  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. A recent survey by the association of European
transport, estimates hefty income losses due to fare evasion, highlighting that
free-riding is a serious problem that needs immediate attention. To this end,
we highlight the attack vectors which can be exploited by free-riders by
analyzing the crowdsourced data about the control-locations. Next, we propose a
framework to generate randomized control-location traces by using generative
adversarial networks (GANs) in order to minimize the attack vectors. Finally,
we propose metrics to evaluate such a system, quantified in terms of increased
risk and higher probability of being subjected to control checks across the
city.
