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An infrastructure based congestion detection and avoidance scheme for VANETs

机译：基于基础架构的VANET拥塞检测和避免方案

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摘要

In urban areas, traffic congestion is a challenging problem which creates many complications to travelers. Unattended congestion in roadways will cause long waiting times and affects individual economy. It also creates environmental issues and affects personal health. In recent years, the development of Vehicular Ad-hoc Network (VANET) provides a promising solutions for handling the congestion in the roadways. In this paper, we adapt a VANET networking model and propose a novel congestion detection and avoidance scheme for urban areas. A light weight histogram model is utilized to compute the congestion for every lane using an infrastructure based system. By computing the probability density function for every lane, the proposed model predicts congestion in advance and the re-routing strategy is initiated on time. For optimizing the re-routing strategy, two different kinds of congestion avoidance scheme are proposed based on static and dynamic modelling. Various scenarios are created in the microscopic simulation environment and the effectiveness of the proposed algorithm is analyzed by measuring the average travel time. The simulation results show that the proposed model detects congestion in priori and initiates reroute strategy effectively.
机译：在城市地区，交通拥堵是一个具有挑战性的问题，给旅行者带来许多麻烦。道路上无人看管的交通拥堵将导致漫长的等待时间，并影响个体经济。它还会造成环境问题并影响个人健康。近年来，车载自组织网络（VANET）的发展为处理道路拥堵提供了有希望的解决方案。在本文中，我们改编了VANET网络模型，并提出了一种新颖的城市拥塞检测和避免方案。轻量直方图模型用于使用基于基础架构的系统来计算每个车道的拥堵情况。通过计算每个车道的概率密度函数，所提出的模型可以提前预测拥堵，并按时启动重新路由策略。为了优化重路由策略，基于静态和动态建模，提出了两种不同的拥塞避免方案。在微观仿真环境中创建了各种场景，并通过测量平均行程时间来分析所提出算法的有效性。仿真结果表明，所提出的模型能够先验地检测出拥塞并有效地启动了重路由策略。

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