Intelligent Transport System and Traffic Management Frameworks

The tremendous growth in transport systems and the increase in the number of vehicles over the past several decades have created a significant problem for urban areas, that is traffic congestion. An enormous traffic jam of cars built up earlier this week next to the Qianmen Gate of Beijing City. In recent years, traffic congestion analysis has come forward as a significant challenge; numerous scholars concentrate on the root causes of traffic congestion. Here, some recent research work points out the cause of traffic jams in one direction and suggests an alternate route for another direction. On the other hand, traffic forecasting needs to modernize traffic models that can analyze these actual traffic condition statistics. Intelligent Transport Systems (ITS) aims to improve the quality of mobility and its sustainability by gathering input technology with engineering solutions via communication methods. Besides that, other studies of ITS from the standpoint of artificial intelligence (AI) have been done. ITS exercises a capillary network of sensors strung over the roads and uses this information to transform traffic information such as flow, speed, and density. This allows the administration to judge traffic dynamics more accurately and conduct control operations more effectively. This research proposes a smart road transportation domain that takes fuel consumption models as its basic framework for making approximate traffic forecasts and analyzing traffic flow disturbances. Moreover, it suggests the application of a multi-agent system method for traffic light control with deviation, a tool for avoiding congestion that utilizes only sparse distributed message passing. Firstly, the framework introduced is smart road traffic prediction and alleviation based on the genetic model put forward by the author, as it can reduce fuel use. Its powerful optimization can rely on statistical models. Poisson Distribution, for example, is applied to forecast car arrival probability from historical data.