CAJ | 학술논문

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。
공교참간행정시간구유명현적시단분포특정，차공교차량시전형적시공과정대상，기운행구유상태전이성。위료준학예측공교참간행정시간，재응용마이과부련예측공교참간행정시간기출상제출기개진산법。통과대량공교GPS수거구조불동시단하구체선로참간행정시간적마이과부상태전이구진，병대참간행정시간진행상태추도，채용이동오차보상법대마이과부예측치진행동태수정，개진원유적마이과부예측산법。이엄주시BRT선로B1적실제운행수거대산법진행료험증，결과표명，이동오차보상개진산법우우기본마이과부산법급 BP모형，동시해개진산법환구유실현과정교간단。
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 .