This project aims to simulate emergency evacuation processes in disaster scenarios, utilizing Markov Decision Processes (MDPs) to optimize ambulance routing. By modeling real-time updates like road congestion, it seeks to minimize evacuation time, ensuring timely medical treatment for the injured.

A cloud-based, AI-powered solution for real-time traffic monitoring and management. This system analyzes CCTV and GPS data to predict traffic density, prioritize emergency vehicles, and optimize traffic signal timing. Features include dynamic route suggestions, accident detection alerts, and a centralized dashboard for city-wide traffic control.

🚀 LogiFlow AI: Intelligent logistics powered by TiDB Serverless vector search & multi-step AI agents. Predictive maintenance, route optimization & emergency response - all automated! 🤖📦

HERO (Hybrid Emergency Route Optimizer) is an end-to-end, real-time intelligence emergency services routing solution built on Microsoft Fabric, that decides the fastest path for an ambulance applying a “siren advantage” model. Winner of the Microsoft Fabric Global Hackathon 2025.

Developed a GIS-based evacuation route optimization tool using Python libraries (GeoPandas, NetworkX, Folium). Implemented machine learning and geospatial analysis for dynamic route planning, enhanced with real-time data visualization and traffic flow simulations for emergency scenarios.

A powerful map-based route optimization tool with real-time routing, multi-mode navigation, emergency POI finder, route animation, journey saving, and satellite map support — built using Leaflet, OpenRouteService, OSRM, and Overpass API.

The Smart Traffic Management System Simulator is a practical project designed to replicate real-world traffic management in a city. It optimizes urban traffic flow using data structures like graphs, heaps, and priority queues. The system focuses on real-time vehicle routing, traffic signal control, congestion management, and emergency handling.

Real-time emergency logistics routing optimization using Grey Wolf Optimization algorithm with live weather integration

Built a Python tool to plan faster disaster relief delivery routes using Linear Programming and Ant Colony Optimization. Aimed at making emergency logistics smarter and more efficient.

This simulation models a city’s traffic system, with different agents controlling various elements such as traffic signals, emergency vehicles, public transportation, and pedestrian crossings. The goal is to optimize traffic flow, reduce congestion, prioritize emergency services, and ensure efficient public transport routing.

Adaptive, cluster-based multi-charger scheduling algorithm for Wireless Rechargeable Sensor Networks (WRSNs). Optimizes charger routes, prioritizes hotspot nodes, and minimizes sensor downtime using energy- and demand-aware clustering with TSP-based route planning and real-time emergency handling.

The EMS Route Planner is a smart routing tool that helps emergency medical services find the quickest paths to hospitals by factoring in real-time traffic, patient priority, and hospital capacity. Built with A* search and Constraint Satisfaction Problem (CSP) techniques, it optimizes routing to reduce EMS response times in critical situations.

LifeLine is a comprehensive web application built using Streamlit, designed to streamline emergency medical services and assist in disease prediction. The system integrates features for real-time ambulance allocation, optimized hospital routing, and symptom-based AI disease prediction, ensuring faster and more efficient healthcare responses.

LifeLine is a comprehensive web application built using Streamlit, designed to streamline emergency medical services and assist in disease prediction. The system integrates features for real-time ambulance allocation, optimized hospital routing, and symptom-based AI disease prediction, ensuring faster and more efficient healthcare responses.

This project enhances EMS response times by developing a navigation system using real-time position data and actual speeds. Using Brussels' EMS data, it calculates average vehicle speeds and integrates them into a navigation system with OSRM and Python. Results show traffic congestion impacts EMS speeds, leading to more accurate ETA predictions.

SUMO

Uses the closest facility route concept to optimize emergency care.

Optimized Routing Approach for Critical and Emergency Networks [C-code]

A comprehensive system for optimizing transportation networks in Cairo, Egypt. This project provides tools for route planning, traffic optimization, emergency routing, and public transit optimization.

SERO (Spatial Emergency Response Optimization) is an R package for emergency service location optimization and accident risk analysis. This vignette demonstrates the core functionality including hotspot analysis, kernel density analysis, optimal location finding, and route optimization.

Smart AI safety app with risk-aware route optimization and multi-sensor emergency detection for real-time protection.

A comprehensive ride-hailing application optimized for emergency evacuations during natural disasters, featuring real-time hazard mapping, safe route optimization, and SOS functionality.

Real-time urban traffic optimization through camera-based vehicle detection, multi-agent reinforcement learning, and Unity simulation. Supports emergency vehicle priority routing.

**SafeRoute AI** is a smart emergency route optimization platform that helps ambulances, police, and fire services reach destinations faster during emergencies. The system uses real-time traffic data, AI-based congestion prediction, and intelligent routing to find the fastest and safest routes. It also provides live vehicle tracking, accident detec

A modular, AI-driven traffic optimization framework using SUMO, Graph Neural Networks (GNN), and Model Predictive Control (MPC) to reduce urban congestion and prioritize emergency vehicle routing.

AI-powered emergency dispatch copilot that automates clinical triage, transcribes live calls, and optimizes ambulance routing using real-time hospital data.

🚨 Optimize disaster response with this C++ system that prioritizes emergencies, calculates routes, and stores data in JSON format for efficient management.

A metaheuristic approach to optimize emergency vehicle dispatch and routing using multiple Genetic Algorithm variants — Standard, Elitist, Adaptive, and Hybrid GA — with comparative performance analysis.

Disaster Logistics Optimization is a system designed to streamline disaster response by providing fast, efficient, and confident route planning. With a dynamic map, subtle overlays, and intuitive visuals, it helps teams make critical decisions quickly and optimize emergency operations.

Developed an intelligent system to optimize emergency response routes by analyzing real-time crowdsourced video data using computer vision and geospatial pathfinding, reducing rescue time in disaster scenarios.