QoS-Aware Reliable Traffic Prediction Model under Wireless Vehicular Networks

Abstract

With the continuous progress of communication quality, the wireless vehicular networks (WVN) will surely be-come an inevitable part of future smart cities. Inside WVN where context is complicated and stochastic, quality of service (QoS) acts as the core concern for broad users. And reliable prediction towards traffic in WVN is essentially an important demand to ensure QoS. Conventionally, related methods mainly focus one side to establish robust prediction models, possessing some limitations. To bridge such gap, model integration may be an intuitive and promising solution. This paper proposes QoS-aware reliable traffic prediction model under WVN (TP-WVN). Firstly, two typical prediction models are used as fundamental learners, which can capture the spatial correlations from different angles. Then, regression model is selected as the integrator to combine base models together. Simulative experiments on a real-world dataset are conducted to evaluate the proposal, and results show that the TP-WVN is able to realize reliable QoS-aware prediction compared with baseline methods.