A CASP-based Solution for Traffic Signal Optimisation

TL;DR

This paper introduces a novel traffic signal optimization method using Constraint Answer Set Programming (CASP), demonstrating improved solution quality over traditional PDDL+ approaches through experiments on real data.

Contribution

It presents a new CASP-based encoding for traffic signal optimization, offering an alternative to PDDL+ with better solution quality.

Findings

CASP approach outperforms PDDL+ in solution quality

Experiments on real data validate the effectiveness of the method

CASP provides a flexible framework for traffic optimization

Abstract

In the context of urban traffic control, traffic signal optimisation is the problem of determining the optimal green length for each signal in a set of traffic signals. The literature has effectively tackled such a problem, mostly with automated planning techniques leveraging the PDDL+ language and solvers. However, such language has limitations when it comes to specifying optimisation statements and computing optimal plans. In this paper, we provide an alternative solution to the traffic signal optimisation problem based on Constraint Answer Set Programming (CASP). We devise an encoding in a CASP language, which is then solved by means of clingcon 3, a system extending the well-known ASP solver clingo. We performed experiments on real historical data from the town of Huddersfield in the UK, comparing our approach to the PDDL+ model that obtained the best results for the considered benchmark. The results showed the potential of our approach for tackling the traffic signal optimisation problem and improving the solution quality of the PDDL+ plans.