  Traffic congestion has become an inevitable challenge in large cities due to
population increases and expansion of urban areas. Various approaches are
introduced to mitigate traffic issues, encompassing from expanding the road
infrastructure to employing demand management. Congestion pricing and incentive
schemes are extensively studied for traffic control in traditional networks
where each driver is a network "player". In this setup, drivers' "selfish"
behavior hinders the network from reaching a socially optimal state. In future
mobility services, on the other hand, a large portion of drivers/vehicles may
be controlled by a small number of companies/organizations. In such a system,
offering incentives to organizations can potentially be much more effective in
reducing traffic congestion rather than offering incentives directly to
drivers. This paper studies the problem of offering incentives to organizations
to change the behavior of their individual drivers (or individuals relying on
the organization's services). We developed a model where incentives are offered
to each organization based on the aggregated travel time loss across all
drivers in that organization. Such an incentive offering mechanism requires
solving a large-scale optimization problem to minimize the system-level travel
time. We propose an efficient algorithm for solving this optimization problem.
Numerous experiments on Los Angeles County traffic data reveal the ability of
our method to reduce system-level travel time by up to 6.9%. Moreover, our
experiments demonstrate that incentivizing organizations can be up to 8 times
more efficient than incentivizing individual drivers in terms of
incentivization monetary cost.
