LAUSR

Mobile edge computing supports the connected cars to ensure real-time, interactive, secured, and distributed services for customers. Connected car-sharing systems, as the promising appliance of connected cars, provide a convenient transportation mode for citizens’ intra-urban commutes. Determining the locations of depots is the primary job in connected car-sharing systems. Existing methods mainly use qualitative method and do not consider spatial–temporal dynamic travel demands. This article proposes a mobile edge computing–based connected car framework which uses normal taxis as connected cars to describe their Global Positioning System trajectory and perform the computing tasks in each mobile edge computing server independently. A spatial–temporal demand coverage approach is developed to optimize the location of depots. This article proposes a deep learning method to predict car-sharing demand constructed by a stacked auto-encoder model and a logistic regression layer. The stacked auto-encoder model is employed for learning the latent spatial and temporal correlation features of demand. A graph-based resource relocation model is proposed to minimize the cost of relocation considering spatio-temporal variation of car-sharing demand. Experiments performed on the large-scale real-world data sets illustrate that our proposed model has superior performance than existing methods.