Intelligent Transportation Systems IoT Solutions for Traffic Optimization and Autonomous Vehicle Networks
Abstract
Many existing frameworks to support Intelligent Transportation Systems (ITS) lean on static infrastructure and sparse real-time data, which have inefficiencies that may exacerbate safety concerns such as are manifested with the emergence of autonomous vehicles. In the paper, a novel IoT-enabled system combining vehicle-to-infrastructure (V2I) communication with complex machine Learning (ML) models is presented to develop an intelligent adaptive traffic control approach. The use of IoT sensors for collecting real-time data to predict traffic flow and improve road safety. Advances between 20 and up to for average travel time, and up-to-32 % reduction in the Traffic Congestion Index were reported from existing methods. Furthermore, accident levels have fallen by 33%, indicating increased safety. Overall, the proposed ITS architecture provides a robust solution for contemporary urban transportation with several fundamental differences from traditional systems without hindering or impeding autonomous vehicles to be integrated in a ubiquitous manner.