Using Data to Optimize Chicago's Public Transit

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The Chicago Transit Authority (CTA) manages and operates the public transportation system in Chicago. The transit system, composed of 8 train lines and 140 bus routes, is the second largest in the nation, and provides an average of 1.69 million rides on an average weekday (based on the 2011 statistics). Despite increasing ridership, CTA continues to face operational issues, due to the ongoing financial crisis and budget cuts. 

In light of these financial challenges, CTA recently implemented a series of changes to existing services. The recommended course of action came from a team of researchers from Northwestern University, who analyzed both historical data and current data (hour-to-hour and day-to-day ridership). Buses and trains were equipped with GPS devices and computers to communicate data related to ridership, e.g. total ridership, early/late arrivals, number of passengers that board/leaves at each stop, etc. 

Using this data, the researchers were able to point out service patterns and identify inefficiencies and overflows in the system; for instance, the trains are "…regularly packed with 90 riders per car during peak hours. According to the CTA, the target should be 70-75." To improve the system, CTA will be adding 17 train runs and improving 50 bus routes, while eliminating 12 bus routes which have extremely low ridership. That said, it's also important to note that data is not the sole determining factor. CTA also takes into account public input and neighborhood demographics, i.e. poorer areas are less likely to get service cuts, as more people in the neighborhood are likely to depend on public transportation to get around.

We see concepts of information organization being employed in this story. The resource being collected is a type of information (ridership data), which is organized into collections using various organizing principles (individual route total ridership, hour-by-hour ridership, etc), and then analyzed to identify pain points, improve service, and save money. In businesses, analyzing performance data is not new, but it's interesting to see it in action in a public agency, which has to deal with internal organizational issues and external public reactions.

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