Sandra Castellanos-Paez, Francesco Percassi, Mauro Vallati

Exploring the Trade-off Between Flexible and Deployable Models for PDDL+ Urban Traffic Control (Extended Abstract)

Introduction

Exploring the trade-off between flexible and deployable models for pddl+ urban traffic control (extended abstract). Discover the trade-off in PDDL+ urban traffic control: balancing flexible models with deployable solutions for existing infrastructure. Introduces "Trade model" for optimized signal plans.

Abstract

The problem of traffic signal optimisation has been successfully tackled using the PDDL+ planning formalism, which also provides an ideal ground for simulating traffic behaviour and performing what-if analysis to assess and compare alternative scenarios. This line of research leads to approaches that can efficiently generate high-quality signal plans with significant benefits in terms of congestion and emissions reduction, as demonstrated both in simulations and real-world deployments. Existing models for automated planning-based traffic signal control can be roughly divided into two classes. (i) Models maximising the flexibility of the traffic controller, where the planning system can dynamically adjust the duration of traffic stages without constraints on the overall cycles and on the differences between subsequent cycles. (ii) Models that guarantee the deployability of traffic signal control techniques also on legacy infrastructure, by forcing the AI approach to select the cycle configurations of traffic signals for the controlled junctions from a given pre-defined set. Of course, both classes offer valuable properties and benefits: the extreme flexibility helps shed light on the potential gains achievable through investment in brand-new infrastructure, whereas the deployable approaches ensure the immediate usability of tools to maximise short-term impact. To bridge the gap between different model classes and explore the trade-off between flexibility and deployability, we present the Trade model. It enables the enforcement of key constraints required for deployability while preserving a level of flexibility that surpasses the capabilities of traditional traffic control infrastructure.

Review

This extended abstract presents a timely and relevant exploration into the core dilemma faced when applying advanced AI planning techniques, specifically PDDL+, to urban traffic control: balancing theoretical optimality with practical implementation constraints. The authors effectively frame the problem, highlighting the proven benefits of PDDL+ in generating high-quality signal plans for congestion and emissions reduction. They clearly delineate the two prevalent classes of models—those maximizing flexibility to demonstrate potential gains with new infrastructure, and those ensuring deployability on existing legacy systems by adhering to predefined cycle configurations. This initial setup clearly establishes the inherent value and distinct advantages of both approaches, setting the stage for the proposed contribution. The paper's core contribution is the introduction of the "Trade model," designed explicitly to bridge the gap between these two seemingly disparate model classes. The abstract positions this model as an innovative solution that intelligently enforces key deployability constraints, thereby making the traffic control techniques viable for existing infrastructure, while simultaneously preserving a level of flexibility that significantly surpasses conventional systems. This approach of exploring the trade-off between flexibility and deployability is highly valuable, as it seeks to reconcile the often-conflicting goals of theoretical performance and real-world applicability, offering a path towards more adaptable and effective traffic management solutions. While an extended abstract naturally limits the depth of detail, the proposed "Trade model" holds significant promise for advancing the field of AI-driven urban traffic control. By aiming to capture the best aspects of both flexible and deployable models, it could accelerate the adoption of sophisticated planning techniques in diverse urban environments. Future work, presumably in a full paper, would benefit from a detailed exposition of the "Trade model's" formalisms, empirical evaluation demonstrating its performance against both extreme flexibility and pure deployability models, and a thorough analysis of the trade-off curve under various traffic conditions. This work represents a crucial step towards developing robust and practical intelligent traffic management systems.

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