การทำนายระยะเวลาการเดินทางบนทางพิเศษโดยวิธีซัพพอร์ตเวกเตอร์แมชชีน - ธีศิษฐ์ ก้อนแก้ว (จุฬาลงกรณ์มหาวิทยาลัย, 2011)
Providing accuracy travel time prediction is important part for route planning applications. This research proposes a short-term prediction method travel time up to 60 minutes by Support Vector Machine (SVM) which inputs are speed, flow, speed and flow before current time and time of day. The result performance is compared to historical approach and artificial neural network (ANN). MAPE (Mean Abosilute Percentage Error) and RMSE (Root Mean Square Error) are used as error measurement. Two traffic scenarios are considered. Firstly, Real Traffic scenario of which data is obtained from installed cameras along Chalerm Mahanakorn Expressway in Bangkok, Thailand.Secondly, Simulated Traffic scenario of which data is obtained from simulation software. According to Real Traffic scenario, the prediction result of SVM outperforms other approaches. Its MAPE result is 7.67% and 9.76% less than ANN and historical approach respectively. In Simulated Traffic scenario, SVM prediction result is 1 - 2% different from ANN. Finally, SVM shows the advantage of prediction potential over ANN. The prediction error over time of SVM is less than that of ANN. Moreover, traffic variant is affect to the prediction performance. According to the experiments, the performance of prediction during the highest traffic variant period of day is worse than other periods.