A CASP-based Solution for Traffic Signal Optimisation

2025-07-25

AI Summary

This work addresses the urban traffic signal timing optimization problem by proposing the first Constraint Answer Set Programming (CASP)-based modeling and solving framework. Unlike conventional PDDL+ approaches—limited in expressing complex optimization objectives and generating provably optimal policies—our method formulates a precise CASP encoding that captures spatiotemporal constraints, traffic flow dynamics, and multi-objective optimization criteria, and solves it efficiently using the clingcon 3 solver. Experiments on real-world historical traffic data from Huddersfield, UK, demonstrate that our approach significantly outperforms the state-of-the-art PDDL+-based method, yielding substantial improvements in key metrics such as traffic throughput and delay reduction. To the best of our knowledge, this is the first application of CASP to traffic signal control, thereby extending the applicability of logic programming in intelligent transportation optimization. The framework offers strong expressive modeling capability, high-quality solutions, and formal verifiability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Combinatorial Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Optimizing traffic signal green times in urban areas
• Overcoming PDDL+ limitations in optimization planning
• Using CASP to improve traffic signal solution quality

Innovation

• Uses Constraint Answer Set Programming (CASP)
• Employs clingcon 3 for solving encoding
• Improves PDDL+ plan solution quality