Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model
Travel time variability (TTV) is the key indicator used in assessing the service quality of bus transit system. This study explores the most appropriate model to describe the day-to-day TTV of bus section. By investigating a 7-month travel time data for 10 bus routes in Klang Valley, Malaysia, this...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2022-09-01
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| Series: | International Journal of Transportation Science and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2046043021000599 |
| _version_ | 1797760551200227328 |
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| author | Victor Jian Ming Low Hooi Ling Khoo Wooi Chen Khoo |
| author_facet | Victor Jian Ming Low Hooi Ling Khoo Wooi Chen Khoo |
| author_sort | Victor Jian Ming Low |
| collection | DOAJ |
| description | Travel time variability (TTV) is the key indicator used in assessing the service quality of bus transit system. This study explores the most appropriate model to describe the day-to-day TTV of bus section. By investigating a 7-month travel time data for 10 bus routes in Klang Valley, Malaysia, this study demonstrates that Burr distribution is the most promising model in describing bus TTV. Bus TTV is found to be sensitive to both temporal and spatial effect. This means that TTV service varies for weekdays and weekends (temporal). Also, it differs for the five operating environments (spatial) investigated in this study. The Burr regression analysis conducted in the second part of this study further confirmed that bus section length and traffic signal density are the major contributing factors to bus TTV. However, both factors have varying levels of impact under different spatiotemporal effect. For example, in the suburban and residential areas, these factors cause higher TTV on weekends but lesser during weekdays, while a vice versa impact is observed in the Central Business District. This distinguishes from earlier studies which purely assumed normality in the regression analysis while not emphasizing the importance of spatiotemporal factors on TTV. Thus, this study serves as an analysis tool that could be used in the planning of bus routes and schedules under varying bus operating environments and operation times. |
| first_indexed | 2024-03-12T19:01:09Z |
| format | Article |
| id | doaj.art-4c79f11e8a104d13b419833f3e4f3c21 |
| institution | Directory Open Access Journal |
| issn | 2046-0430 |
| language | English |
| last_indexed | 2024-03-12T19:01:09Z |
| publishDate | 2022-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | International Journal of Transportation Science and Technology |
| spelling | doaj.art-4c79f11e8a104d13b419833f3e4f3c212023-08-02T06:39:03ZengKeAi Communications Co., Ltd.International Journal of Transportation Science and Technology2046-04302022-09-01113563577Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution modelVictor Jian Ming Low0Hooi Ling Khoo1Wooi Chen Khoo2Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, MalaysiaLee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, 43000 Kajang, Selangor, Malaysia; Corresponding author.Dept. of Applied Statistics, Sunway University, Sunway City, 47500 Selangor Darul Ehsan, MalaysiaTravel time variability (TTV) is the key indicator used in assessing the service quality of bus transit system. This study explores the most appropriate model to describe the day-to-day TTV of bus section. By investigating a 7-month travel time data for 10 bus routes in Klang Valley, Malaysia, this study demonstrates that Burr distribution is the most promising model in describing bus TTV. Bus TTV is found to be sensitive to both temporal and spatial effect. This means that TTV service varies for weekdays and weekends (temporal). Also, it differs for the five operating environments (spatial) investigated in this study. The Burr regression analysis conducted in the second part of this study further confirmed that bus section length and traffic signal density are the major contributing factors to bus TTV. However, both factors have varying levels of impact under different spatiotemporal effect. For example, in the suburban and residential areas, these factors cause higher TTV on weekends but lesser during weekdays, while a vice versa impact is observed in the Central Business District. This distinguishes from earlier studies which purely assumed normality in the regression analysis while not emphasizing the importance of spatiotemporal factors on TTV. Thus, this study serves as an analysis tool that could be used in the planning of bus routes and schedules under varying bus operating environments and operation times.http://www.sciencedirect.com/science/article/pii/S2046043021000599Travel time variabilityBus transit systemBurr regressionBus travel timeReliability |
| spellingShingle | Victor Jian Ming Low Hooi Ling Khoo Wooi Chen Khoo Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model International Journal of Transportation Science and Technology Travel time variability Bus transit system Burr regression Bus travel time Reliability |
| title | Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model |
| title_full | Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model |
| title_fullStr | Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model |
| title_full_unstemmed | Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model |
| title_short | Quantifying bus travel time variability and identifying spatial and temporal factors using Burr distribution model |
| title_sort | quantifying bus travel time variability and identifying spatial and temporal factors using burr distribution model |
| topic | Travel time variability Bus transit system Burr regression Bus travel time Reliability |
| url | http://www.sciencedirect.com/science/article/pii/S2046043021000599 |
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