Reinforcement learning to address the free-rider problem in transport


Public transport network constitutes for an indispensable part of a city by providing mobility services to the general masses. To improve ease of access and reduce infrastructural investments, public transport authorities often adopt proof of payment system. Such a system operates by eliminating ticket controls when boarding the vehicle and subjecting the travelers to random ticket checks by affiliated personnel (controllers). Although cost efficient, such a system promotes free-riders, who deliberately decide to evade fares for the transport service.


Use datasources provided by TL such as ridership numbers, fraud history, ticket sales to generate a datat drive scheduling pattern for their controllers to go to stop in order to maximize controlling people without valid tickets. This project will be done in collaboration with the Transports Publics de la région lausannoise (TL).