基于马尔科夫链的公交站间行程时间预测算法

公交站间行程时间具有明显的时段分布特征,且公交车辆是典型的时空过程对象,其运行具有状态转移性。为了准确预测公交站间行程时间,在应用马尔科夫链预测公交站间行程时间基础上提出其改进算法。通过大量公交GPS数据构造不同时段下具体线路站间行程时间的马尔科夫状态转移矩阵,并对站间行程时间进行状态推导,采用移动误差补偿法对马尔科夫预测值进行动态修正,改进原有的马尔科夫预测算法。以广州市BRT线路B1的实际运行数据对算法进行了验证,结果表明,移动误差补偿改进算法优于基本马尔科夫算法及 BP模型,同时该改进算法还具有实现过程较简单。 

Algorithm for Predicting Bus Travel Time between Stops Based on Markov Chain

Bus travel time between stops has obvious period distribution characteristics .The buses ,with the char-acteristic of state transition ,have a typical space-time process .In order to predict the bus travel time between stops in the future period of time accurately ,an improved algorithm based on the basic Markov chain is proposed .The algorithm can be divided into two steps .The first step is to set up the first-order Markov transition matrix for a specific bus route dur-ing different period of time with the bus GPS data and then to predict the bus travel time between stops based on the ma-trix .The second step is to improve the basic Markov chain algorithm by leading up the compensation of moving error . The algorithm was tested and validated by using the data taken from the bus route B1 of Guangzhou BRT .The test result shows that the improved algorithm with the compensation of moving error provides better predicting accuracy than both basic Markov chain algorithm and the BP neural network algorithm and that the improved algorithm is simple in imple-mentation .