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

Kumar, B.A., Vanajakshi, L. and Subramanian, S.C., 2017. Bus travel time prediction using a time-space discretization approach. Transportation Research Part C: Emerging Technologies, 79, pp.308-332.

Furth, P.G., Hemily, B., Muller, T. and Strathman, J.G., 2003. Uses of archived AVL-APC data to improve transit performance and management: Review and potential. TCRP Web Document, 23.

Khadhir, A., Anil Kumar, B. and Vanajakshi, L.D., 2021. Analysis of global positioning system based bus travel time data and its use for advanced public transportation system applications. Journal of Intelligent Transportation Systems, 25(1), pp.58-76.

Taguchi, G., 1995. Quality engineering (Taguchi methods) for the development of electronic circuit technology. IEEE Transactions on Reliability, 44(2), pp.225-229.

Ramlie, F., Jamaludin, K.R., Dolah, R. and Muhamad, W.Z.A.W., 2016. Optimal feature selection of taguchi character recognition in the mahalanobis-taguchi system using bees algorithm. Global Journal of Pure and Applied Mathematics, 12(3), pp.2651-2671.

Negishi, S., Morimoto, Y., Takayama, S. and Ishigame, A., 2017. Daily peak load forecasting by Taguchi's T method. Electrical Engineering in Japan, 201(1), pp.57-65.

Matsui, S. and Nagata, Y., 2019. Proposal for a univariate time series analysis method based on Taguchi’s T-method. Total Quality Science, 5(1), pp.1-10.

Downloads

Published

31-03-2022

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

Issue

Section

Articles

Most read articles by the same author(s)

Similar Articles

1 2 3 4 5 6 7 8 9 > >> 

You may also start an advanced similarity search for this article.