Abstract
Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.
Highlights
Urban traffic problems have increasingly worsened given the increase in urban vehicle numbers and negative effects of vehicle energy consumption and exhaust emissions on the environment
P. et al (2013) focused on a trade-off between delay and emissions based signal optimization; in their study, a methodology was first developed for drive vehicle profiles, and a motor vehicle emission simulation was applied to estimate emissions given a macroscopic input; in addition, these authors developed and solved an optimization methodology for signal timing through a genetic algorithm; the air quality benefit by reducing vehicle emissions using an intersection signal control was demonstrated through a case study, and the quality benefit from the intersection signal control was discussed under various scenarios of cycle lengths, percentages of turning vehicles, and traffic demands on major/minor roads
The signal cycle and green time length of each phase were used as decision variables under the constraint of the saturation level of the approach and signal time range, and a typical intersection in Lanzhou City was selected as the case study on the basis of the non-dominated sorting ABC (NSABC) algorithm for solving a multi-objective optimization model
Summary
Urban traffic problems have increasingly worsened given the increase in urban vehicle numbers and negative effects of vehicle energy consumption and exhaust emissions on the environment. F. et al (2011) proposed a multi-objective optimization model called non-dominated sorting genetic algorithm II (NSGA II) for unsaturated intersections and solved the multi-objective optimization problems These authors analyzed the validity of common objectives, such as average vehicle delay, stops, and queue length to signal control parameters. The method presented uses the vehicle delay and stops as optimization targets, and the model is solved by non-dominated sorting artificial bee colony (ABC) algorithm. The NSABC algorithm is selected to solve the model using VC ++6.0 to obtain the Pareto solution set of this model because the proposed model is a typical multi-objective optimization problem. The results demonstrate that the ABC algorithm can effectively solve the model
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