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Evacuation Management Framework towards Smart City-wide Intelligent Emergency Interactive Response System

Anuj Abraham, Yi Zhang, Shitala Prasad

TL;DR

The paper tackles emergency evacuation in smart cities by proposing a Smart City-wide Intelligent Emergency Interactive Response System that leverages 6G-V2X, multi-sensor data fusion, and AI to enable real-time perception, classification, and optimized dispatch. It introduces a three-stage framework—detection, processing/storage, and application-layer operations—applied to indoor household, urban road, and large facility scenarios. Key contributions include sensor selection for diverse environments, CNN-based incident classification with real-time notifications, and optimization models (ACCP and DARP) for hospital assignment and ambulance routing, as well as GLOSA-based signal control and multi-modal evacuation planning. The framework aims to reduce emergency response times, ease congestion, and improve overall public safety and mobility, with future work focusing on privacy-preserving learning and city-wide disaster management using UAVs and satellites.

Abstract

A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform an existing emergency response system into an intelligent interactive system, thereby improving the public services and the quality of life for residents at home, on road, in hospitals, transport hubs, etc. In this context, we consider a city wide view from three different application scenes that are closely related to peoples daily life, to optimize the actions taken at relevant departments. Therefore, using artificial intelligence (AI) and machine learning (ML) techniques to enable the next generation connected vehicle experiences, we specifically focus on accidents happening in indoor households, urban roads, and at large public facilities. This smart interactive response system will benefit from advanced sensor fusion and AI by formulating a real time dynamic model.

Evacuation Management Framework towards Smart City-wide Intelligent Emergency Interactive Response System

TL;DR

The paper tackles emergency evacuation in smart cities by proposing a Smart City-wide Intelligent Emergency Interactive Response System that leverages 6G-V2X, multi-sensor data fusion, and AI to enable real-time perception, classification, and optimized dispatch. It introduces a three-stage framework—detection, processing/storage, and application-layer operations—applied to indoor household, urban road, and large facility scenarios. Key contributions include sensor selection for diverse environments, CNN-based incident classification with real-time notifications, and optimization models (ACCP and DARP) for hospital assignment and ambulance routing, as well as GLOSA-based signal control and multi-modal evacuation planning. The framework aims to reduce emergency response times, ease congestion, and improve overall public safety and mobility, with future work focusing on privacy-preserving learning and city-wide disaster management using UAVs and satellites.

Abstract

A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform an existing emergency response system into an intelligent interactive system, thereby improving the public services and the quality of life for residents at home, on road, in hospitals, transport hubs, etc. In this context, we consider a city wide view from three different application scenes that are closely related to peoples daily life, to optimize the actions taken at relevant departments. Therefore, using artificial intelligence (AI) and machine learning (ML) techniques to enable the next generation connected vehicle experiences, we specifically focus on accidents happening in indoor households, urban roads, and at large public facilities. This smart interactive response system will benefit from advanced sensor fusion and AI by formulating a real time dynamic model.
Paper Structure (18 sections, 6 figures)

This paper contains 18 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Scope of Work
  3. Detection stage for perception
  4. Processing and storage
  5. Application layer for department operations
  6. Industry relevance
  7. The Proposed Smart-Interactive Response System
  8. Detection stage
  9. Home-based Fall Accident Detection (enhance public health, vital sign triage)
  10. On-Road Traffic Accident Detection
  11. Large Public Facility Abnormal Detection (fire/ terrorist)
  12. Data fusion and ML algorithm development stage
  13. Optimized operation stage
  14. Household emergency service system
  15. Adaptive signal control and vehicle re-routing to alleviate congestions after traffic accidents
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Overall stages involved in the smart interactive system.
  • Figure 2: The message routing of proposed smart interactive system.
  • Figure 3: AI module to classify incidents/accidents and notifying the relevant departments for assistance.
  • Figure 4: Graphical representation of the community covering problem.
  • Figure 5: Graph representation of network in dynamic ambulance routing.
  • ...and 1 more figures