Innovations in European Smart Transportation using Geospatial Information System

Abstract. The smart cities necessitate the advanced operational information and communication technology to share and improve efficiency with the nation. The datasets of high traffic roads were examined for 4 different European Countries namely Germany, Greece, Turkey, and Romania for this study. Moreover, the study involves the identifying the busy traffic time on the roads and identifies the solution of following the other routes enabling the commuter to reach the destination on time. This is carried out with deep learning techniques in GIS environment using ArcGIS to identify the different route-finding scenario. The Dijkstra’s algorithm is best suited for this routing model. This study revolves around multimodal transport planning within smart cities for smart traffic flow for smart living. A case study of Greece street roads was considered; the linear regression model was implemented for the datasets. The obtained p > |t| for average speed is Stratigou Makrigianni (0.906), Agias Annis (0.754), Mystra (0.675), and Komvos Ag.loanni Renti (0.470). The study reflects the traffic congestion on four European Countries with best roads and national highways in the world. However, the traffic in these countries seems to be heavy during the peak hours or unusual hours. With GIS, one can trace the traffic routes and also take proper decision to avoid the traffic, and move toward the destination with different paths. This approach will help the closeby places to be free from the pollution.