Application of support vector machine in a traffic lights control

This article presents the process of adapting support vector machine model’s parameters used for studying the effect of traffic light cycle length parameter’s value on traffic quality. The survey is carried out using data collected during running simulations in author’s traffic simulator. The article shows results of searching for optimum traffic light cycle length parameter’s value

A MOBILE-BASED RFID NETWORK TO TRACK THE THEFT VEHICLE

With emergency vehicle clearance, the traffic signal turns to eco-friendly as extended since the emergency vehicle delays inside the traffic junction. Presently, we have implemented system by considering one road inside the traffic junction. It might be improved by extending to everybody the roads inside the multi-road junction. Intelligent control of traffic flows helps to reduce the negative impact of congestion. Lately, wireless systems are broadly utilized all the time transport given that they provide less pricey options. AT instructions are broadly-accustomed to control modems. These instructions are a consequence of Hayes instructions that have been employed by the Hayes smart modems. Once the RFID-tag-read is most likely the stolen vehicle, an e-mail is shipped using GSM SIM300 for your police control room. Additionally, when an ambulance is approaching the junction, it'll communicate for your traffic controller inside the junction to show over the eco-friendly light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-nick for wireless communications concerning the ambulance and traffic controller. The system utilizes tags that adhere to various components to acquire tracked. The tags store information and understanding in regards to the info on the merchandise of products to acquire tracked. The prototype was tested under different mixtures of inputs inside our wireless communication laboratory and experimental effects come up with unsurprisingly

Control of a Mixed Autonomy Signalised Urban Intersection: An Action-Delayed Reinforcement Learning Approach

We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple traffic-light blocks. The objective of the traffic intersection controller is to minimize the queue length at each lane and maximize the outflow of vehicles over each block. We consider that the traffic intersection controller informs the autonomous vehicle (AV) whether the traffic light will be green or red for the future traffic-light block. Thus, the AV can adapt its dynamics by solving an optimal control problem. We model the decision process of the traffic intersection controller as a deterministic delay Markov decision process owing to the delayed action by the traffic controller. We propose Reinforcement-learning based algorithm to obtain the optimal policy. We show - empirically - that our algorithm converges and reduces the energy costs of AVs drastically as the traffic controller communicates with the AVs.Comment: Accepted for Publication at 24th IEEE International Conference on Intelligent Transportation (ITSC'2021