Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing

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.