Abstract
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.
Highlights
Real-time traffic signal control is very important for operation and management of urban transportation systems
Comparison between the proposed model and current scheme indicates that the multi-objective traffic control model provides real-time signal timing plan applicable to time-varying traffic flow and obtains better delay time, number of stops, and effective capacity than the current signal scheme
This article presents a general methodology for realtime traffic control at intersections, including a multiobjective optimization model and the corresponding
Summary
Real-time traffic signal control is very important for operation and management of urban transportation systems. Focusing on real-time traffic control for intersections, one key feature of this article is to formulate a multi-objective traffic signal control model, utilizing minimum delay time, minimum number of stops, and maximum effective capacity as three objectives. To generally optimize the signal timing plan, we formulate a multi-objective traffic control model based on the above variables. The multi-objective traffic signal control model is generally formulated as the following equation f1(tn) = min D = (dn Á qn)= qn n=1 n=1 In this model, the time-varying turning movement flows are estimated from detected entering and exiting volumes using dynamic origin–destination estimation methods. We integrate Pareto front into the PSO algorithm for solution of the multi-objective real-time signal control model in equation (19). Shrinkage factor is used in place of inertia weight to avoid too small weight in the late period, which will lose the ability of searching new space
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