  Tolling, or congestion pricing, offers a promising traffic management policy
for regulating congestion, but has also attracted criticism for placing
outsized financial burdens on low-income users. Credit-based congestion pricing
(CBCP) and discount-based congestion pricing (DBCP) policies, which
respectively provide travel credits and toll discounts to low-income users on
tolled roads, have emerged as promising mechanisms for reducing traffic
congestion without worsening societal inequities. However, the optimal design
of CBCP and DBCP policies, as well as their relative advantages and
disadvantages, remain poorly understood. To address this, we study the effects
of implementing CBCP and DBCP policies to route users on a network of
multi-lane highways with tolled express lanes. We formulate a non-atomic
routing game framework in which a subset of eligible users is granted toll
relief in the form of a fixed budget or toll discount, while the remaining
ineligible users must pay out-of-pocket. We prove the existence of Nash
equilibrium traffic flow patterns corresponding to any given CBCP or DBCP
policy. Under the additional assumption that eligible users have time-invariant
VoTs, we provide a convex program to efficiently compute these equilibria. For
networks consisting of a single edge, we identify conditions under which CBCP
policies outperform DBCP policies (and vice versa), in the sense of improving
eligible users' access to the express lane. Finally, we present empirical
results from a CBCP pilot study of the San Mateo 101 Express Lane Project in
California. Our empirical results corroborate our theoretical analysis of the
impact of deploying credit-based and discount-based policies, and lend insights
into the sensitivity of their impact with respect to the travel demand and
users' VoTs.
