An efficient intelligent traffic light control and deviation system for traffic congestion avoidance using multi-agent system

An efficient and intelligent road traffic management system is the corner stone for every smart cities. Vehicular Ad-hoc NETworks (VANETs) applies the principles of mobile ad hoc networks in a wireless network for Vehicle-to-vehicle data exchange communication. VANETs supports in providing an efficient Intelligent Transportation System (ITS) for smart cities. Road traffic congestion is a most common problem faced by many of the metropolitan cities all over the world. Traffic on the road networks are widely increasing at a larger rate and the current traffic management systems is unable to tackle this impediment. In this paper, we propose an Efficient Intelligent Traffic Light Control and Deviation (EITLCD) system, which is based on multi-agent system. This proposed system overcomes the difficulties of the existing traffic management systems and avoids the traffic congestion problem compare to the prior scenario. The proposed system is composed of two systems: Traffic Light Controller (TLC) system and Traffic Light Deviation (TLD) system. The TLC system uses three agents to supervise and control the traffic parameters. TLD system deviate the vehicles before entering into congested road. Traffic and travel related information from several sensors are collected through a VANET environment to be processed by the proposed technique. The proposed structure comprises of TLC system and makes use of vehicle measurement, which is feed as input to the TLD system in a wireless network. For route pattern identification, any traditional city map can be converted to planar graph using Euler’s path approach. The proposed system is validated using Nagel–Schreckenberg model and the performance of the proposed system is proved to be better than the existing systems in terms of its time, cost, expense, maintenance and performance. First published online 26 September 201

Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning

Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces. Inspired by these successes, in this paper, we build two kinds of reinforcement learning algorithms: deep policy-gradient and value-function based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The policy-gradient based agent maps its observation directly to the control signal, however the value-function based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Our methods show promising results in a traffic network simulated in the SUMO traffic simulator, without suffering from instability issues during the training process

In the Truman show: generating dynamic scenarios in a driving simulator

All the devices, animals, and people make their decisions based on what you're doing, but you don't know it or even notice it. Your world is that of Truman Burbank, from the 1998 movie The Truman Show. With this idea in mind, we've taken the movie metaphor to implement a prototype simulation system where the user steps into Truman's shoes. The set of our "movie" is a driving simulator, and the user is learning to drive a car. During the driving lessons, users drive in a virtual world that lets them experience all kinds of traffic scenarios. The system generates the scenarios with the student as the focal point, and the other traffic entities respond to the student's behavior, without the student noticing. To control the traffic scenarios and make them more effective, our prototype employs an agent-based framework. In this framework, each entity in the simulator is an actor agent playing a role. The prototype also includes a hierarchy of directors that directs the main action and the behind-the-scenes activity. The advantage of the movie metaphor is that it helps separate scenario description from scenario playing. The agents can read their required information from a script and perform their actions based on that information. Using this framework lets us build software that's extensible, maintainable, and easy to understan