Ganeshkumar P, Gokulakrishnan P
Department of Information Technology, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu 624622, India.
Department of Computer Science and Engineering, PSNA College of Engineering and Technology, Dindigul, Tamil Nadu 624622, India.
ScientificWorldJournal. 2015;2015:218379. doi: 10.1155/2015/218379. Epub 2015 May 10.
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.
在印度的四车道高速公路上，每天都有数百万车辆行驶。事故是不幸的，并且在这些高速公路上频繁发生，导致人员死亡、死亡人数增加以及基础设施受损。需要一种机制来最大程度地避免此类道路事故，以减少死亡人数。本文提出了一种紧急情况预测机制，这是一种新颖且主动的方法，旨在利用车载自组织网络实现智能交通系统的最佳效果。ESPM旨在预测印度四车道高速公路上发生事故的可能性。在ESPM中，路边单元基于（i）在RSU范围内的车辆发送的状态报告以及（ii）RSU进行的道路交通流量分析来进行紧急情况预测。一旦提前预测到紧急情况或事故，就会构建一条紧急警告消息并传播给RSU区域内的所有车辆，以提醒并防止车辆发生事故。ESPM在事故发生前的紧急情况预测方面表现良好。ESPM能在0.20秒内预测到紧急情况，这比统计值要小。在不同的交通场景下，发现ESPM针对车辆密度的预测准确率更高。
