In one of the first production application for extensive analysis of “big” data in a U.S. transit agency, we designed and implemented a user-friendly computer program that automatically detected and corrected inevitable data errors in the daily Automated Fare Collection (AFC) system transaction log files, and devised an algorithm to compute actual aggregate mileage travelled by each individual bus passenger on a zero manual intervention and daily reporting basis. This method was approved by the Federal Transit Administration (FTA) as a 100% sample for bus passenger-miles for National Transit Database reporting and Federal Capital funding purposes, replacing previous labor-intensive random sample practices with higher error margins. At the time, the AFC transaction logs were not broken down by trip, no electronic bus driver sign-on data was available, and no geo-location information was available from the buses. This resulted in various heuristics being necessary to derive the required results. Since that time, the agency has progressively moved towards equipping all buses with automated passenger counters and automated vehicle location systems. However, until 100% of the fleet is fitted with entrance-exit door sensors, this farecard-based method of measuring passenger ridership and passenger-miles remains in daily production use.
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