A Mobile-Side-Dominant Method for Querying Present and Future Velocity on Urban Roads
Querying present and future traffic velocities of road segments is a routine task in urban intelligence transportation management,and a Vehicle-equipped-Edge Dominant(VED)method is proposed to answer the querying of present and future velocity of urban road segments.The collected data is exchanged with the other mobile sides by every vehicle-equipped mobile side when the mobile side's speed falls below a given threshold,and the light-weighted present and history velocity indexes are constructed locally to support the querying of present velocity.To train as few models as possible to predict future velocities,a road network is proposed to be partitioned into a set of road-segment clusters based on the segments'topological morphism and the spatio-temporal space is proposed to be partitioned into a set of model-equivalence classes according to the periodic time windows and road-segment clusters.The similar traffic patterns are exhibited by the road segments in the same model-equivalence class within the given time window.For every model-equivalence class,the federated learning is performed between the mobile sides and the data center to train the Long Short-Term Memories(LSTMs)which are stored at the mobile sides to answer the querying of future velocities of nearby areas.Data is indexed by every mobile side and queries are answered locally,thus the query response latency and possible communication congestion can be avoided.Further,data is stored at the mobile sides,rather than at one data center,so as to prevent the privacy leakage due to security attacks.