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Emergency Situation Prediction Mechanism: A Novel Approach for Intelligent Transportation System Using Vehicular Ad Hoc Networks

机译:紧急情况预测机制:基于车辆自组织网络的智能交通系统新方法

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摘要

In Indian four-lane express highway, millions of vehicles are travelling every day. Accidents are unfortunate and frequently occurring in these highways causing deaths, increase in death toll, and damage to infrastructure. A mechanism is required to avoid such road accidents at the maximum to reduce the death toll. An Emergency Situation Prediction Mechanism, a novel and proactive approach, is proposed in this paper for achieving the best of Intelligent Transportation System using Vehicular Ad Hoc Network. ESPM intends to predict the possibility of occurrence of an accident in an Indian four-lane express highway. In ESPM, the emergency situation prediction is done by the Road Side Unit based on (i) the Status Report sent by the vehicles in the range of RSU and (ii) the road traffic flow analysis done by the RSU. Once the emergency situation or accident is predicted in advance, an Emergency Warning Message is constructed and disseminated to all vehicles in the area of RSU to alert and prevent the vehicles from accidents. ESPM performs well in emergency situation prediction in advance to the occurrence of an accident. ESPM predicts the emergency situation within 0.20 seconds which is comparatively less than the statistical value. The prediction accuracy of ESPM against vehicle density is found better in different traffic scenarios.
机译:在印度的四车道高速公路上,每天有数百万辆汽车在行驶。不幸的是,在这些高速公路上经常发生事故,导致死亡,死亡人数增加和基础设施损坏。需要一种机制来最大程度地避免此类交通事故,以减少死亡人数。本文提出了一种应急主动的预测机制,一种新颖,主动的方法,以利用车载自组织网络实现最佳的智能交通系统。 ESPM旨在预测印度四车道高速公路发生事故的可能性。在ESPM中,路边单位基于(i)RSU范围内的车辆发送的状态报告和(ii)RSU进行的道路交通流量分析来完成紧急情况预测。一旦事先预测到紧急情况或事故,便会生成紧急警告消息并将其分发给RSU区域内的所有车辆,以警告并防止车辆发生事故。 ESPM在事故发生之前的紧急情况预测中表现良好。 ESPM可以在0.20秒内预测出紧急情况,这比统计值要少。在不同的交通场景中,ESPM对车辆密度的预测准确性更高。

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