A Novel Max Pressure Algorithm Based on Traffic Delay

TL;DR

This paper introduces a travel-delay-based Max Pressure algorithm for traffic control that overcomes practical implementation challenges of traditional methods and outperforms benchmarks in simulations, especially in connected vehicle environments.

Contribution

A new travel-delay-based Max Pressure control algorithm that improves practical implementation and performance over traditional vehicle-count-based methods.

Findings

Outperforms benchmark Max Pressure variants in simulations.

Generates lower delays with partial vehicle penetration in connected vehicle scenarios.

Maintains maximum stability property of original Max Pressure algorithm.

Abstract

This paper considers a novel travel-delay-based Max Pressure algorithm for control of arbitrary transportation networks with signalized intersections. The traditional number-of-vehicle-based Max Pressure (Original-MP) algorithm has received tremendous attention recently due to its ease of implementation and scalability to large network scenarios. The Original-MP algorithm also has a desirable property called maximum stability, which means a demand scenario can be accommodated by this method as long as it can be accommodated by any existing control policy. However, implementation of the Original-MP Max Pressure algorithm may be difficult in practice as estimation of queue lengths at intersections requires significant measurement infrastructure. The Original-MP framework also uses a point queue model to represent the vehicle transition between links, which does not consider the position of the vehicles, even though this may significantly impact the control performance. In addition, intersection approaches with low travel demand can incur arbitrarily large delays due to having short queues. The proposed travel-delay based Max Pressure model overcomes these drawbacks while inheriting the maximum stability feature of the Original-MP. It also is shown to outperform several benchmark Max Pressure variants in a battery of simulation tests. Lastly, the proposed algorithm can be implemented in a Connected Vehicle (CV) environment, in which a subset of vehicles serve as mobile probes. The results show the proposed travel-delay-based model generates lower delay with non-full penetration rate than the benchmark models with full penetration rate under certain traffic conditions.