Adaptive and Optimized Emergency Vehicle Dispatching Algorithm for Intelligent Traffic Management System

被引:14
作者
Chakraborty, Partha Sarathi [1 ]
Tiwari, Arti [1 ]
Sinha, Pranshu Raj [1 ]
机构
[1] SRM Univ, Dept Comp Sci & Engn, Madras, Tamil Nadu, India
来源
3RD INTERNATIONAL CONFERENCE ON RECENT TRENDS IN COMPUTING 2015 (ICRTC-2015) | 2015年 / 57卷
关键词
Intelligent Transportation Systems; Emergency Vehicle; Traffic lights; Wireless Sensor Networks; Traffic Management; Smart Cities;
D O I
10.1016/j.procs.2015.07.454
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Rapid development of metropolitan cities into smart cities and daily increased in number of vehicles on the roads causes increase in traffic maintenance challenges such as traffic congestion, accidents, dispatching of emergency vehicle and pollution. It will suffers people travel time, delay in response time of emergency vehicle such as ambulance, fire brigade, police etc., causes heavy loss or effects society in terms of wealth, emotions and security. The research work done in the area of Intelligent Transportation Systems and Traffic Management primarily focuses on determining effectively green light duration along with dispatching of emergency vehicle. In this research paper, we propose an algorithm which not only determines green light duration dynamically but also handles the emergency vehicle management efficiently. The objectives of the proposed algorithm are minimizing average waiting time. It always analysis the traffic situation at traffic intersection, along with continuous monitoring of emergency vehicle arrival at different lanes causes to optimize the waiting time of vehicle in lanes. It also deals with the deadlock and starvation condition, due to arrival of emergency vehicle in repeated interval of time in traffic intersection. The security level of emergency vehicle is in consideration during dispatching that assigned and verified by authorities to preserve security measures and society values. Here, the source of input on dynamic traffic condition is collected through Wireless Sensor Networks technology. (C) 2015 The Authors. Published by Elsevier B.V.
引用
收藏
页码:1384 / 1393
页数:10
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