In this paper, a system for data ingestion and reconciliation of smart cities related aspects as road graph, services available on the roads, traffic sensors etc., is proposed.
The APIs return us the shortest path taking into account the road graph, which is a reasonable estimate of the route the customers will actually use to reach the shop.2 For this route, the APIs report both the total distance in meters and the estimate number of seconds the trip would take.
Proposing a novel framework based on Big Data graph analysis tools such as Neo4j or Apache Shindig to better explore the road network graph.
Result of the OWA aggregation specifies weight of each edge in the road network graph.
In this paper, we study how to refresh a cache when one edge of the underlying road network (graph) changes.
In the FCD processing, the vehicle trajectories are first map-matched to an OpenStreetMap road network graph of the region.
Trip T is composed of GPS location traces and S is ordered sequence of edges (path) of road network graph G [5 7, 25, 26].
This case represents when input trajectory is empty and none of the edges of the road network graph is yet traversed.
For a given ZoQ Z(L s, L dδ),δwe define a subgraph (mathcal {G}(Z) = (mathcal {V}(Z), mathcal {E}(Z))) from the road network graph derived from a ZoQ.
We performed tests with the data sets of the french road network graph used in [6]. Figure 14 compares the computation time of our MapReduce-A* approach with the computation time of MapReduce-Dijkstra approach while ranging the subgraphs length l from 15too 200 km.
