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The Chicago Transit Authority (CTA) has made a concerted effort to decrease crowding on public transportation that has been a cause for concern over the last few years. The CTA has been collecting information such as: the number of people riding a specific bus, the times at which more people board buses at a specific stop, the delay times on different routes, among other things. This data, after some processing, is used to understand and modify schedules and routes for the following quarter.
The buses pick up information regarding ridership and bus performance while executing a proposed schedule. The Planning Analytics department then calculates performance metrics such as Load, Flow, Bunching, and Crowding to determine the effectiveness of current CTA strategies.
While current best practices are data-focused, they are retrospective in nature. It is only after we implement the proposed schedule and wait for data to be collected (which takes several months not including data clean-up and aggregation) that we can assess the effectiveness of a certain schedule on de-crowding. We propose to turn scheduling into a more prospective exercise through statistical modeling and simulation (described below). This will allow the planning analytics department to be proactive and better understand the impact of certain scheduling decisions on bus crowding before implementation. Given the richness of the data available, we believe that even a simple statistical model and simulation approach will provide useful insight into bus de-crowding.