Description:Title:

DYNAMIC EMERGENCY RESPONSE SYSTEM: MACHINE LEARNING-ENABLED AMBULANCE ROUTING AND NAVIGATION

Field of the Invention

[0001] The present invention is related to the computer science domain and e-learning field.
[0002] The use of machine learning algorithms to develop a system which handle traffic in a smart way by automatically adjusting its timing based on traffic density. It is because in today’s world we have been facing problem due to increasing traffic on roads. This causes a lot of time wastage and increase in stress level. Many emergency vehicles are also stuck in traffic for hours leads to losing life of people.

Background

[0003] In metropolitan regions, traffic congestion is a major issue that increases travel times, fuel consumption, and pollution. These problems can be resolved with effective traffic control techniques. The fixed time intervals or basic vehicle detectors used in traditional traffic signal management systems can lead to poor traffic flow. There is no system present for change the traffic light for the ambulance which leads to loss of life.
[0004] There are several different types of traffic control systems using traffic lights. Some control the traffic over several intersections, whereas others only control it within one intersection. For the latter type, there are three main systems used today for controlling traffic lights: pre-timed systems, semi-actuated systems, and fully-actuated systems. Pretimed systems utilize fixed time intervals to control traffic. This means that one phase is active for a fixed amount of time before the control system switches to the next phase and keeps that active equally long. After each phase has been active, the process is repeated. Semi-actuated systems use sensors at the smaller of two crossing roads at an intersection. The larger road has a green light until these sensors indicate that vehicles from the smaller road wish to traverse the intersection. A phase allowing this is then activated until there are no more vehicles on the lesser road or when a maximum fixed time has been reached. After this, the larger road is given a green light again. Fully actuated systems are similar to semi-actuated systems. However, fully actuated systems use sensors for each road at an intersection. The phases are therefore activated in a way that responds to the current traffic situation.

Objects of the Invention

[0005] Following are the objectives of the present disclosure:
• To provide clear way to the ambulance.
• To save the time of people by detecting the density of automobiles on the lanes whenever it enter the range of the camera. Hence it will lead to avoiding waste of time and saving human life.
• Consider environmentally sustainable traffic management practices, such as encouraging the use of eco-friendly modes of transportation during emergency situations.
• Involve the public in reporting traffic incidents, congestion, or road hazards through a website to enhance data accuracy and response effectiveness.

Summary

[0006] By this project the problem of traffic can be easily sorted out: the timing of each signal can be automatically adjusted according to density of traffic which is real time operation. It will also clear the path for the ambulance, fire brigade in emergency cases and also it will help to public in taking decisions for reaching their destination in time using auto-routing method. It shows that it can reduce the traffic congestion and avoids the time being wasted by a green light on an empty road. It is also more consistent in detecting vehicle presence because it uses actual traffic images. It visualizes the reality so it functions much better than those systems that rely on the detection of the vehicles metal content. Overall, the system is good but it still needs improvement to achieve a hundred percent accuracy.
[0007] It is well recognized that vision-based camera system are more versatile for traffic parameter estimation. In addition to quantitative description of road congestion, image measurement can provide quantitative description of traffic flow.

Drawings

Figure 1: Algorithm Process Flowchart

Brief Description of the Drawing

[0008] The figure 1 represents Algorithm Process Flowchart of the working model in the present invention.
Detailed Description

[0009] The proposed system provides passage for ambulances using RCNN (Region-based Convolutional Neural Network), webcam, NodeMcu controller, LEDs. The core idea revolves around traffic management through the assessment of traffic volume on each side of the road, with the aim of implementing smart traffic signal control based on this density information. The webcam captures images of the vehicles on the road and send images to the Nodemcu microcontroller and then we apply RCNN algorithm and OpenCV to detect and count number of vehicles on the lane and set traffic timing accordingly. If ambulance is detected traffic light turns green on that side and other side are turn red to provide passage for emergency vehicles.
[0010] The Technology used in the invention is as follows:
• Hardware Components:
? Processor : Pentium IV
? RAM : 512MB 3.3
? NodeMcu Controller

• Software Components:
? Operating System : Windows 7 and Above
? Software Module : Open CV Image Processing
? Interfacing : The interfacing between the hardware
[0011] There are a number of potential end users and benefits of the traffic light management system for "Enhancing Urban Mobility" that uses image processing, including:
• Municipalities and Local Governments: In urban areas, transportation infrastructure management falls within the purview of municipal and local government agencies. They can put this system in place to enhance overall transportation efficiency, improve traffic flow, and lessen congestion.
• Transportation Departments: To optimize traffic signal timing at intersections under their jurisdiction, transportation departments at the local or regional level may implement this method. They may be able to shorten commuting times and enhance transportation systems thanks to it.
• Traffic Management Authorities: This technology can be useful for traffic management authorities, such as traffic control centers and organizations in charge of observing and managing traffic.
• Drivers and Commuters: Drivers and commuters experience reduced travel times, less frustration, and improved road safety due to well-managed traffic signals.
• Pedestrians and Cyclists: Well-coordinated traffic signals can enhance pedestrian and cyclist safety at intersections, encouraging sustainable modes of transportation.
• Emergency Services: Rapid response times for emergency services, such as ambulances and fire departments, are facilitated by traffic signal optimization, potentially saving lives.

Advantages of the Invention

[0012] The "Enhancing Urban Mobility" traffic light control system using image processing offers numerous advantages and benefits, both for the community and the broader transportation ecosystem. Here are some of the key advantages:
1. Improved Traffic Flow: The system optimizes traffic signal timings in real-time, reducing congestion and improving the overall flow of traffic at intersections. This leads to shorter travel times for commuters and less time spent idling in traffic.
2. Reduced Congestion: By dynamically adjusting traffic lights based on actual traffic conditions, the system helps prevent gridlock and reduces traffic congestion, which can lead to fewer accidents and safer roadways.
3. Enhanced Safety: The system can include features that prioritize pedestrian safety by ensuring adequate crossing time and by detecting and responding to potential hazards or accidents more quickly.
4. Lower Fuel Consumption: Reduced congestion and smoother traffic flow lead to decreased fuel consumption and lower greenhouse gas emissions. This benefits the environment and helps combat air pollution and climate change.
5. Real-time Adaptation: The system can respond to changing traffic patterns in real-time, making it adaptable to accidents, road closures, special events, and construction zones. This flexibility helps minimize disruptions.
6. Enhanced Quality of Life: Ultimately, the system improves the overall quality of life for urban residents by reducing commute times, improving safety, and making cities more livable.
7. Save Life: As it provide way to ambulance as soon as it captures the ambulance image
8. Cost-Effective: Compared to physical infrastructure improvements, vision-based camera systems can be a cost-effective solution for traffic management and monitoring.
9. Remote Monitoring: These systems can be remotely monitored and controlled, reducing the need for on-site personnel and enhancing operational efficiency.
10. Safety: Improved traffic management and incident detection enhance road safety for all road users.
These advantages make the "Enhancing Urban Mobility" traffic light control system using image processing a valuable tool for addressing traffic-related challenges in urban areas and creating more efficient, sustainable, and livable cities.
, Claims:Claims

Following are the claims of the invention:
1. To develop an image-processing-based adaptive traffic light control system.
2. The system of claim 1 wherein, a model for taking pictures that can record traffic at an intersection in real time.
3. The system of claim 1 wherein, a vehicle and pedestrian detection method for processing photos after they have been taken.
4. The system of claim 1 wherein, image analysis-based traffic lights control logic that modifies the timing of traffic light signals.
5. The system of claim 1 wherein, a setup of communication and command for coordinating with and managing traffic signals.
6. The system of claim 1 wherein, a smart traffic signal control system for ambulance.