Optimizing traffic lights in a cellular automaton model for city traffic

TLDR

The cellular automaton model blends Biham‑Middleton‑Levine and Nagel‑Schreckenberg dynamics on a simple square lattice where all streets and intersections are treated equally. The study investigates how global traffic‑light control strategies affect traffic flow in this cellular automaton model. The authors evaluate global strategies such as green‑wave and random switching to boost throughput. Capacity depends on traffic‑light cycle times, with optimal periods set by intersection spacing; synchronized lights reduce to a single‑street bottleneck optimization, and green‑wave or random switching strategies unexpectedly improve throughput.

Abstract

We study the impact of global traffic light control strategies in a recently proposed cellular automaton model for vehicular traffic in city networks. The model combines basic ideas of the Biham-Middleton-Levine model for city traffic and the Nagel-Schreckenberg model for highway traffic. The city network has a simple square lattice geometry. All streets and intersections are treated equally, i.e., there are no dominant streets. Starting from a simple synchronized strategy, we show that the capacity of the network strongly depends on the cycle times of the traffic lights. Moreover, we point out that the optimal time periods are determined by the geometric characteristics of the network, i.e., the distance between the intersections. In the case of synchronized traffic lights, the derivation of the optimal cycle times in the network can be reduced to a simpler problem, the flow optimization of a single street with one traffic light operating as a bottleneck. In order to obtain an enhanced throughput in the model, improved global strategies are tested, e.g., green wave and random switching strategies, which lead to surprising results.