Application of Artificial Neural Network to the Prediction of Pollutant Concentration in Road Tunnels

In this study, it was purposed to develop the new method for the prediction of pollutant concentration in road tunnels. The new method was the use of artificial neural network with the back-propagation algorithm which can model the non-linear system of tunnel environment. This network system was separated into two parts as the visibility and the CO concentration. For this study, data was collected from two highway road tunnels on Yeongdong Expressway. The tunnels have two lanes with one-way direction and adopt the longitudinal ventilation system. The actually measured data from the tunnels was used to develop the neural network system for the prediction of pollutant concentration. The output results from the newly developed neural network system were analysed and compared with the calculated values by PIARC method. Results showed that the prediction accuracy by the neural network system was approximately five times better than the one by PIARC method. In addition, the system predicted much more accurately at the situation where the drivers have to be stayed for a while in tunnels caused by the low velocity of vehicles.

인공신경망을 이용한 도로터널 오염물질 농도 예측

본 연구에서는 비서형 모델에 적용 가능한 역전파 알고리즘을 이용하여 도로터널에서 발생하는 오염물질을 예측하기 위한 인공신경망을 개발하였다. 도로 터널에서 중요시되는 오염인자는 CO농도와 가시도이므로, 인공신경망의 구성을 각각의 독립적인 네트워크로서 구성하였다. 사용한 입력데이터는 영동고속도로에 위치한 종류식 환기 방식을 채택한 일방향 2차선 도로 터널 2개소에서 실측한 데이터를 사용하였다. 예측치와 실측치를 비교할 때 인공신경망의 학습도는 약 95%의 정확성을 보이는 것으로 나타났다. 분석결과 개발된 인공신경망에 의한 결과는 PIARC 방식에 의한 계산치 보다 약 5배 정도의 정확성을 보였다. 특히 주행속도가 낮을 경우 더 높은 정확도를 나타낼 것으로 기대 되었다.