Y.P. Huang, C. Chen, Z.C. Su, T.S. Chen, A. Sumalee, T.L. Pan, R.X. Zhong

    To maintain the stability and punctuality of bus systems, an accurate forecast of arrival time is essential to devise control strategies to prevent bus bunching especially under congested traffic conditions. Transit agencies provide travelers with accurate and reliable bus arrival times to downstream stations to improve transit service quality so as to attract more transit riders. Varieties of approaches have been dedicated to providing high prediction accuracy while the measure of the associated uncertainty is ignored. Noting that the quantification of uncertainty is vital for robust performance, this paper proposes data-driven approaches based on the Functional Data Analysis (FDA) and Bayesian Support Vector Regression (BSVR) for short-term bus travel time prediction while anticipating various uncertainties. To capture spatial–temporal dynamic traffic conditions along the route so as to increase the accuracy of the journey time prediction and to capture the skewness in journey time distribution, a probabilistic nested delay operator is adopted. Journey time reliability analysis is then conducted using the skewness of dynamic journey time distribution. An empirical study is carried out by fusing the bus transit date of No. 261 bus route and Floating Car Data (FCD) in Guangzhou. The proposed FDA and BSVR methods applied in conjunction with the probabilistic nested delay operator turn out to be highly competitive when performing forecasts under various traffic conditions. Comparative studies indicate that FDA provides more accurate prediction results and tends to anticipate uncertainties in journey time distribution more effectively.

DOI:https://doi.org/10.1016/j.asoc.2021.107663

Date:2021-11