一种基于改进的马尔可夫链的交通状况预测模型

A prediction model for traffic conditions based on an improved Markov chain

城市交通日益拥堵的今天,为用户推荐最快行驶路线成为一个研究热点。行驶路线推荐的核心问题是对路线将来某段时间(途径这段线路时)交通状况的预测。交通状况受到路线本身状况、行驶时间、天气状况、驾驶员习惯等多种因素影响,其变化快、变化方式复杂,难以准确预测。对多阶马尔可夫链模型进行了改进,提高了运算效率和响应速度,建立一种高效的交通状况预测模型,经北京市实际交通数据的检验,得到了比较好的预测效果。

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.