Traffic Signal Timing Optimization: From Evolution to Adaptation
提出一种学习自适应框架，将交通信号配时从耗时的进化优化转变为快速的自适应，通过多源迁移粒子群优化和分层平滑正则化方法，实现对未见交通状况的实时优化。
Traffic signal timing optimization (TSTO) is a significant problem in smart cities. Evolutionary computation (EC) algorithms have been widely studied to solve the TSTO problem. However, as existing EC-based algorithms usually use iterative-based evolution and simulation-based evaluation to optimize the timing plan for certain traffic situations, they still suffer from two difficulties, which are the time-consuming optimization process in certain traffic situations and the weak adaptation ability to unseen traffic situations. Therefore, this article proposes a novel learning for adaptation (LA) framework for real-time TSTO by changing the optimization process from evolution to adaptation. The LA learns a map model from the traffic volume input to the traffic signal timing. Then, the learned map model is expected to adapt to unseen traffic situations faster and better. The LA framework includes a data collection stage and a model learning (ML) stage. First, in the data collection stage, a multisource transfer-based particle swarm optimization (MTPSO) algorithm is proposed to avoid the under-optimizing issue. Second, in the ML stage, a multilevel smoothness regularization (MSR) method is proposed to deal with the over-fitting issue. The proposed LA framework with MTPSO and MSR is named as LMM method. This way, the TSTO process is changed from time-consuming evolution to time-efficient adaptation, which is significant in practical real-time applications. Comprehensive experiments with ten different synthetic and real-world road networks under different traffic volume distributions are carried out. The results show that the LMM method adapts better to unseen traffic volume inputs and can achieve real-time TSTO.