To improve the self-adaptability of Kalman filtering estimation model,a real-time route travel time algorithm is proposed in this paper. First,the relevancy between route travel time and influence factors time series will be analyzed using principal component analysis method,and the main factors are chosen as the independent variables of a regression model. Secondly,the state equations of Kalman filtering are built up. Finally,the estimation values can be calculated using a set of Kalman filtering recursion formulas. We apply the algorithm for travel time estimation using real data collected in Guangzhou and compare it to a conventional Kalman filter-ing model,the results show that the algorithm is applicable and performs well for route travel time estimation. To improve the self-adaptability of Kalman filtering estimation model, a real-time route travel time algorithm is proposed in this paper. First, the relevancy between route travel time and influence factors time series will be analyzed using principal component analysis method, and the Main factors are chosen as the independent variables of a regression model. Finally, the estimation equations can be calculated using a set of Kalman filtering recursion formulas. We apply the algorithm for travel time estimation using real data collected in Guangzhou and compare it to conventional Kalman filter-ing model, the results show that the algorithm is applicable and performs well for route travel time estimation.