A Novel Travel-Delay Aware Short-Term Vehicular Traffic Flow Prediction Scheme for VANET
How to achieve a fast and safe data dissemination in the Vehicular ad-hoc network (VANET) is a hot research topic these days. However, the high mobility of the vehicles makes the topology of VANET unstable, and real-time road information is generally limited. Considering these shortcomings, it is helpful to use the accurate traffic prediction to assist the topology control in the VANET. For offering a better traffic flow prediction, this paper proposes an innovative hybrid prediction method, Delay-based Spatial-Temporal Autoregressive Moving Average model (DSTARMA) to enhance prediction effect. This model mainly focuses on dealing with the travel delay problem in short-term traffic flow prediction. In other words, vehicles always need some time to move from one place to another in a real traffic situation, and this period is called travel delay. In previous spatial-temporal models, no one takes this factor into account. In our model, the travel delay is handled in the form of spatial-temporal weighted matrices and treated as a key role. We evaluated our approach based on data in England highway traffic system. The result proves our approach is reliable and has the ability to offer more accurate road information in advance to support VANET.
