Effect of Automatic Vehicle Location on Schedule Adherence for Mass Transit Administration Bus System
Young-Jae Lee, Kyung Soo Chon, David L. Hill, Nimish Desai
Abstract
Young-Jae Lee, Kyung Soo Chon, David L. Hill, Nimish Desai
Abstract
The effect of an automatic vehicle location (AVL) system on schedule adherence and operator behavior is studied. During two sets of 4 weeks each, one set with and one set without AVL intervention, data were collected from the Mass Transit Administration (MTA) bus system in Baltimore, Maryland. Although AVL systems have many benefits, the focus is on improvements in schedule adherence and operator behavior for off-schedule bus arrival. Although ahead-of-schedule operation generates longer waiting time, adjustments can be made if the operator is aware and willing to keep on schedule. Thus, arrival status at the next time points (TPs), after early arrival at the main TPs, and link travel time between those two TPs were carefully analyzed. As a result, an improvement in schedule adherence with AVL was demonstrated with a 99 percent confidence level. However, with MTA’s current AVL intervention, the schedule adherence and link travel time after early arrival at the previous TP did not improve significantly. Those results are explained by the link travel time distribution curves, which have two peaks by two groups of operator behavior—one enjoying congestionfree traffic and the other paying attention to schedule adherence. Without strong AVL intervention, operator behavior could not be relied on to improve schedule adherence effectively.
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The effect of an automatic vehicle location (AVL) system on schedule adherence and operator behavior is studied. During two sets of 4 weeks each, one set with and one set without AVL intervention, data were collected from the Mass Transit Administration (MTA) bus system in Baltimore, Maryland. Although AVL systems have many benefits, the focus is on improvements in schedule adherence and operator behavior for off-schedule bus arrival. Although ahead-of-schedule operation generates longer waiting time, adjustments can be made if the operator is aware and willing to keep on schedule. Thus, arrival status at the next time points (TPs), after early arrival at the main TPs, and link travel time between those two TPs were carefully analyzed. As a result, an improvement in schedule adherence with AVL was demonstrated with a 99 percent confidence level. However, with MTA’s current AVL intervention, the schedule adherence and link travel time after early arrival at the previous TP did not improve significantly. Those results are explained by the link travel time distribution curves, which have two peaks by two groups of operator behavior—one enjoying congestionfree traffic and the other paying attention to schedule adherence. Without strong AVL intervention, operator behavior could not be relied on to improve schedule adherence effectively.
Key concepts: Schedule, Operator (biology), Transit (satellite), Automatic vehicle location, Transport engineering, Arrival time, Computer science, Travel time