TY  - JOUR
PY  - 2022//
TI  - Traffic signal control with adaptive online-learning scheme using multiple-model neural networks
JO  - IEEE transactions on neural networks and learning systems
A1  - Hong, Wanshi
A1  - Tao, Gang
A1  - Wang, Hong
A1  - Wang, Chieh
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - This article proposes a new traffic signal control algorithm to deal with unknown-traffic-system uncertainties and reduce delays in vehicle travel time. Unknown-traffic-system dynamics are approximated using a recurrent neural network (NN). To accurately identify the traffic system model, an online-learning scheme is developed to switch among a set of candidate NNs (i.e., multiple-model NNs) based on their estimation errors. Then, a bank of optimal signal-timing controllers is designed based on the online identification of the traffic system. Simulation studies have been carried out for the obtained control strategies using multiple-model NNs, and the desired results have been obtained. Moreover, compared with the widely used actuated traffic signal control schemes, it is shown that the proposed method can reduce vehicle travel delays and improve traffic system robustness.<p />  <p>Language: en</p>
LA  - en
SN  - 2162-237X
UR  - http://dx.doi.org/10.1109/TNNLS.2022.3146811
ID  - ref1
ER  -