Lingfeng Qi

Service migration is one of the key topics of Internet of Vehicles (IoV). MEC (mobile edge computing), which is carried on the roadside unit (RSU), could serve as a service provider and provide a V2I (vehicle to infrastructure) cooperation service. To solve the high migration rate caused by the vehicles high mobility feature, MSCN (mobile secondary computing node) framework is defined. To study service migration features under the MSCN framework further, in this paper, road vehicles motion features, e.g., driving intention and regional traffic condition, are introduced to construct the Markov decision model, which is used to explain the service migration decision procedure, while DIMDP (driving intention-based MDP) is defined. Corresponding cost functions are defined, while the optimal object is given. A two-way road scenario is selected as a typical scenario. NS3 platform is employed to fulfill the simulation process. Simulation results show that the proposed service migration strategy performs well and is intensive to vehicle density change.

The accuracy of travel time prediction is not only decided by the state of the current journey but also affected by that of the transfer journey. This paper focuses on the transit transfer problem of urban public transport and aims to improve the performance of multi-route bus travel time prediction. In this paper, two kinds of infect factors, which are macro infect factors and local infect factors, are defined. Then, a pre-processing approach is proposed to construct a travel chain, which is used to compensate the shortage of raw data set. Multi-route bus travel time is divided into four parts, which are travel time of the current driving segment (CDS), bus stop dwelling time module, stop-stop travel time, and transfer point waiting time. A parrel processing architecture, which includes four learning blocks, is designed to fulfill the prediction process of the above four time parts. The classic LSTM model is used to predict the CDS travel time and transfer point waiting time. An improved DA-RNN model, the MDARNN model, is proposed to predict the bus stop dwelling time module, stop-stop travel time. Moreover, a real-time traffic flow model is used to calibrate the prediction time of all four sub-models. Experiment results prove the validity of the proposed methods.