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Impact Factor : 8.14

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Paper Title: AI DRIVEN SMART AMBULANCE SYSTEM FOR FASTER EMERGENCY RESPONSE
Authors Name: VIGNESH M , SURESH M , RISHIKESAVAN S , KARTHICKRAJ S S , KAVIBHARATHI N
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IJRTI_204149
Published Paper Id: IJRTI2505247
Published In: Volume 10 Issue 5, May-2025
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Abstract: With every passing day, our roads are becoming more crowded, and unfortunately, so are the reports of traffic accidents. As cities grow and more people depend on personal vehicles, the chances of accidents naturally increase. According to a global survey conducted by the World Health Organization (WHO), around 1.3 million people die each year, and 50 million more are injured in road accidents worldwide. These are not just numbers they represent real people, real families, and real heartbreak. What makes many of these deaths even more tragic is that they could have been prevented with faster medical help. In countless cases, people don’t die because of the crash itself but because emergency assistance arrives too late. Every minute counts after an accident. If help can get there just a little faster, a life can be saved, a future preserved. However, traffic congestion and routing issues often delay ambulances. In busy cities, even emergency vehicles can get stuck or take longer routes. That’s where this project comes in—with a mission to transform the way we respond to road emergencies. The idea is simple but powerful: use data to predict where accidents are most likely to happen, and place ambulances nearby so they can reach the scene as quickly as possible. To do this, the project uses a smart combination of machine learning models—specifically a technique called Variational Deep Embedding (VaDE) and Linear Regression. VaDE is a next-generation clustering method that uses deep learning and probability modeling to group accident locations in a much smarter way than traditional methods. This helps us understand not just where accident happen, but patterns that might not be obvious at first glance. Then, Linear Regression takes that information and helps predict the best possible spots to position ambulances, based on past data like how often accidents occur in an area, how far ambulances usually travel, and how quickly they respond. But it doesn’t stop there. The system also sends real-time alerts to hospitals and traffic departments when an accident occurs, so traffic can be cleared, hospital staff can prepare, and ambulances can move without delay. Imagine a world where an ambulance is already just minutes away from a crash before it even happens that’s the future this project envisions. By combining predictive analytics, real-time coordination, and proactive planning, this approach can dramatically reduce emergency response times, giving accident victims the best possible chance of survival. It's not just a technical solution. it’s a lifesaving mission built on the belief that no one should die waiting for help to arrive.
Keywords: Ambulance placement, Real time alert system,
Cite Article: "AI DRIVEN SMART AMBULANCE SYSTEM FOR FASTER EMERGENCY RESPONSE", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c399-c406, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505247.pdf
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ISSN: 2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publication Details: Published Paper ID: IJRTI2505247
Registration ID:204149
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c399-c406
Country: SALEM, Tamil Nadu, India
Research Area: Computer Engineering 
Publisher : IJ Publication
Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505247
Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505247
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED, Journal Starting Year (ESTD) : 2016

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