|Title||Unexpected Bus Operator Absence and Extraboard Scheduling – MBTA Case Study|
|Publication Type||Conference Paper|
|Year of Publication||2020|
|Authors||Qingyi Wang, Haris Koutsopoulos, Nigel Wilson|
|Conference Name||Transportation Research Board 99th Annual Meeting|
|Conference Location||Washington, D.C.|
Improving service reliability and reducing cost have always been prioritized by transit agencies and workforce planning is related to both performance metrics. An important workforce planning function is the management of the extraboard operators who cover for absent drivers. Despite its importance, extraboard planning is an understudied area, in part due to the lack of detailed and reliable data. In this paper, using data from HASTUS Daily at the MBTA, we investigate open work caused by operator absence and how it affects extraboard scheduling. Using k-means clustering, the representative time-of-day absence profiles are identified, and a logistic regression model is estimated to classify each day into the identified clusters and predict the time-of-day absence distribution by combining clustered profiles and classification results. The daily total absent hours are modelled by negative binomial regression. An integer optimization program is formulated to analyze the impact of wrong predictions on scheduling. Key findings are: 1) Time-of-day absence patterns follow regular service schedules well. 2) There is a large variation in the number of extraboard operators needed from week to week, resulting in inherent inefficiencies. 3) Time-of-day profile alignment error is around 26% on average. 4) The average error in predicting daily total absent hours using negative binomial regression is around 22% (19h) for weekdays 32% (21h) for weekends. 5) Optimal extraboard assignment is much more sensitive to the total number of hours than the time-of-day distribution of absences.