Traffic and Welfare Impacts of Credit-Based Congestion Pricing Applications: An Austin Case Study

Abstract

This study seeks smart credit-based congestion pricing (CBCP) solutions for maximally improving travelers’ welfare by varying toll levels and locations across the Austin, Texas network. Scenarios evaluated include selecting links with maximum delays by variably tolling bridges and by recognizing congestion externalities across all links. Travel demand models deliver inputs for normalized logsum differences to quantify and compare consumer surplus changes across traveler types, around the region. Results suggest limited tolling locations under four broad times of day can do more harm than good, unless travelers shift out of the PM and AM peak periods or revenues are returned to travelers as credits. When using CBCP across all congested links at congested times of day (with 10% of revenues retained to cover system administrative costs), an average net benefit of $1.61 per licensed driver per weekday is estimated, with almost all travelers benefiting. For example, 95% of the traffic analysis zones’ lowest value of travel time (VOTT) group (VOTT1 = $5/hour) are expected to benefit from the CBCP policy. Tolling at twice the difference between marginal social cost and average travel cost (on each subset of congested links) appears to benefit more people, although tolling high on various links adds to congestion elsewhere. For example: tolling Austin’s highest-delay-producing or “top 500” links is estimated to benefit 98.5% of the zones’ highest VOTT (VOTT5 = $45/hour) travelers, while raising vehicle-miles traveled by just 0.8% (as a result of more circuitous, congestion- and toll-avoiding travel).

Achieving Hierarchy-Free Approximation for Bilevel Programs With Equilibrium Constraints

“…The earliest study on bilevel programs with equilibrium constraints was motivated by Stackelberg congestion games (SCGs), which concern a leader (usually a traffic planner) who aims to induce a desirable equilibrium state in a congestion game (Wardrop, 1952;Roughgarden and Tardos, 2002) played by many self-interested followers (travelers). The network design problem (LeBlanc, 1975;Li et al, 2012) and the congestion pricing problem (Lawphongpanich and Hearn, 2004;Li et al, 2021) are two classic examples. More recently, the study of SCGs has been influenced by the introduction and constant evolution of connected and automated vehicle (CAV) technologies (Mahmassani, 2016), leading to such applications as the design of dedicated CAV facilities (Chen et al, 2016(Chen et al, , 2017Bahrami and Roorda, 2020) and the control of CAVs within such facilities (Levin and Boyles, 2016;Zhang and Nie, 2018).…”

Futures Market for Demand Responsive Travel Pricing

“…Although highly effective at reducing the number of automobiles, scarcity of resources in any scheme can lead to severe equity imbalances if alternatives do not exist. Fortunately, transit options are plentiful in the dense city-state of Singapore, but such is not the case elsewhere, such as in the U.S.A. Opposition often cites inequity risk as a reason not to implement dynamic pricing systems from both ends of the political spectrum (29)(30)(31). One potential mitigation scheme introduced by Kockelman et al is a ''travel credit'' scheme where all travelers are issued credits to use toward congestion pricing (15,(32)(33)(34).…”