Advanced Intelligent Predictive Models for Urban Transportation

 Advanced Intelligent Predictive Models for Urban Transportation

Advanced Intelligent Predictive Models for Urban  Transportation

The tremendous growth in transport systems and the increase in the number of vehicles on the roads in recent decades have created a significant problem in urban areas, namely traffic congestion. Traffic congestion inroads have been the biggest problem in the largest cities around the globe, especially cities in developing countries, where roads are not well designed and traffic on the roads is poorly managed. Traffic congestion increases fuel consumption and causes air pollution. In recent years, minimizing road traffic congestion has been a significant challenge; many researchers have focused on discovering the causes of traffic congestion.

Some recent research works have merely identified the causes of traffic jams and suggest alternate routes to avoid traffic congestion. Besides, traffic forecasting requires accurate traffic models which can analyze the actual traffic condition statistically.

Intelligent transport systems (ITS) are being designed to develop the quality and sustainability of mobility by incorporating data as well as communication technologies with transport engineering. Other studies on ITS from the perspective of artificial intelligence (AI) have also been done. ITS depends on a capillary network of sensors which are installed on the roads to provide information on traffic variables like flow, speed, and density. These variables are monitored by administration centers to approximate traffic dynamics and apply control operations.

This book recommends a smart framework for the domain of transportation that performs traffic prediction with a fuel consumption model and analyzes traffic flow congestion using a genetic and regression model. It also proposes a traffic light controller and traffic deviation system based on a multi-agent system. First, this framework proposes a smart traffic prediction and congestion avoidance system based on the genetic model to reduce fuel consumption and pollution. The model uses Poisson distribution for the prediction of vehicle arrival based on recurring size. This model comprises traffic identification, prediction, and congestion avoidance phases. The system checks for the fitness function to determine traffic intensity and further uses predictive analytics to determine future traffic levels. It also integrates a fuel consumption model to save time and energy.

This framework then predicts short-term traffic flow using structure pattern and regression methods. Short-term traffic prediction is one of the required fields of study in the transportation domain. It is beneficial to develop a more advanced transportation system to control traffic signals and avoid congestion. The framework proposed will improve the traffic system and thereby also protect the environment, allowing rerouting, improving fuel consumption, and saving time. The traffic flow structure pattern can be constructed from freeway toll data. Based on the pattern, a prediction method was proposed, which is based on locally weighted learning (LWL) and regression.

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