Towards a sustainable monitoring: A self-powered smart transportation infrastructure skin

Sustainable monitoring of traffic using clean energy supply has always been a significant problem for engineers. In this study, we proposed a self-powered smart transportation infrastructure skin (SSTIS) as an innovative and bionic system for the traffic classification of a smart city. This system incorporated the self-powered flexible sensors with net-zero power consumption based on the Triboelectric Nanogenerator (TENG) and an intelligent analysis system based on artificial intelligence (AI). The feasibility of the SSTIS was tested using the full-scale accelerated pavement tests (APT) and the long-short term memory (LSTM) deep learning model with a vehicle axle load classification accuracy up to 89.06%. This robust SSTIS was later tested on highway and collected around 869,600 pieces of signals data. The generative adversarial networks (GAN) WGAN-GP (Wasserstein GAN - Gradient Penalty) was used for data augmentation, due to the imbalanced data of different vehicle types in actual traffic. The overall accuracy for on-road vehicle type classification improved to 81.06% using the convolutional neural network ResNet. Finally, we developed a mobile traffic signal information monitoring system based on cloud platform and Android framework, which enabled engineers to obtain the vehicle axle-load information mobilely. This study is the emerging design and engineering application of the self-powered flexible sensors for smart traffic monitoring, which provides a significant advance for intelligent transportation and smart cities in future.