How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods cannot solve the problem effectively for ignoring the traffic delay jitter. In this paper,a three-stage mixed model is proposed for bus arrival time prediction. The first stage is pattern training. In this stage,the traffic delay jitter patterns(TDJP)are mined by K nearest neighbor and K-means in the historical traffic time data. The second stage is the single-step prediction,which is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage,as the influence of historical law is increasing in long distance prediction,we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi-step prediction. The experimental results show that the proposed single-step prediction model performs better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models. How to predict the bus arrival time accurately is a crucial problem to be solved in Internet of Vehicle. Existed methods can not solve the problem effectively for ignoring the traffic delay jitter. In this paper, a three-stage mixed model is proposed for bus arrival time The first stage is pattern training. In this stage, the traffic delay jitter patterns (TDJP) are mined by K nearest neighbor and K-means in the historical traffic time data. which is is based on real-time adjusted Kalman filter with a modification of historical TDJP. In the third stage, as the influence of historical law is increasing in long distance prediction, we combine the single-step prediction dynamically with Markov historical transfer model to conduct the multi -step prediction. The experimental results show that the proposed single-step prediction model perform better in accuracy and efficiency than short-term traffic flow prediction and dynamic Kalman filter. The multi-step prediction provides a higher level of veracity and reliability in travel time forecasting than short-term traffic flow and historical traffic pattern prediction models.