Optimizing traffic lights in a cellular automaton model for city traffic.
By E. Brockfeld, R. Barlović, Andreas Schadschneider, Michael Schreckenberg. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001
399 citations recorded by the source corpus.
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.
Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Transportation
View Paper: https://www.semanticscholar.org/paper/c529a80f52b50a2d65f3768b9e89a0c0946279ed
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In Computational Theory and Mathematics
In Control and Systems Engineering
In Transportation
Citations in the corpus
- Predictive Cruise Control: Utilizing Upcoming Traffic Signal Information for Improving Fuel Economy and Reducing Trip Time (2011, background): “Optimal traffic management at intersections has been mainly studied from a signal-timing optimization perspective (signal synchronization) [9]–[11].”
- Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning (2017, background): “In particular, significant research has been conducted employing reinforcement learning methods in the area of traffic light signal control [39, 2, 7].”
- Opportunities for multiagent systems and multiagent reinforcement learning in traffic control (2009, methodology): “In [15] the impact of a coordination among traffic signals is simulated in a cellular-automata-based model.”