Throughput-Improving Control of Highways Facing Stochastic Perturbations
For traffic engineers and highway operators, this work provides a novel method to optimize throughput under stochastic conditions, though it is domain-specific and incremental in nature.
This paper develops a computational method for maximizing highway throughput under stochastic perturbations, using a cell-transmission model with Markovian capacities. The approach, formulated as a mixed-integer linear program, achieves optimal control of on-ramp flows and metering decisions while ensuring bounded queues, with performance demonstrated on a segment of Interstate 210.
In this paper, we study the problem of traffic management in highways facing stochastic perturbations. To model the macroscopic traffic flow under perturbations, we use cell-transmission model with Markovian capacities. The decision variables are: (i) the admissible flow through each on-ramp, and (ii) whether individual on-ramps are metered to prioritize the mainline traffic or not. The objective is to maximize the throughput while ensuring that on-ramp queues remain bounded on average. We develop a computational approach to solving this stability-constrained, throughput-maximization problem. We establish a mixed-integer linear program for throughput maximization and construct an algorithm that gives the optimal solution in particular settings. We illustrate the performance benefits of the proposed approach through a computational study on a segment of Interstate 210 in California, USA.