Zhengxi Li

TL;DR: A novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently and is applied for the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction.

TL;DR: This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs) that is robust to illumination changes and weather conditions during 24 h or one day.

TL;DR: A smart grid vision of the electric vehicles for the next 30 years and beyond is presented from six perspectives pertinent to intelligent transportation systems: 1) vehicles; 2) infrastructure; 3) travelers; 4) systems, operations, and scenarios; 5) communications; and 6) social, economic, and political.

TL;DR: A probabilistic model is developed that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains and is validated using a large-scale data set collected from the Shenzhen Metro system.

TL;DR: This is the first paper, which implements a system utilizing smart card data and GPS data to forecast the passenger flow in real time, and is believed to be the first to be implemented.