2014
DOI: 10.1016/j.tra.2014.04.019
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Extraboard team sizing: An analysis of short unscheduled absences among regular transit drivers

Abstract: Research Highlights1-This article focuses on regular bus drivers' short-duration unscheduled absences. 2-It analyzes absenteeism data at the aggregate level of garage-day-period. 3-A multilevel regression model is generated to investigate regular drivers' absence 4-Sensitivity analyses are conducted to evaluate the benefits of using modeling. 5-It provides transit planners with a methodology to support extraboard planning practice.

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Cited by 6 publications
(4 citation statements)
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“…It should be noted that to ensure robustness of the generated data, 20 stop times per stop location, time period, bus type, route number, and direction was used as a threshold for a group, which is comparable to previous studies (24,29). Accordingly, the groups that have 20 stop times or fewer were deleted from the data set.…”
Section: Methodsmentioning
confidence: 99%
“…It should be noted that to ensure robustness of the generated data, 20 stop times per stop location, time period, bus type, route number, and direction was used as a threshold for a group, which is comparable to previous studies (24,29). Accordingly, the groups that have 20 stop times or fewer were deleted from the data set.…”
Section: Methodsmentioning
confidence: 99%
“…Other factors such as seasonality, month, day of the week, and timing of special events might also affect driver absences ( 51 ). Diab ( 52 ) proposed a method to predict the short-term absences among regular drivers. Long and Perry ( 53 ) conducted a survey in California and found that most transit agencies were using subjective methods based on personal experience to determine the extraboard driver size given driver absence.…”
Section: Related Workmentioning
confidence: 99%
“…By contrast, the analysis of differences between units remained relatively superficial. The subsidiaries were compared, without making distinctions between their size, their environment (urban or suburban), or composition of the workforce, it is well known that, for example, age is a determinant of absenteeism (Diab et al 2014). Such comparisons, 'all else held unequal', reveal an inaccurate exploitation of such statistics: despite active strategies to prevent absenteeism, some visited subsidiaries that were structurally more prone to absenteeism due to the composition of their workforce were 'ranked badly' by management.…”
Section: Close Monitoring Of Absences and Accidents But Poor Benchmarkingmentioning
confidence: 99%
“…Absenteeism still impacts the performance of transport companies. Extraboard teams -used to fill in for absent operators if no extra drivers are available -overtime paid to present drivers and missed trips are huge sources of costs, despite the improvement of planning practices (Diab et al 2014). In addition to their costs, these schemes cannot compensate for all the disruptions generated by absences; therefore, firms have a priori strong incentives to prevent absenteeism at work.…”
mentioning
confidence: 99%