CAJ | 학술논문

为了提高行程时间预测的可靠性，构建了自回归综合移动平均与广义自回归条件异方差性（ ARIMA-GARCH）模型进行城市主干道行程时间动态置信区间预测，其中ARIMA模型作为GARCH模型的均值方程用于捕获行程时间均值，GARCH模型用于捕获行程时间条件方差。运用昆山市交通监测系统中采集的实际交通流数据进行验证和评估。结果表明，相较于传统的ARIMA模型，提出的方法虽然不能显著提升行程时间均值的预测性能，但是在行程时间波动性预测方面具有较大的优势。该方法可捕获行程时间异方差，从而能够预测出比ARIMA模型预测的固定置信区间更能反映行程时间观测值波动性的动态置信区间。
위료제고행정시간예측적가고성，구건료자회귀종합이동평균여엄의자회귀조건이방차성（ ARIMA-GARCH）모형진행성시주간도행정시간동태치신구간예측，기중ARIMA모형작위GARCH모형적균치방정용우포획행정시간균치，GARCH모형용우포획행정시간조건방차。운용곤산시교통감측계통중채집적실제교통류수거진행험증화평고。결과표명，상교우전통적ARIMA모형，제출적방법수연불능현저제승행정시간균치적예측성능，단시재행정시간파동성예측방면구유교대적우세。해방법가포획행정시간이방차，종이능구예측출비ARIMA모형예측적고정치신구간경능반영행정시간관측치파동성적동태치신구간。
To improve the forecasting reliability of travel time the time-varying confidence interval of travel time on arterials is forecasted using an autoregressive integrated moving average and generalized autoregressive conditional heteroskedasticity ARIMA-GARCH model.In which the ARIMA model is used as the mean equation of the GARCH model to model the travel time levels and the GARCH model is used to model the conditional variances of travel time.The proposed method is validated and evaluated using actual traffic flow data collected from the traffic monitoring system of Kunshan city. The evaluation results show that compared with the conventional ARIMA model the proposed model cannot significantly improve the forecasting performance of travel time levels but has advantage in travel time volatility forecasting. The proposed model can well capture the travel time heteroskedasticity and forecast the time-varying confidence intervals of travel time which can better reflect the volatility of observed travel times than the fixed confidence interval provided by the ARIMA model.