ABSTRACT
An intelligent transportation system for smart city applications is regarded as a solution for reducing traffic congestion, fuel consumption, and accidents, and saving lives by levering computing, communication, control, and collected traffic data. Currently, traffic officials are not only facing a significant absence of automated and accurate traffic prediction but also making informed decisions in a real-time manner for traffic management in smart cities. This chapter proposes an automated method for counting vehicles for identifying traffic patterns and offering an adaptive traffic management system by using deep learning. The proposed approach helps to count vehicles in the intersection which helps to make an informed decision about making streets one-way or two-way adaptive traffic light systems depending not only on the number of vehicles in the intersection but also based on the time of the day. Specifically, the proposed approach detects and counts turning vehicles, and pedestrians at a traffic intersection in a real-time manner (as well as in recorded videos in case of offline processing) using deep learning. Then the collected statistics are used to make an informed decision. The proposed approach gives better results (in terms of time and accuracy) compared to state-of-the-art work.
