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

Abstract

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.

KEYWORDS:

• Burr mixture autoregressive model
• bus travel time prediction
• time series analysis
• intermediate-to-long term prediction
• burr distribution

Acknowledgments

The authors would like to express their appreciation to Prasarana Malaysia Berhad for their assistance with the data collection. This research study is supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS), Project No.: FRGS/1/2018/TK08/UTAR/02/1. The authors confirm that the manuscript has been read and approved for submission by all the named authors.

Data availability statement

The research data generated is not publicly available due to confidential company data by Prasarana Malaysia Berhad.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Ministry of Higher Education Malaysia under the Fundamental Research Grant Scheme (FRGS): [Grant Number FRGS/1/2018/TK08/UTAR /02/1].