The implementation of automatic transit data collection via Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems provides an opportunity to create large, detailed datasets of transit operations. These datasets are valuable because they provide an opportunity to evaluate and optimize transit operations using methods that were previously infeasible and without the need for expensive manual data collection. This thesis develops a methodology to utilize data collected by typical AVL/APC system installations in order to (a) develop advanced performance measures to quantify schedule adherence and (b) automatically determine the causes of poor schedule adherence. The methodology addresses the difficulty that many small to medium sized transit agencies have in utilizing the data being collected by proposing a methodology that can be automated, thereby reducing resource and expertise requirements and allowing the data to be more effectively utilized. The ultimate output of the proposed methodology includes the following: 1. A ranked list of routes by direction (for a given time period) that identifies routes with the poorest schedule adherence performance. 2. Performance measures within any given route, direction, and time period that identify which timepoints are contributing most to poor schedule adherence. 3. Statistics indicating identified causes of poor schedule adherence at individual timepoints. 4. A visualization aid to be used in conjunction with the cause statistics generated in Step 3 in order to develop an effective strategy for improving schedule adherence issues. With this information, transit agencies will be able to act proactively to improve their transit system, rather than wait until they discover problems on their own or hear complaints from passengers and drivers. The methodology is tested and demonstrated through application to AVL/APC system data from Grand River Transit, a public transit agency serving Waterloo Region in Ontario, Canada.

The implementation of automatic transit data collection via Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) systems provides an opportunity to create large, detailed datasets of transit operations. These datasets are valuable because they provide an opportunity to evaluate and optimize transit operations using methods that were previously infeasible and without the need for expensive manual data collection. This thesis develops a methodology to utilize data collected by typical AVL/APC system installations in order to (a) develop advanced performance measures to quantify schedule adherence and (b) automatically determine the causes of poor schedule adherence. The methodology addresses the difficulty that many small to medium sized transit agencies have in utilizing the data being collected by proposing a methodology that can be automated, thereby reducing resource and expertise requirements and allowing the data to be more effectively utilized. The ultimate output of the proposed methodology includes the following: 1. A ranked list of routes by direction (for a given time period) that identifies routes with the poorest schedule adherence performance. 2. Performance measures within any given route, direction, and time period that identify which timepoints are contributing most to poor schedule adherence. 3. Statistics indicating identified causes of poor schedule adherence at individual timepoints. 4. A visualization aid to be used in conjunction with the cause statistics generated in Step 3 in order to develop an effective strategy for improving schedule adherence issues. With this information, transit agencies will be able to act proactively to improve their transit system, rather than wait until they discover problems on their own or hear complaints from passengers and drivers. The methodology is tested and demonstrated through application to AVL/APC system data from Grand River Transit, a public transit agency serving Waterloo Region in Ontario, Canada.