Chen-Fu Liao, Senior Systems Engineer, Civil Engineering
Metro Transit has deployed automatic data collection systems to collect bus location and passenger count information for their fleet operation and planning. Data collected from the system allow transit analysts and planners to identify problems with operations and route schedules and make appropriate adjustments. However, due to the magnitude of data amounts and limited human resources, these data sources are currently queried on an "as needed" basis, or used for quarterly bus schedule planning. Improved data mining, data fusion, and data visualization tools are necessary to extract the essential information needed for transit system performance analysis from the huge volume of available data. This project extends the researchers? previous development, funded by the University's Digital Technology Center, on a time-point-based bus route model. The researchers refined the time-point-based route model for better evaluation of bus route performance, and developed strategies to improve route performance through transit schedule optimization based on user costs and operation costs. The goal of this project was to develop transit planning support tools to evaluate the impact on different operational strategies such as performance of alternative schedules, mixes of fare media types, mixes of vehicle types (for example, standard vs. articulated, and low vs. high floor buses), signal priority, and stop consolidation.