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An Intelligent Emergency Response Framework for Ambulance Priority Routing Using Real-Time GPS Tracking and Traffic Signal Preemption
Subject area: Science,Engineering and Technology · Area of research: Bangalore
Abstract
Ambulance delay costs lives. It’s sounds harsh, but its true. Studies show that even a small delay before a patient receives can reduce their chances of survival, sometimes by up to 6% for every minute lost. This is what made us think why, even in 2024, are ambulances still getting at red signals? That question led us to build SAPS in this paper, we explain the system we designed and tested. The idea is simple , first the ambulance is tracked in real time using GPS. Then, the system continuously finds the fastest route based on current traffic conditions. At the same time, it communicates with traffic signals ahead so that the ambulance can get a green light before reaching an intersection. We also included a shared application interface where the patient, hospital, and driver can all view the same informative live. This helps everyone stay coordinated during emergencies We are nor saying this is final product it is working prototype that shows the concept can actually work. Results from similar system are promising traffic signals preemption alone can reduce delays at intersection 15% to 50 %. Improvements like this can make a real difference when every second matters.
References
[1] Ghosh, Nandi, and Roy (2022) wrote about using PALL systems in emergency medical services. Main thing they focused on was real-time data sharing between people involved in the response. It is a pretty important issue honestly — during emergencies information does not always reach the right people in time and that causes problems. Their work looks at fixing that.
[2] Zhang, Zhao, and Chen (2023) brought 5G into it. They made a framework for allocating ambulance resources more efficiently using 5G. The concept makes sense. Though in areas where 5G is not even available yet it is hard to see how practical it gets.
[3] Wang, Liu, and Zhang (2023) made a triage model using machine learning to figure out how serious emergency cases are. The point is to help medical staff decide who needs attention first. Staff are usually overworked anyway so something that helps sort that out automatically is useful. Not perfect but useful.
[4] Ramya and others (2022) tracked ambulances in real time using GPS and Android. Simple system. Works. Easy to set up compared to a lot of other proposed solutions. That alone makes it stand out a bit.
[5] Ziliaskopoulos and Mahmassani (1996) is old but still relevant. They worked on route optimization and actually accounted for intersection delays and turn times when calculating travel time. That kind of detail made their results more accurate than a lot of other work from the same period. Still gets cited now.
[6] Li, Wang, and Zhang (2017) did something with traffic signals — made them respond to real traffic data instead of fixed timers. Helps with congestion. For ambulances this kind of thing actually matters because sitting at red lights costs time.
[7] Hulsebosch, van Eeten, and van den Berg (2007) were not really focused on tech. More about coordination between agencies during emergencies. Their whole thing was that smart systems only work if agencies actually work together. Which is obvious when you say it out loud but apparently needs saying.
[8] WHO (2019) put out a document on prehospital trauma care. Global standards basically. More of a reference thing than actual research but good for checking how a system compares to what is internationally expected.
[9] OSRM is just a routing tool. Open source. Used a lot for real-time navigation. Works well which is probably why it keeps getting used in different projects (Project OSRM, 2025).
How to cite this paper
@article{1715344,
author = {K Naga Sathwik, Pavithra P, Koushik V, Kareena S Dutta, Kameswar Reddy},
title = {An Intelligent Emergency Response Framework for Ambulance Priority Routing Using Real-Time GPS Tracking and Traffic Signal Preemption},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {3456-3462},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715344.pdf},
abstract = {Ambulance delay costs lives. It’s sounds harsh, but its true. Studies show that even a small delay before a patient receives can reduce their chances of survival, sometimes by up to 6% for every minute lost. This is what made us think why, even in 2024, are ambulances still getting at red signals? That question led us to build SAPS in this paper, we explain the system we designed and tested. The idea is simple , first the ambulance is tracked in real time using GPS. Then, the system continuously finds the fastest route based on current traffic conditions. At the same time, it communicates with traffic signals ahead so that the ambulance can get a green light before reaching an intersection. We also included a shared application interface where the patient, hospital, and driver can all view the same informative live. This helps everyone stay coordinated during emergencies We are nor saying this is final product it is working prototype that shows the concept can actually work. Results from similar system are promising traffic signals preemption alone can reduce delays at intersection 15% to 50 %. Improvements like this can make a real difference when every second matters.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715344}
}