The travel times of public transit systems that operate on mixed use right-of-ways are often dictated by the delays experienced at signalized intersections. When these delays become large and/or highly variable, transit quality degrades and agency operating costs increase. A number of transit priority measures can be applied, including transit signal priority or queue jump lanes. However, it is necessary that a process of prioritizing intersections for priority treatment be conducted so as to ensure the greatest return on investment is achieved. This thesis proposes and demonstrates a methodology to determine the distribution of stopped delays experienced by transit vehicles at signalized intersections using archived AVL (automated vehicle location) and APC (automated passenger counting) data. This methodology is calibrated and validated using queue length and bus unscheduled stopped delay data measured at a field site. Results show the proposed methodology is of sufficient accuracy to be used in practice for prioritizing signalized intersections for priority treatment. On the condition that a sample of the transit vehicle fleet is equipped with an AVL/APC system, the proposed methodology can be automatically implemented using the archived AVL/APC data and therefore avoid the need to conduct dedicated data collection surveys. The proposed methodology can provide estimates of (1) the maximum extent of the queue; and (2) measures of the distribution of stopped delays experienced by transit vehicles (e.g. mean, standard deviation, 90th percentile, etc.) caused by the downstream traffic signal. These measures can be produced separately for different analysis periods (e.g. different times of the day; days of the week; and time of the year) and can be compiled separately for different transit routes. These outputs can then be used to identify and prioritize signalized intersections as candidates for transit signal priority measures. The proposed method is suitable for application to most transit AVL/APC databases and is demonstrated using data from Grand River Transit, the public transit service provider in the Region of Waterloo, Ontario Canada.

The travel times of public transit systems that operate on mixed use right-of-ways are often dictated by the delays experienced at signalized intersections. When these delays become large and/or highly variable, transit quality degrades and agency operating costs increase. A number of transit priority measures can be applied, including transit signal priority or queue jump lanes. However, it is necessary that a process of prioritizing intersections for priority treatment be conducted so as to ensure the greatest return on investment is achieved. This thesis proposes and demonstrates a methodology to determine the distribution of stopped delays experienced by transit vehicles at signalized intersections using archived AVL (automated vehicle location) and APC (automated passenger counting) data. This methodology is calibrated and validated using queue length and bus unscheduled stopped delay data measured at a field site. Results show the proposed methodology is of sufficient accuracy to be used in practice for prioritizing signalized intersections for priority treatment. On the condition that a sample of the transit vehicle fleet is equipped with an AVL/APC system, the proposed methodology can be automatically implemented using the archived AVL/APC data and therefore avoid the need to conduct dedicated data collection surveys. The proposed methodology can provide estimates of (1) the maximum extent of the queue; and (2) measures of the distribution of stopped delays experienced by transit vehicles (e.g. mean, standard deviation, 90th percentile, etc.) caused by the downstream traffic signal. These measures can be produced separately for different analysis periods (e.g. different times of the day; days of the week; and time of the year) and can be compiled separately for different transit routes. These outputs can then be used to identify and prioritize signalized intersections as candidates for transit signal priority measures. The proposed method is suitable for application to most transit AVL/APC databases and is demonstrated using data from Grand River Transit, the public transit service provider in the Region of Waterloo, Ontario Canada.

Transit agencies include buffer time in their schedules to maintain stable headways and avoid bus bunching. In this work, a real-time holding mechanism is proposed to dispatch buses on a loop-shaped route, solely based on operating conditions in real-time. Holds are applied at the terminal station to minimize the expected variance of bus headways at departure. The bus-dispatching problem is formulated as a stochastic decision process. The optimality equations are derived and structural properties of the optimal policy are inferred by backward induction. The exact optimal holding policy is then found in closed form, as a function of the expected travel time of buses currently running. A simulation assuming stochastic operating conditions and unstable headway dynamics is performed to assess the expected average waiting time of passengers at stations. The proposed control strategy is found to provide lower passenger waiting time and better resiliency than methods recommended in the literature and used in practice.