School of Computer Science, University of Adelaide, Adelaide 5005, Australia.
Department of Information Systems and Business Analytics, RMIT University, Melbourne 3000, Australia.
Sensors (Basel). 2023 Jun 4;23(11):5324. doi: 10.3390/s23115324.
Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
应急管理系统（EMS）是智能交通系统的重要组成部分，其主要目标是派遣应急车辆（EV）到报告事件的地点。然而，城市地区交通量的增加，尤其是在高峰时段，导致 EV 在许多情况下延迟到达，最终导致更高的死亡率、更高的财产损失和更高的交通拥堵。现有文献通过在 EV 前往事故地点的途中改变交通信号（例如，使信号变绿）来为其提供更高的优先级来解决这个问题。一些工作还试图在旅程开始时利用交通信息（例如，车辆数量、流量和清除时间）为 EV 找到最佳路线。然而，这些工作没有考虑到与 EV 行驶路径相邻的非紧急车辆所面临的拥堵或中断。选择的行驶路径也是静态的，并且不考虑在 EV 行驶途中改变交通参数。为了解决这些问题，本文提出了一种基于无人机（UAV）引导的优先级事件管理系统，以帮助 EV 在交叉口获得更好的清除时间，从而实现更低的响应时间。所提出的模型还考虑了与 EV 行驶路径相邻的其他周边非紧急车辆所面临的中断，并通过控制交通信号相位时间来选择最佳解决方案，以确保 EV 能够按时到达事故地点，同时对其他道路上的车辆造成最小的干扰。仿真结果表明，所提出的模型使 EV 的响应时间降低了 8%，同时事故地点周围的清除时间提高了 12%。
