On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model

• Published in: Transportmetrica (Abingdon, Oxfordshire, UK) Oxfordshire, UK), 2024-09, Vol.20 (3)
• Main Authors: Ming Low, Victor Jian, Khoo, Hooi Ling, Khoo, Wooi Chen
• Format: Article
• Language: English
• Subjects: Automatic vehicle location; Autoregressive models; burr distribution; Burr mixture autoregressive model; bus travel time prediction; intermediate-to-long term prediction; Mixtures; Reliability; Scheduling; time series analysis; Transportation services; Travel time
• ISSN: 2324-9935; 2324-9943
• DOI: 10.1080/23249935.2023.2181023

Travel time is an essential indicator for trip planning, transportation service planning, and operation. This study aims to propose a novel Burr mixture autoregressive (BMAR) model for the intermediate-to-long term period of bus section travel time prediction, which is useful for bus service and schedule planning. The BMAR model exhibits greater flexibility, allowing it to effectively capture the multi-peak and non-peak, non-linear with heteroscedasticity characteristics of travel time. The model is trained and well-validated with 6-month bus section travel time data collected via the automatic vehicle location system. Results show that the BMAR model gives promising results in travel time point and interval prediction, especially for the higher degree of variability and irregular pattern of travel time observed on urban roads and highways. The reliability ratio index derived from the BMAR model could be used to measure the bus service reliability and aid in the bus scheduling of maintenance activities.