in the regression relationship. To reflect the actual traffic situations adequately, the data collection period included weekdays and weekends. For most links and paths, the mean absolute percentage errors (MAPEs) of forecasted bus travel times were lower than 9.6%, indicating a high-quality performance. For most links, the forecasted travel times computed from speeds forecasted by IPGM(1,1) and IPPGM(1,1) were more accurate than those forecasted by RGM(1,1). Empirical studies have shown that the proposed procedure effectively combines traffic data from many detectors to form travel-time information for travelers and traffic managers. The forecasted travel time can be calculated by inserting real-time traffic data into the function as required.
|Translated title of the contribution||Sequential Update of Highway Travel-Time Forecasting Using a Grey Model|
|Number of pages||12|
|Journal||Journal of the Chinese Institute of Civil and Hydraulic Engineering|
|Publication status||Published - 2019 Jun 1|
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering