Abstract Reducing the travel time of an Emergency Vehicle (EV) is essential to increase the chance of casualty’s survival. An efficient vehicle routing solution can avoid instantaneous road blockages such as construction works, strikes, and accidents. This paper proposes an Internet of Things (IoT) network model for an EV routing using fuzzy logic-based data fusion technique. The data fusion process estimates the location-specific congestion by considering sensory data and human inputs from the crowd. Further, an open source routing engine (Open Source Routing Machine (OSRM)) computes the shortest congestion-aware route by responding to live traffic updates in a road network. Moreover, a sensor node has been designed for gathering the emissions and speeds of moving vehicles on the road. An Android application is developed to collect road blockage information from the crowd. Leveraging the Android application service, a driver in the EV is guided towards a medical center through the shortest congestion-aware route.

中文翻译：

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基于模糊逻辑的紧急车辆路由：面向智慧城市应用的物联网系统开发

摘要 减少紧急车辆（EV）的行驶时间对于增加伤员的生存机会至关重要。高效的车辆路线规划解决方案可以避免瞬间道路堵塞，例如建筑工程、罢工和事故。本文提出了一种使用基于模糊逻辑的数据融合技术的 EV 路由物联网 (IoT) 网络模型。数据融合过程通过考虑来自人群的感官数据和人类输入来估计特定位置的拥堵。此外，开源路由引擎（开源路由机（OSRM））通过响应道路网络中的实时交通更新来计算最短的拥塞感知路线。此外，还设计了一个传感器节点来收集道路上行驶车辆的排放量和速度。开发了一个 Android 应用程序来从人群中收集道路堵塞信息。利用 Android 应用服务，EV 中的驾驶员通过最短的拥堵感知路线被引导至医疗中心。