| Title |
Multiple Period Forecasting of Motorway Traffic Volumes by Using Big Historical Data |
| Authors |
장현호(Chang, Hyun-ho) ; 윤병조(Yoon, Byoung-jo) |
| DOI |
https://doi.org/10.12652/Ksce.2018.38.1.0073 |
| Keywords |
고속도로;다중시간대;예측;대용량자료;k-최근린 이웃 비모수회귀 Motorway;Multiple time-period;Forecasting;Big data;k-Nearest Neighbor-NonParametric Regression (KNN-NPR) |
| Abstract |
In motorway traffic flow control, the conventional way based on real-time response has been changed into advanced way based on proactive response. Future traffic conditions over multiple time intervals are crucial input data for advanced motorway traffic flow control. It is necessary to overcome the uncertainty of the future state in order for forecasting multiple-period traffic volumes, as the number of uncertainty concurrently increase when the forecasting horizon expands. In this vein, multi-interval forecasting of traffic volumes requires a viable approach to conquer future uncertainties successfully. In this paper, a forecasting model is proposed which effectively addresses the uncertainties of future state based on the behaviors of temporal evolution of traffic volume states that intrinsically exits in the big past data. The model selects the past states from the big past data based on the state evolution of current traffic volumes, and then the selected past states are employed for estimating future states. The model was also designed to be suitable for data management systems in practice. Test results demonstrated that the model can effectively overcome the uncertainties over multiple time periods and can generate very reliable predictions in term of prediction accuracy. Hence, it is indicated that the model can be mounted and utilized on advanced data management systems. |