GIS and ITS Oriented Modelling Analytic to Enhance Level of Service of Traffic at Mid-Block on Junctions

Abstract

Intelligent transportation system is used as supportive tool to track the charactristictics of traffic. Computer vision based deep learning approach is attempted and collected data on classified volume counts, speeds and geometric deficiencies which are causative for poor level of service. artificial intelligence based trained data is used for classifying traffic and deep learning tools of neural network is used for recognizing speed of vehicle. GIS is used to map the violations of physical in a junction and at mid-block. Dynamic reactions of vehicle driver are sensitized to adjust and accommodate traffic mobility. Certain analytical inputs are framed in tri angle model. This model has identified the centroid positions of idealistic and observed. successively integration method is used the minimize the obstructions for reducing level of service. Results has shown that role of lane discipline, geometric needs to enhance, preferential signal of green for high occupancy vehicles, vehicle activated signal system, pedestrian sensitive signal on mobility. Deliverables have been made as outcome from models, analytics and conceptual needs on way out for enhancing level of service of junction and mid-block with reference to traffic.