Real Time Traffic Control for Emergency Service Vehicles

Vishal, Dasari; Reddy, Rishika J; M, Bala Abhirami; Ramesh, T. K.

doi:10.1109/iccic.2017.8524354

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But the journey time has increased over the years due to increased traffic density. According to emergency medics a delay of a quarter hour may not seem much for commuting in the city, but becomes a matter of survival when a patient is fighting for life [6].…”

Section: Literature Surveymentioning

confidence: 99%

Density based Traffic Control System Embedded with Patient Weightage Method

2019

IJITEE

View full text Add to dashboard Cite

Every now and then traffic congestion has always been a hindrance in everyone’s normal life routine. This traffic congestion hindrance is very much problematic in case of high priority emergency vehicles namely ambulances, VIP vehicles, fire engines etc. So, traffic control has to be made proficient to provide smooth flow of vehicles. However, efficient synchronization of traffic at multiple junctions is complex. Conventional control systems do not handle the dynamic flow of traffic which results in chaos. In this project, implementation of an emergency vehicle detection system is done. The main objective is to provide a traffic-free route to the vehicles in order to save life. In this paper two cases are considered, in the initial case, the lane density at the junction will be calculated using ultrasonic sensors by Raspberry pi (Rpi) and the lane with minimal density route will be directed to the emergency vehicle NodeMcu in this case. Considering the assumption that hospital is present near to every lane. And in the second case, the conflict arises when two emergency vehicles are encountered at the same time at a junction, so this conflict is resolved in thispaper.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Density based Traffic Control System Embedded with Patient Weightage Method

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Among the numerous challenges these vehicles face, one of the most critical is navigating through traffic swiftly. Studies by Chowdhury et al [1], Xie et al [2], and Vishal et al [3] have identified key factors contributing to response delays, including traffic congestion, geographical barriers, and operational inefficiencies. Specifically, ambulances encounter unique challenges in their mission to provide immediate medical care.…”

Section: Introductionmentioning

confidence: 99%

RTAIAED: A Real-Time Ambulance in an Emergency Detector with a Pyramidal Part-Based Model Composed of MFCCs and YOLOv8

Mecocci,

Grassi

2024

Sensors

View full text Add to dashboard Cite

In emergency situations, every second counts for an ambulance navigating through traffic. Efficient use of traffic light systems can play a crucial role in minimizing response time. This paper introduces a novel automated Real-Time Ambulance in an Emergency Detector (RTAIAED). The proposed system uses special Lookout Stations (LSs) suitably positioned at a certain distance from each involved traffic light (TL), to obtain timely and safe transitions to green lights as the Ambulance in an Emergency (AIAE) approaches. The foundation of the proposed system is built on the simultaneous processing of video and audio data. The video analysis is inspired by the Part-Based Model theory integrating tailored video detectors that leverage a custom YOLOv8 model for enhanced precision. Concurrently the audio analysis component employs a neural network designed to analyze Mel Frequency Cepstral Coefficients (MFCCs) providing an accurate classification of auditory information. This dual-faceted approach facilitates a cohesive and synergistic analysis of sensory inputs. It incorporates a logic-based component to integrate and interpret the detections from each sensory channel, thereby ensuring the precise identification of an AIAE as it approaches a traffic light. Extensive experiments confirm the robustness of the approach and its reliable application in real-world scenarios thanks to its predictions in real time (reaching an fps of 11.8 on a Jetson Nano and a response time up to 0.25 s), showcasing the ability to detect AIAEs even in challenging conditions, such as noisy environments, nighttime, or adverse weather conditions, provided a suitable-quality camera is appropriately positioned. The RTAIAED is particularly effective on one-way roads, addressing the challenge of regulating the sequence of traffic light signals so as to ensure a green signal to the AIAE when arriving in front of the TL, despite the presence of the “double red” periods in which the one-way traffic is cleared of vehicles coming from one direction before allowing those coming from the other side. Also, it is suitable for managing temporary situations, like in the case of roadworks.

show abstract