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Real-time traffic control is very important for urban transportation systems. Due to conflicts among different optimization objectives, the existing multi-objective models often convert into single-objective problems through weighted sum method. To obtain real-time signal parameters and evaluation indices, this article puts forward a Pareto front–based multi-objective traffic signal control model using particle swarm optimization algorithm. The article first formulates a control model for intersections based on detected real-time link volumes, with minimum delay time, minimum number of stops, and maximum effective capacity as three objectives. Moreover, this article designs a step-by-step particle swarm optimization algorithm based on Pareto front for solution. Pareto dominance relation and density distance are employed for ranking, tournament selection is used to select and weed out particles, and Pareto front for the signal timing plan is then obtained, including time-varying cycle length and split. Finally, based on actual survey data, scenario analyses determine the optimal parameters of the particle swarm algorithm, comparisons with the current situation and existing models demonstrate the excellent performances, and the experiments incorporating outliers in the input data or total failure of detectors further prove the robustness. Generally, the proposed methodology is effective and robust enough for real-time traffic signal control

Pareto front–based multi-objective real-time traffic signal control model for intersections using particle swarm optimization algorithm