A prediction model for traffic conditions based on an improved Markov chain - Zhou Mingsheng; Liu Shuyang (Dianzi Jishu Yingyong)
With the growth of urban traffic jam, how to recommend the fastest driving route for end users has become a research focus. The core problem of route recommending is how to forecast the traffic condition of the route in future, when the user will drive on this route section. The traffic condition is influenced by many factors, like road condition itself, passing time, weather conditions and habits of the driver. Because traffic condition changes very fast and complicated, it is difficult to accurately predict directly. This paper proposed a traffic condition prediction model based on an improved M-order Markov chain, which is more efficient. The model was tested with the actual traffic data in Beijing, and got a good result.