An Investigation of Travel Time Pattern of T224 RapidKL Bus Using AVL Data

Authors

  • Siti Nur Athirah Ahmad Latfi Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia
  • Nur Najmiyah Jaafar Faculty of Manufacturing and Mechatronics Engineering Technology, Universiti Malaysia Pahang, 26600 Pahang, Malaysia

DOI:

https://doi.org/10.15282/jmmst.v6i1.7431

Keywords:

predictive model, time series data, Taguchi's T Method, Travel Time Pattern

Abstract

Transit dependability is a measure of the quality of service provided by public transportation networks. The trip time distribution may represent the nature and pattern of travel time variability, and it is required in the reliability analysis. Predicting transit bus travel times along routes is essential in bus design and operation, particularly in metropolitan regions. Bus riders are more likely to trust a transportation system if journey times can be accurately anticipated within a given margin of error. Many studies have been conducted to better understand travel time distribution, however there are very few studies on heterogeneous traffic in emerging countries such as Malaysia. In this work, the Taguchi T- technique and linear regression scales were used to evaluate the journey time distribution of public bus route T224 utilising AVL data. To increase the accuracy of applications, a prediction mechanism should be developed. Finally, a formulation was constructed to test the influence of side roads on prediction accuracy, and it was discovered that the additional need in terms of location-based data had no discernible effect on prediction accuracy. This clearly proved that the suggested method based on vehicle tracking data is adequate for the contemplated use of bus trip time prediction.

References

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Published

2022-03-31

How to Cite

Ahmad Latfi, S. N. A. ., & Jaafar, N. N. . (2022). An Investigation of Travel Time Pattern of T224 RapidKL Bus Using AVL Data. Journal of Modern Manufacturing Systems and Technology, 6(1), 66–72. https://doi.org/10.15282/jmmst.v6i1.7431

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Section

Articles