An investigation of travel time pattern of T224 RapidKL bus using AVL data
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 ess...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UMP
2022
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34130/1/An%20investigation%20of%20travel%20time%20pattern%20of%20T224%20RapidKL%20bus.pdf http://umpir.ump.edu.my/id/eprint/34130/ https://doi.org/10.15282/jmmst.v6i1.7431 https://doi.org/10.15282/jmmst.v6i1.7431 |
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| Summary: | 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. |
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