Tarun Rambha

TL;DR: The flexibility of this tolling scheme is demonstrated in three specific traffic modeling contexts with varying traffic flow and user behavior assumptions: a day-to-day pricing model using static network equilibrium with link delay functions; a within-day adaptive Pricing model using the cell transmission model and dynamic routing of vehicles; and a microsimulation of reservation-based intersection control for connected and autonomous vehicles with myopic routing.

TL;DR: This paper proposes an average cost Markov decision process model to reduce the expected total system travel time of the logit route choice model using dynamic pricing and develops approximation schemes for handling a large number of users.

TL;DR: This paper introduces △-tolling, a novel tolling scheme that is adaptive in real-time and able to scale to large networks, and provides theoretical evidence showing that under certain assumptions ▵-Tolling is equal to Marginal-Cost Tolling, which provably leads to system-optimal, and empirical evidence showing the increase in social welfare in two traffic simulators.

TL;DR: This paper considers a scenario where a centralized network manager wishes to optimize utilities over all agents, i.e., implement a system-optimum routing policy and presents a computationally tractable method that computes the minimal amount of agents that the system manager needs to influence (compliant agents) in order to achieve system optimal performance.

TL;DR: An asymptotic reset model is proposed that generalizes the full-reset and no-reset cases and uses the concept of reset rate to characterize the temporal dependence of parking probabilities on earlier observations.