CAJ | 학술논문

公交周转时间是制定公交行车作业计划的关键参数，公交周转时间的精确预测也是提高公交服务水平的重要保证。文章基于公交运行的动态随机性，运用数据挖掘有序样本聚类算法，研究了对公交周转时间影响较大的动态因素，分析了全天时段每个时间窗内周转时间的分布特征，建立了基于 BP 神经网络的公交周转时间预测模型；通过实例分析验证了模型的有效性和精度。结果表明：公交周转时间的分布特征主要受时段、天气状况、节假日或大型活动、星期、季节月份等动态因素的影响，通过划分时间窗可知，同一时间窗内的公交转时间近似呈正态分布；BP 神经网络预测模型可有效地揭示公交周转时间和各动态因素之间的非线性关系，通过实例分析，预测公交周转时间的平均绝对百分比误差为5．69％，具有较高的预测精度。
공교주전시간시제정공교행차작업계화적관건삼수，공교주전시간적정학예측야시제고공교복무수평적중요보증。문장기우공교운행적동태수궤성，운용수거알굴유서양본취류산법，연구료대공교주전시간영향교대적동태인소，분석료전천시단매개시간창내주전시간적분포특정，건립료기우 BP 신경망락적공교주전시간예측모형；통과실례분석험증료모형적유효성화정도。결과표명：공교주전시간적분포특정주요수시단、천기상황、절가일혹대형활동、성기、계절월빈등동태인소적영향，통과화분시간창가지，동일시간창내적공교전시간근사정정태분포；BP 신경망락예측모형가유효지게시공교주전시간화각동태인소지간적비선성관계，통과실례분석，예측공교주전시간적평균절대백분비오차위5．69％，구유교고적예측정도。
Bus turnaround time is the key parameter of bus driving operation plan,and the accurate prediction of the bus turnaround time is also important guarantee to improve the service level of public transportation.Based on the bus running dynamic randomness and by using the ordered sample clustering data mining algorithm,the paper analyzes the distribution features of the bus turnaround time in each time window to get the main dynamic factors,establishes the bus turnaround time prediction model,and through example analysis verifies the effectiveness and accuracy of the model. The results show using GPS data can obtain the accurate bus running bus turnaround time,and by partitioning time window,the bus turnaround time approximately normally distributed at the same time window.Through the establishment of the BP neural network prediction model can effectively reveal the bus turnaround time and the nonlinear relationship between the dynamic factors,and the example analysis results show that the forecasting bus turnaround time mean absolute percentage error is 5.69%,which has higher prediction accuracy.