Transit agencies have long been operating in data-poor environments. They have been relying on labourintensive and project-specific data collection, which are often sparse and costly. Recently, the adoption of technologies that generate passive data streams has become increasingly common. One of such technologies is the smart card automatic fare collection (AFC) system which provides a massive database containing detailed and up-to-date fare validation records. An important preoccupation of both practitioners and researchers is to transform the raw and often partial data into useful information.
Various aspects of a transit agency, from day-to-day operations to strategic rethinking of the transit system, can benefit from such intelligence. While data timeliness of smart card data is an obvious advantage over other data collection methods, data organization and processing are important issues. Each application, aiming to tackle a specific problem, requires a different approach and data enrichment procedure. Drawing data and experience from the Montréal region, which currently operates an extensive multi-region, multimodal and multi-operator smart card AFC system that generates two million transactions per day, this paper presents contributions of smart card intelligence within a regional transit authority context by showcasing several real-world applications on transit services, transit equipment and fare use.
Smart card data analytics provide different departments within the organization and among partner organizations in the region access to up-to-date and previously unavailable information. This in turn allows them to make more informed decision on policies and plans. The future of public transit relies greatly on adequate data and intelligence. The next steps would be to expand the number of applications, to put in place a structure that facilitates systematic and corporate use of the data and to increase cooperation among partner organizations.
